feat: CV Application - AI-powered CV management, parsing, matching, and generation

venv/lib/python3.12/site-packages/openai/__init__.py

@@ -0,0 +1,470 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+import os as _os
+import typing as _t
+from typing_extensions import override
+
+from . import types
+from ._types import NOT_GIVEN, Omit, NoneType, NotGiven, Transport, ProxiesTypes, omit, not_given
+from ._utils import file_from_path
+from ._client import Client, OpenAI, Stream, Timeout, Transport, AsyncClient, AsyncOpenAI, AsyncStream, RequestOptions
+from ._models import BaseModel
+from ._version import __title__, __version__
+from ._response import APIResponse as APIResponse, AsyncAPIResponse as AsyncAPIResponse
+from ._constants import DEFAULT_TIMEOUT, DEFAULT_MAX_RETRIES, DEFAULT_CONNECTION_LIMITS
+from ._exceptions import (
+    APIError,
+    OAuthError,
+    OpenAIError,
+    ConflictError,
+    NotFoundError,
+    APIStatusError,
+    RateLimitError,
+    APITimeoutError,
+    BadRequestError,
+    APIConnectionError,
+    AuthenticationError,
+    InternalServerError,
+    PermissionDeniedError,
+    LengthFinishReasonError,
+    WebSocketQueueFullError,
+    UnprocessableEntityError,
+    APIResponseValidationError,
+    InvalidWebhookSignatureError,
+    ContentFilterFinishReasonError,
+    WebSocketConnectionClosedError,
+)
+from ._base_client import DefaultHttpxClient, DefaultAioHttpClient, DefaultAsyncHttpxClient
+from ._utils._logs import setup_logging as _setup_logging
+from ._legacy_response import HttpxBinaryResponseContent as HttpxBinaryResponseContent
+from .types.websocket_reconnection import ReconnectingEvent, ReconnectingOverrides
+
+__all__ = [
+    "types",
+    "__version__",
+    "__title__",
+    "NoneType",
+    "Transport",
+    "ProxiesTypes",
+    "NotGiven",
+    "NOT_GIVEN",
+    "not_given",
+    "Omit",
+    "omit",
+    "OpenAIError",
+    "APIError",
+    "APIStatusError",
+    "APITimeoutError",
+    "APIConnectionError",
+    "APIResponseValidationError",
+    "BadRequestError",
+    "AuthenticationError",
+    "OAuthError",
+    "PermissionDeniedError",
+    "NotFoundError",
+    "ConflictError",
+    "UnprocessableEntityError",
+    "RateLimitError",
+    "InternalServerError",
+    "LengthFinishReasonError",
+    "ContentFilterFinishReasonError",
+    "InvalidWebhookSignatureError",
+    "Timeout",
+    "RequestOptions",
+    "Client",
+    "AsyncClient",
+    "Stream",
+    "AsyncStream",
+    "OpenAI",
+    "AsyncOpenAI",
+    "BedrockOpenAI",
+    "AsyncBedrockOpenAI",
+    "file_from_path",
+    "BaseModel",
+    "DEFAULT_TIMEOUT",
+    "DEFAULT_MAX_RETRIES",
+    "DEFAULT_CONNECTION_LIMITS",
+    "DefaultHttpxClient",
+    "DefaultAsyncHttpxClient",
+    "DefaultAioHttpClient",
+    "ReconnectingEvent",
+    "ReconnectingOverrides",
+    "WebSocketQueueFullError",
+    "WebSocketConnectionClosedError",
+]
+
+if not _t.TYPE_CHECKING:
+    from ._utils._resources_proxy import resources as resources
+
+from .lib import azure as _azure, bedrock as _bedrock, pydantic_function_tool as pydantic_function_tool
+from .version import VERSION as VERSION
+from .lib.azure import AzureOpenAI as AzureOpenAI, AsyncAzureOpenAI as AsyncAzureOpenAI
+from .lib.bedrock import BedrockOpenAI as BedrockOpenAI, AsyncBedrockOpenAI as AsyncBedrockOpenAI
+from .lib._old_api import *
+from .lib.streaming import (
+    AssistantEventHandler as AssistantEventHandler,
+    AsyncAssistantEventHandler as AsyncAssistantEventHandler,
+)
+
+_setup_logging()
+
+# Update the __module__ attribute for exported symbols so that
+# error messages point to this module instead of the module
+# it was originally defined in, e.g.
+# openai._exceptions.NotFoundError -> openai.NotFoundError
+__locals = locals()
+for __name in __all__:
+    if not __name.startswith("__"):
+        try:
+            __locals[__name].__module__ = "openai"
+        except (TypeError, AttributeError):
+            # Some of our exported symbols are builtins which we can't set attributes for.
+            pass
+
+# ------ Module level client ------
+import typing as _t
+import typing_extensions as _te
+
+import httpx as _httpx
+
+from ._base_client import DEFAULT_TIMEOUT, DEFAULT_MAX_RETRIES
+
+api_key: str | None = None
+
+admin_api_key: str | None = None
+
+organization: str | None = None
+
+project: str | None = None
+
+webhook_secret: str | None = None
+
+base_url: str | _httpx.URL | None = None
+
+timeout: float | Timeout | None = DEFAULT_TIMEOUT
+
+max_retries: int = DEFAULT_MAX_RETRIES
+
+default_headers: _t.Mapping[str, str] | None = None
+
+default_query: _t.Mapping[str, object] | None = None
+
+http_client: _httpx.Client | None = None
+
+_ApiType = _te.Literal["openai", "azure", "amazon-bedrock"]
+
+api_type: _ApiType | None = _t.cast(_ApiType, _os.environ.get("OPENAI_API_TYPE"))
+
+api_version: str | None = _os.environ.get("OPENAI_API_VERSION")
+
+azure_endpoint: str | None = _os.environ.get("AZURE_OPENAI_ENDPOINT")
+
+azure_ad_token: str | None = _os.environ.get("AZURE_OPENAI_AD_TOKEN")
+
+azure_ad_token_provider: _azure.AzureADTokenProvider | None = None
+
+_bedrock_api_key: str | None = None
+
+bedrock_token_provider: _bedrock.BedrockTokenProvider | None = None
+
+
+class _ModuleClient(OpenAI):
+    # Note: we have to use type: ignores here as overriding class members
+    # with properties is technically unsafe but it is fine for our use case
+
+    @property  # type: ignore
+    @override
+    def api_key(self) -> str | None:
+        return api_key
+
+    @api_key.setter  # type: ignore
+    def api_key(self, value: str | None) -> None:  # type: ignore
+        global api_key
+
+        api_key = value
+
+    @property  # type: ignore
+    @override
+    def admin_api_key(self) -> str | None:
+        return admin_api_key
+
+    @admin_api_key.setter  # type: ignore
+    def admin_api_key(self, value: str | None) -> None:  # type: ignore
+        global admin_api_key
+
+        admin_api_key = value
+
+    @property  # type: ignore
+    @override
+    def organization(self) -> str | None:
+        return organization
+
+    @organization.setter  # type: ignore
+    def organization(self, value: str | None) -> None:  # type: ignore
+        global organization
+
+        organization = value
+
+    @property  # type: ignore
+    @override
+    def project(self) -> str | None:
+        return project
+
+    @project.setter  # type: ignore
+    def project(self, value: str | None) -> None:  # type: ignore
+        global project
+
+        project = value
+
+    @property  # type: ignore
+    @override
+    def webhook_secret(self) -> str | None:
+        return webhook_secret
+
+    @webhook_secret.setter  # type: ignore
+    def webhook_secret(self, value: str | None) -> None:  # type: ignore
+        global webhook_secret
+
+        webhook_secret = value
+
+    @property
+    @override
+    def base_url(self) -> _httpx.URL:
+        if base_url is not None:
+            return _httpx.URL(base_url)
+
+        return super().base_url
+
+    @base_url.setter
+    def base_url(self, url: _httpx.URL | str) -> None:
+        super().base_url = url  # type: ignore[misc]
+
+    @property  # type: ignore
+    @override
+    def timeout(self) -> float | Timeout | None:
+        return timeout
+
+    @timeout.setter  # type: ignore
+    def timeout(self, value: float | Timeout | None) -> None:  # type: ignore
+        global timeout
+
+        timeout = value
+
+    @property  # type: ignore
+    @override
+    def max_retries(self) -> int:
+        return max_retries
+
+    @max_retries.setter  # type: ignore
+    def max_retries(self, value: int) -> None:  # type: ignore
+        global max_retries
+
+        max_retries = value
+
+    @property  # type: ignore
+    @override
+    def _custom_headers(self) -> _t.Mapping[str, str] | None:
+        return default_headers
+
+    @_custom_headers.setter  # type: ignore
+    def _custom_headers(self, value: _t.Mapping[str, str] | None) -> None:  # type: ignore
+        global default_headers
+
+        default_headers = value
+
+    @property  # type: ignore
+    @override
+    def _custom_query(self) -> _t.Mapping[str, object] | None:
+        return default_query
+
+    @_custom_query.setter  # type: ignore
+    def _custom_query(self, value: _t.Mapping[str, object] | None) -> None:  # type: ignore
+        global default_query
+
+        default_query = value
+
+    @property  # type: ignore
+    @override
+    def _client(self) -> _httpx.Client:
+        return http_client or super()._client
+
+    @_client.setter  # type: ignore
+    def _client(self, value: _httpx.Client) -> None:  # type: ignore
+        global http_client
+
+        http_client = value
+
+
+class _AzureModuleClient(_ModuleClient, AzureOpenAI):  # type: ignore
+    ...
+
+
+class _BedrockModuleClient(_ModuleClient, BedrockOpenAI):  # type: ignore
+    @property  # type: ignore
+    @override
+    def api_key(self) -> str | None:
+        return api_key if api_key is not None else _bedrock_api_key
+
+    @api_key.setter  # type: ignore
+    def api_key(self, value: str | None) -> None:  # type: ignore
+        global _bedrock_api_key
+
+        _bedrock_api_key = value
+
+    @override
+    def _refresh_api_key(self) -> str:
+        if api_key is not None:
+            return api_key
+
+        return super()._refresh_api_key()
+
+    @override
+    def _legacy_auth_configuration(self) -> _bedrock._LegacyAuthConfiguration:
+        if api_key is not None:
+            return ("bearer", api_key)
+        return super()._legacy_auth_configuration()
+
+
+class _AmbiguousModuleClientUsageError(OpenAIError):
+    def __init__(self) -> None:
+        super().__init__(
+            "Ambiguous use of module client; please set `openai.api_type` or the `OPENAI_API_TYPE` environment variable to `openai`, `azure`, or `amazon-bedrock`"
+        )
+
+
+def _has_openai_credentials() -> bool:
+    return _os.environ.get("OPENAI_API_KEY") is not None
+
+
+def _has_azure_credentials() -> bool:
+    return azure_endpoint is not None or _os.environ.get("AZURE_OPENAI_API_KEY") is not None
+
+
+def _has_azure_ad_credentials() -> bool:
+    return (
+        _os.environ.get("AZURE_OPENAI_AD_TOKEN") is not None
+        or azure_ad_token is not None
+        or azure_ad_token_provider is not None
+    )
+
+
+_client: OpenAI | None = None
+
+
+def _load_client() -> OpenAI:  # type: ignore[reportUnusedFunction]
+    global _client
+
+    if _client is None:
+        global api_type, azure_endpoint, azure_ad_token, api_version
+
+        if azure_endpoint is None:
+            azure_endpoint = _os.environ.get("AZURE_OPENAI_ENDPOINT")
+
+        if azure_ad_token is None:
+            azure_ad_token = _os.environ.get("AZURE_OPENAI_AD_TOKEN")
+
+        if api_version is None:
+            api_version = _os.environ.get("OPENAI_API_VERSION")
+
+        if api_type is None:
+            has_openai = _has_openai_credentials()
+            has_azure = _has_azure_credentials()
+            has_azure_ad = _has_azure_ad_credentials()
+
+            if has_openai and (has_azure or has_azure_ad):
+                raise _AmbiguousModuleClientUsageError()
+
+            if (azure_ad_token is not None or azure_ad_token_provider is not None) and _os.environ.get(
+                "AZURE_OPENAI_API_KEY"
+            ) is not None:
+                raise _AmbiguousModuleClientUsageError()
+
+            if has_azure or has_azure_ad:
+                api_type = "azure"
+            else:
+                api_type = "openai"
+
+        if api_type == "azure":
+            _client = _AzureModuleClient(  # type: ignore
+                api_version=api_version,
+                azure_endpoint=azure_endpoint,
+                api_key=api_key,
+                azure_ad_token=azure_ad_token,
+                azure_ad_token_provider=azure_ad_token_provider,
+                organization=organization,
+                base_url=base_url,
+                timeout=timeout,
+                max_retries=max_retries,
+                default_headers=default_headers,
+                default_query=default_query,
+                http_client=http_client,
+            )
+            return _client
+
+        if api_type == "amazon-bedrock":
+            _client = _BedrockModuleClient(  # type: ignore
+                api_key=api_key,
+                bedrock_token_provider=bedrock_token_provider,
+                organization=organization,
+                project=project,
+                webhook_secret=webhook_secret,
+                base_url=base_url,
+                timeout=timeout,
+                max_retries=max_retries,
+                default_headers=default_headers,
+                default_query=default_query,
+                http_client=http_client,
+            )
+            return _client
+
+        _client = _ModuleClient(
+            api_key=api_key,
+            admin_api_key=admin_api_key,
+            organization=organization,
+            project=project,
+            webhook_secret=webhook_secret,
+            base_url=base_url,
+            timeout=timeout,
+            max_retries=max_retries,
+            default_headers=default_headers,
+            default_query=default_query,
+            http_client=http_client,
+            _enforce_credentials=False,
+        )
+        return _client
+
+    return _client
+
+
+def _reset_client() -> None:  # type: ignore[reportUnusedFunction]
+    global _client
+
+    _client = None
+
+
+from ._module_client import (
+    beta as beta,
+    chat as chat,
+    admin as admin,
+    audio as audio,
+    evals as evals,
+    files as files,
+    images as images,
+    models as models,
+    skills as skills,
+    videos as videos,
+    batches as batches,
+    uploads as uploads,
+    realtime as realtime,
+    webhooks as webhooks,
+    responses as responses,
+    containers as containers,
+    embeddings as embeddings,
+    completions as completions,
+    fine_tuning as fine_tuning,
+    moderations as moderations,
+    conversations as conversations,
+    vector_stores as vector_stores,
+)

venv/lib/python3.12/site-packages/openai/_compat.py

@@ -0,0 +1,238 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Union, Generic, TypeVar, Callable, cast, overload
+from datetime import date, datetime
+from typing_extensions import Self, Literal, TypedDict
+
+import pydantic
+from pydantic.fields import FieldInfo
+
+from ._types import IncEx, StrBytesIntFloat
+
+_T = TypeVar("_T")
+_ModelT = TypeVar("_ModelT", bound=pydantic.BaseModel)
+
+# --------------- Pydantic v2, v3 compatibility ---------------
+
+# Pyright incorrectly reports some of our functions as overriding a method when they don't
+# pyright: reportIncompatibleMethodOverride=false
+
+PYDANTIC_V1 = pydantic.VERSION.startswith("1.")
+
+if TYPE_CHECKING:
+
+    def parse_date(value: date | StrBytesIntFloat) -> date:  # noqa: ARG001
+        ...
+
+    def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime:  # noqa: ARG001
+        ...
+
+    def get_args(t: type[Any]) -> tuple[Any, ...]:  # noqa: ARG001
+        ...
+
+    def is_union(tp: type[Any] | None) -> bool:  # noqa: ARG001
+        ...
+
+    def get_origin(t: type[Any]) -> type[Any] | None:  # noqa: ARG001
+        ...
+
+    def is_literal_type(type_: type[Any]) -> bool:  # noqa: ARG001
+        ...
+
+    def is_typeddict(type_: type[Any]) -> bool:  # noqa: ARG001
+        ...
+
+else:
+    # v1 re-exports
+    if PYDANTIC_V1:
+        from pydantic.typing import (
+            get_args as get_args,
+            is_union as is_union,
+            get_origin as get_origin,
+            is_typeddict as is_typeddict,
+            is_literal_type as is_literal_type,
+        )
+        from pydantic.datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime
+    else:
+        from ._utils import (
+            get_args as get_args,
+            is_union as is_union,
+            get_origin as get_origin,
+            parse_date as parse_date,
+            is_typeddict as is_typeddict,
+            parse_datetime as parse_datetime,
+            is_literal_type as is_literal_type,
+        )
+
+
+# refactored config
+if TYPE_CHECKING:
+    from pydantic import ConfigDict as ConfigDict
+else:
+    if PYDANTIC_V1:
+        # TODO: provide an error message here?
+        ConfigDict = None
+    else:
+        from pydantic import ConfigDict as ConfigDict
+
+
+# renamed methods / properties
+def parse_obj(model: type[_ModelT], value: object) -> _ModelT:
+    if PYDANTIC_V1:
+        return cast(_ModelT, model.parse_obj(value))  # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
+    else:
+        return model.model_validate(value)
+
+
+def field_is_required(field: FieldInfo) -> bool:
+    if PYDANTIC_V1:
+        return field.required  # type: ignore
+    return field.is_required()
+
+
+def field_get_default(field: FieldInfo) -> Any:
+    value = field.get_default()
+    if PYDANTIC_V1:
+        return value
+    from pydantic_core import PydanticUndefined
+
+    if value == PydanticUndefined:
+        return None
+    return value
+
+
+def field_outer_type(field: FieldInfo) -> Any:
+    if PYDANTIC_V1:
+        return field.outer_type_  # type: ignore
+    return field.annotation
+
+
+def get_model_config(model: type[pydantic.BaseModel]) -> Any:
+    if PYDANTIC_V1:
+        return model.__config__  # type: ignore
+    return model.model_config
+
+
+def get_model_fields(model: type[pydantic.BaseModel]) -> dict[str, FieldInfo]:
+    if PYDANTIC_V1:
+        return model.__fields__  # type: ignore
+    return model.model_fields
+
+
+def model_copy(model: _ModelT, *, deep: bool = False) -> _ModelT:
+    if PYDANTIC_V1:
+        return model.copy(deep=deep)  # type: ignore
+    return model.model_copy(deep=deep)
+
+
+def model_json(model: pydantic.BaseModel, *, indent: int | None = None) -> str:
+    if PYDANTIC_V1:
+        return model.json(indent=indent)  # type: ignore
+    return model.model_dump_json(indent=indent)
+
+
+class _ModelDumpKwargs(TypedDict, total=False):
+    by_alias: bool
+
+
+def model_dump(
+    model: pydantic.BaseModel,
+    *,
+    exclude: IncEx | None = None,
+    exclude_unset: bool = False,
+    exclude_defaults: bool = False,
+    warnings: bool = True,
+    mode: Literal["json", "python"] = "python",
+    by_alias: bool | None = None,
+) -> dict[str, Any]:
+    if (not PYDANTIC_V1) or hasattr(model, "model_dump"):
+        kwargs: _ModelDumpKwargs = {}
+        if by_alias is not None:
+            kwargs["by_alias"] = by_alias
+        return model.model_dump(
+            mode=mode,
+            exclude=exclude,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            # warnings are not supported in Pydantic v1
+            warnings=True if PYDANTIC_V1 else warnings,
+            **kwargs,
+        )
+    return cast(
+        "dict[str, Any]",
+        model.dict(  # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
+            exclude=exclude, exclude_unset=exclude_unset, exclude_defaults=exclude_defaults, by_alias=bool(by_alias)
+        ),
+    )
+
+
+def model_parse(model: type[_ModelT], data: Any) -> _ModelT:
+    if PYDANTIC_V1:
+        return model.parse_obj(data)  # pyright: ignore[reportDeprecated]
+    return model.model_validate(data)
+
+
+def model_parse_json(model: type[_ModelT], data: str | bytes) -> _ModelT:
+    if PYDANTIC_V1:
+        return model.parse_raw(data)  # pyright: ignore[reportDeprecated]
+    return model.model_validate_json(data)
+
+
+def model_json_schema(model: type[_ModelT]) -> dict[str, Any]:
+    if PYDANTIC_V1:
+        return model.schema()  # pyright: ignore[reportDeprecated]
+    return model.model_json_schema()
+
+
+# generic models
+if TYPE_CHECKING:
+
+    class GenericModel(pydantic.BaseModel): ...
+
+else:
+    if PYDANTIC_V1:
+        import pydantic.generics
+
+        class GenericModel(pydantic.generics.GenericModel, pydantic.BaseModel): ...
+    else:
+        # there no longer needs to be a distinction in v2 but
+        # we still have to create our own subclass to avoid
+        # inconsistent MRO ordering errors
+        class GenericModel(pydantic.BaseModel): ...
+
+
+# cached properties
+if TYPE_CHECKING:
+    cached_property = property
+
+    # we define a separate type (copied from typeshed)
+    # that represents that `cached_property` is `set`able
+    # at runtime, which differs from `@property`.
+    #
+    # this is a separate type as editors likely special case
+    # `@property` and we don't want to cause issues just to have
+    # more helpful internal types.
+
+    class typed_cached_property(Generic[_T]):
+        func: Callable[[Any], _T]
+        attrname: str | None
+
+        def __init__(self, func: Callable[[Any], _T]) -> None: ...
+
+        @overload
+        def __get__(self, instance: None, owner: type[Any] | None = None) -> Self: ...
+
+        @overload
+        def __get__(self, instance: object, owner: type[Any] | None = None) -> _T: ...
+
+        def __get__(self, instance: object, owner: type[Any] | None = None) -> _T | Self:
+            raise NotImplementedError()
+
+        def __set_name__(self, owner: type[Any], name: str) -> None: ...
+
+        # __set__ is not defined at runtime, but @cached_property is designed to be settable
+        def __set__(self, instance: object, value: _T) -> None: ...
+else:
+    from functools import cached_property as cached_property
+
+    typed_cached_property = cached_property

venv/lib/python3.12/site-packages/openai/_constants.py

@@ -0,0 +1,14 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+import httpx
+
+RAW_RESPONSE_HEADER = "X-Stainless-Raw-Response"
+OVERRIDE_CAST_TO_HEADER = "____stainless_override_cast_to"
+
+# default timeout is 10 minutes
+DEFAULT_TIMEOUT = httpx.Timeout(timeout=600, connect=5.0)
+DEFAULT_MAX_RETRIES = 2
+DEFAULT_CONNECTION_LIMITS = httpx.Limits(max_connections=1000, max_keepalive_connections=100)
+
+INITIAL_RETRY_DELAY = 0.5
+MAX_RETRY_DELAY = 8.0

venv/lib/python3.12/site-packages/openai/_event_handler.py

@@ -0,0 +1,85 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+import threading
+from typing import Any, Callable
+
+EventHandler = Callable[..., Any]
+
+
+class EventHandlerRegistry:
+    """Thread-safe (optional) registry of event handlers."""
+
+    def __init__(self, *, use_lock: bool = False) -> None:
+        self._handlers: dict[str, list[EventHandler]] = {}
+        self._once_ids: set[int] = set()
+        self._lock: threading.Lock | None = threading.Lock() if use_lock else None
+
+    def _acquire(self) -> None:
+        if self._lock is not None:
+            self._lock.acquire()
+
+    def _release(self) -> None:
+        if self._lock is not None:
+            self._lock.release()
+
+    def add(self, event_type: str, handler: EventHandler, *, once: bool = False) -> None:
+        self._acquire()
+        try:
+            handlers = self._handlers.setdefault(event_type, [])
+            handlers.append(handler)
+            if once:
+                self._once_ids.add(id(handler))
+        finally:
+            self._release()
+
+    def remove(self, event_type: str, handler: EventHandler) -> None:
+        self._acquire()
+        try:
+            handlers = self._handlers.get(event_type)
+            if handlers is not None:
+                try:
+                    handlers.remove(handler)
+                except ValueError:
+                    pass
+                self._once_ids.discard(id(handler))
+        finally:
+            self._release()
+
+    def get_handlers(self, event_type: str) -> list[EventHandler]:
+        """Return a snapshot of handlers for the given event type, removing once-handlers."""
+        self._acquire()
+        try:
+            handlers = self._handlers.get(event_type)
+            if not handlers:
+                return []
+            result = list(handlers)
+            to_remove = [h for h in result if id(h) in self._once_ids]
+            for h in to_remove:
+                handlers.remove(h)
+                self._once_ids.discard(id(h))
+            return result
+        finally:
+            self._release()
+
+    def has_handlers(self, event_type: str) -> bool:
+        self._acquire()
+        try:
+            handlers = self._handlers.get(event_type)
+            return bool(handlers)
+        finally:
+            self._release()
+
+    def merge_into(self, target: EventHandlerRegistry) -> None:
+        """Move all handlers from this registry into *target*, then clear self."""
+        self._acquire()
+        try:
+            for event_type, handlers in self._handlers.items():
+                for handler in handlers:
+                    once = id(handler) in self._once_ids
+                    target.add(event_type, handler, once=once)
+            self._handlers.clear()
+            self._once_ids.clear()
+        finally:
+            self._release()

venv/lib/python3.12/site-packages/openai/_exceptions.py

@@ -0,0 +1,207 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Optional, cast
+from typing_extensions import Literal
+
+import httpx
+
+from ._utils import is_dict
+from ._models import construct_type
+from .types.shared.oauth_error_code import OAuthErrorCode
+
+if TYPE_CHECKING:
+    from .types.chat import ChatCompletion
+
+__all__ = [
+    "BadRequestError",
+    "AuthenticationError",
+    "OAuthError",
+    "PermissionDeniedError",
+    "NotFoundError",
+    "ConflictError",
+    "UnprocessableEntityError",
+    "RateLimitError",
+    "InternalServerError",
+    "LengthFinishReasonError",
+    "ContentFilterFinishReasonError",
+    "InvalidWebhookSignatureError",
+    "SubjectTokenProviderError",
+    "WebSocketConnectionClosedError",
+    "WebSocketQueueFullError",
+]
+
+
+class OpenAIError(Exception):
+    pass
+
+
+class SubjectTokenProviderError(OpenAIError):
+    response: httpx.Response | None
+
+    def __init__(self, message: str, *, response: httpx.Response | None = None) -> None:
+        super().__init__(message)
+        self.response = response
+
+
+class APIError(OpenAIError):
+    message: str
+    request: httpx.Request
+
+    body: object | None
+    """The API response body.
+
+    If the API responded with a valid JSON structure then this property will be the
+    decoded result.
+
+    If it isn't a valid JSON structure then this will be the raw response.
+
+    If there was no response associated with this error then it will be `None`.
+    """
+
+    code: Optional[str] = None
+    param: Optional[str] = None
+    type: Optional[str]
+
+    def __init__(self, message: str, request: httpx.Request, *, body: object | None) -> None:
+        super().__init__(message)
+        self.request = request
+        self.message = message
+        self.body = body
+
+        if is_dict(body):
+            self.code = cast(Any, construct_type(type_=Optional[str], value=body.get("code")))
+            self.param = cast(Any, construct_type(type_=Optional[str], value=body.get("param")))
+            self.type = cast(Any, construct_type(type_=str, value=body.get("type")))
+        else:
+            self.code = None
+            self.param = None
+            self.type = None
+
+
+class APIResponseValidationError(APIError):
+    response: httpx.Response
+    status_code: int
+
+    def __init__(self, response: httpx.Response, body: object | None, *, message: str | None = None) -> None:
+        super().__init__(message or "Data returned by API invalid for expected schema.", response.request, body=body)
+        self.response = response
+        self.status_code = response.status_code
+
+
+class APIStatusError(APIError):
+    """Raised when an API response has a status code of 4xx or 5xx."""
+
+    response: httpx.Response
+    status_code: int
+    request_id: str | None
+
+    def __init__(self, message: str, *, response: httpx.Response, body: object | None) -> None:
+        super().__init__(message, response.request, body=body)
+        self.response = response
+        self.status_code = response.status_code
+        self.request_id = response.headers.get("x-request-id")
+
+
+class APIConnectionError(APIError):
+    def __init__(self, *, message: str = "Connection error.", request: httpx.Request) -> None:
+        super().__init__(message, request, body=None)
+
+
+class APITimeoutError(APIConnectionError):
+    def __init__(self, request: httpx.Request) -> None:
+        super().__init__(message="Request timed out.", request=request)
+
+
+class BadRequestError(APIStatusError):
+    status_code: Literal[400] = 400  # pyright: ignore[reportIncompatibleVariableOverride]
+
+
+class AuthenticationError(APIStatusError):
+    status_code: Literal[401] = 401  # pyright: ignore[reportIncompatibleVariableOverride]
+
+
+class OAuthError(AuthenticationError):
+    error: Optional[OAuthErrorCode]
+
+    def __init__(self, *, response: httpx.Response, body: object | None) -> None:
+        message = "OAuth authentication error."
+        error = None
+
+        if is_dict(body):
+            error = body.get("error")
+            description = body.get("error_description")
+            if description and isinstance(description, str):
+                message = description
+
+        super().__init__(message, response=response, body=body)
+        self.error = cast(Optional[OAuthErrorCode], error)
+
+
+class PermissionDeniedError(APIStatusError):
+    status_code: Literal[403] = 403  # pyright: ignore[reportIncompatibleVariableOverride]
+
+
+class NotFoundError(APIStatusError):
+    status_code: Literal[404] = 404  # pyright: ignore[reportIncompatibleVariableOverride]
+
+
+class ConflictError(APIStatusError):
+    status_code: Literal[409] = 409  # pyright: ignore[reportIncompatibleVariableOverride]
+
+
+class UnprocessableEntityError(APIStatusError):
+    status_code: Literal[422] = 422  # pyright: ignore[reportIncompatibleVariableOverride]
+
+
+class RateLimitError(APIStatusError):
+    status_code: Literal[429] = 429  # pyright: ignore[reportIncompatibleVariableOverride]
+
+
+class InternalServerError(APIStatusError):
+    pass
+
+
+class LengthFinishReasonError(OpenAIError):
+    completion: ChatCompletion
+    """The completion that caused this error.
+
+    Note: this will *not* be a complete `ChatCompletion` object when streaming as `usage`
+          will not be included.
+    """
+
+    def __init__(self, *, completion: ChatCompletion) -> None:
+        msg = "Could not parse response content as the length limit was reached"
+        if completion.usage:
+            msg += f" - {completion.usage}"
+
+        super().__init__(msg)
+        self.completion = completion
+
+
+class ContentFilterFinishReasonError(OpenAIError):
+    def __init__(self) -> None:
+        super().__init__(
+            f"Could not parse response content as the request was rejected by the content filter",
+        )
+
+
+class InvalidWebhookSignatureError(ValueError):
+    """Raised when a webhook signature is invalid, meaning the computed signature does not match the expected signature."""
+
+
+class WebSocketConnectionClosedError(OpenAIError):
+    """Raised when a WebSocket connection closes with unsent messages."""
+
+    unsent_messages: list[str]
+
+    def __init__(self, message: str, *, unsent_messages: list[str]) -> None:
+        super().__init__(message)
+        self.unsent_messages = unsent_messages
+
+
+class WebSocketQueueFullError(OpenAIError):
+    """Raised when the outgoing WebSocket message queue exceeds its byte-size limit."""
+
+    pass

venv/lib/python3.12/site-packages/openai/_extras/__init__.py

@@ -0,0 +1,3 @@
+from .numpy_proxy import numpy as numpy, has_numpy as has_numpy
+from .pandas_proxy import pandas as pandas
+from .sounddevice_proxy import sounddevice as sounddevice

venv/lib/python3.12/site-packages/openai/_extras/_common.py

@@ -0,0 +1,21 @@
+from .._exceptions import OpenAIError
+
+INSTRUCTIONS = """
+
+OpenAI error:
+
+    missing `{library}`
+
+This feature requires additional dependencies:
+
+    $ pip install openai[{extra}]
+
+"""
+
+
+def format_instructions(*, library: str, extra: str) -> str:
+    return INSTRUCTIONS.format(library=library, extra=extra)
+
+
+class MissingDependencyError(OpenAIError):
+    pass

venv/lib/python3.12/site-packages/openai/_extras/numpy_proxy.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+from typing_extensions import override
+
+from .._utils import LazyProxy
+from ._common import MissingDependencyError, format_instructions
+
+if TYPE_CHECKING:
+    import numpy as numpy
+
+
+NUMPY_INSTRUCTIONS = format_instructions(library="numpy", extra="voice_helpers")
+
+
+class NumpyProxy(LazyProxy[Any]):
+    @override
+    def __load__(self) -> Any:
+        try:
+            import numpy
+        except ImportError as err:
+            raise MissingDependencyError(NUMPY_INSTRUCTIONS) from err
+
+        return numpy
+
+
+if not TYPE_CHECKING:
+    numpy = NumpyProxy()
+
+
+def has_numpy() -> bool:
+    try:
+        import numpy  # noqa: F401  # pyright: ignore[reportUnusedImport]
+    except ImportError:
+        return False
+
+    return True

venv/lib/python3.12/site-packages/openai/_extras/pandas_proxy.py

@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+from typing_extensions import override
+
+from .._utils import LazyProxy
+from ._common import MissingDependencyError, format_instructions
+
+if TYPE_CHECKING:
+    import pandas as pandas
+
+
+PANDAS_INSTRUCTIONS = format_instructions(library="pandas", extra="datalib")
+
+
+class PandasProxy(LazyProxy[Any]):
+    @override
+    def __load__(self) -> Any:
+        try:
+            import pandas
+        except ImportError as err:
+            raise MissingDependencyError(PANDAS_INSTRUCTIONS) from err
+
+        return pandas
+
+
+if not TYPE_CHECKING:
+    pandas = PandasProxy()

venv/lib/python3.12/site-packages/openai/_extras/sounddevice_proxy.py

@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+from typing_extensions import override
+
+from .._utils import LazyProxy
+from ._common import MissingDependencyError, format_instructions
+
+if TYPE_CHECKING:
+    import sounddevice as sounddevice  # type: ignore
+
+
+SOUNDDEVICE_INSTRUCTIONS = format_instructions(library="sounddevice", extra="voice_helpers")
+
+
+class SounddeviceProxy(LazyProxy[Any]):
+    @override
+    def __load__(self) -> Any:
+        try:
+            import sounddevice  # type: ignore
+        except ImportError as err:
+            raise MissingDependencyError(SOUNDDEVICE_INSTRUCTIONS) from err
+
+        return sounddevice
+
+
+if not TYPE_CHECKING:
+    sounddevice = SounddeviceProxy()

venv/lib/python3.12/site-packages/openai/_files.py

@@ -0,0 +1,173 @@
+from __future__ import annotations
+
+import io
+import os
+import pathlib
+from typing import Sequence, cast, overload
+from typing_extensions import TypeVar, TypeGuard
+
+import anyio
+
+from ._types import (
+    FileTypes,
+    FileContent,
+    RequestFiles,
+    HttpxFileTypes,
+    Base64FileInput,
+    HttpxFileContent,
+    HttpxRequestFiles,
+)
+from ._utils import is_list, is_mapping, is_tuple_t, is_mapping_t, is_sequence_t
+
+_T = TypeVar("_T")
+
+
+def is_base64_file_input(obj: object) -> TypeGuard[Base64FileInput]:
+    return isinstance(obj, io.IOBase) or isinstance(obj, os.PathLike)
+
+
+def is_file_content(obj: object) -> TypeGuard[FileContent]:
+    return (
+        isinstance(obj, bytes) or isinstance(obj, tuple) or isinstance(obj, io.IOBase) or isinstance(obj, os.PathLike)
+    )
+
+
+def assert_is_file_content(obj: object, *, key: str | None = None) -> None:
+    if not is_file_content(obj):
+        prefix = f"Expected entry at `{key}`" if key is not None else f"Expected file input `{obj!r}`"
+        raise RuntimeError(
+            f"{prefix} to be bytes, an io.IOBase instance, PathLike or a tuple but received {type(obj)} instead. See https://github.com/openai/openai-python/tree/main#file-uploads"
+        ) from None
+
+
+@overload
+def to_httpx_files(files: None) -> None: ...
+
+
+@overload
+def to_httpx_files(files: RequestFiles) -> HttpxRequestFiles: ...
+
+
+def to_httpx_files(files: RequestFiles | None) -> HttpxRequestFiles | None:
+    if files is None:
+        return None
+
+    if is_mapping_t(files):
+        files = {key: _transform_file(file) for key, file in files.items()}
+    elif is_sequence_t(files):
+        files = [(key, _transform_file(file)) for key, file in files]
+    else:
+        raise TypeError(f"Unexpected file type input {type(files)}, expected mapping or sequence")
+
+    return files
+
+
+def _transform_file(file: FileTypes) -> HttpxFileTypes:
+    if is_file_content(file):
+        if isinstance(file, os.PathLike):
+            path = pathlib.Path(file)
+            return (path.name, path.read_bytes())
+
+        return file
+
+    if is_tuple_t(file):
+        return (file[0], read_file_content(file[1]), *file[2:])
+
+    raise TypeError(f"Expected file types input to be a FileContent type or to be a tuple")
+
+
+def read_file_content(file: FileContent) -> HttpxFileContent:
+    if isinstance(file, os.PathLike):
+        return pathlib.Path(file).read_bytes()
+    return file
+
+
+@overload
+async def async_to_httpx_files(files: None) -> None: ...
+
+
+@overload
+async def async_to_httpx_files(files: RequestFiles) -> HttpxRequestFiles: ...
+
+
+async def async_to_httpx_files(files: RequestFiles | None) -> HttpxRequestFiles | None:
+    if files is None:
+        return None
+
+    if is_mapping_t(files):
+        files = {key: await _async_transform_file(file) for key, file in files.items()}
+    elif is_sequence_t(files):
+        files = [(key, await _async_transform_file(file)) for key, file in files]
+    else:
+        raise TypeError(f"Unexpected file type input {type(files)}, expected mapping or sequence")
+
+    return files
+
+
+async def _async_transform_file(file: FileTypes) -> HttpxFileTypes:
+    if is_file_content(file):
+        if isinstance(file, os.PathLike):
+            path = anyio.Path(file)
+            return (path.name, await path.read_bytes())
+
+        return file
+
+    if is_tuple_t(file):
+        return (file[0], await async_read_file_content(file[1]), *file[2:])
+
+    raise TypeError(f"Expected file types input to be a FileContent type or to be a tuple")
+
+
+async def async_read_file_content(file: FileContent) -> HttpxFileContent:
+    if isinstance(file, os.PathLike):
+        return await anyio.Path(file).read_bytes()
+
+    return file
+
+
+def deepcopy_with_paths(item: _T, paths: Sequence[Sequence[str]]) -> _T:
+    """Copy only the containers along the given paths.
+
+    Used to guard against mutation by extract_files without copying the entire structure.
+    Only dicts and lists that lie on a path are copied; everything else
+    is returned by reference.
+
+    For example, given paths=[["foo", "files", "file"]] and the structure:
+        {
+            "foo": {
+                "bar": {"baz": {}},
+                "files": {"file": <content>}
+            }
+        }
+    The root dict, "foo", and "files" are copied (they lie on the path).
+    "bar" and "baz" are returned by reference (off the path).
+    """
+    return _deepcopy_with_paths(item, paths, 0)
+
+
+def _deepcopy_with_paths(item: _T, paths: Sequence[Sequence[str]], index: int) -> _T:
+    if not paths:
+        return item
+    if is_mapping(item):
+        key_to_paths: dict[str, list[Sequence[str]]] = {}
+        for path in paths:
+            if index < len(path):
+                key_to_paths.setdefault(path[index], []).append(path)
+
+        # if no path continues through this mapping, it won't be mutated and copying it is redundant
+        if not key_to_paths:
+            return item
+
+        result = dict(item)
+        for key, subpaths in key_to_paths.items():
+            if key in result:
+                result[key] = _deepcopy_with_paths(result[key], subpaths, index + 1)
+        return cast(_T, result)
+    if is_list(item):
+        array_paths = [path for path in paths if index < len(path) and path[index] == "<array>"]
+
+        # if no path expects a list here, nothing will be mutated inside it - return by reference
+        if not array_paths:
+            return cast(_T, item)
+        return cast(_T, [_deepcopy_with_paths(entry, array_paths, index + 1) for entry in item])
+    return item

venv/lib/python3.12/site-packages/openai/_legacy_response.py

@@ -0,0 +1,491 @@
+from __future__ import annotations
+
+import os
+import inspect
+import logging
+import datetime
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Union,
+    Generic,
+    TypeVar,
+    Callable,
+    Iterator,
+    AsyncIterator,
+    cast,
+    overload,
+)
+from typing_extensions import Awaitable, ParamSpec, override, deprecated, get_origin
+
+import anyio
+import httpx
+import pydantic
+
+from ._types import NoneType
+from ._utils import is_given, extract_type_arg, is_annotated_type, is_type_alias_type
+from ._models import BaseModel, is_basemodel, add_request_id
+from ._constants import RAW_RESPONSE_HEADER
+from ._streaming import Stream, AsyncStream, is_stream_class_type, extract_stream_chunk_type
+from ._exceptions import APIResponseValidationError
+
+if TYPE_CHECKING:
+    from ._models import FinalRequestOptions
+    from ._base_client import BaseClient
+
+
+P = ParamSpec("P")
+R = TypeVar("R")
+_T = TypeVar("_T")
+
+log: logging.Logger = logging.getLogger(__name__)
+
+
+class LegacyAPIResponse(Generic[R]):
+    """This is a legacy class as it will be replaced by `APIResponse`
+    and `AsyncAPIResponse` in the `_response.py` file in the next major
+    release.
+
+    For the sync client this will mostly be the same with the exception
+    of `content` & `text` will be methods instead of properties. In the
+    async client, all methods will be async.
+
+    A migration script will be provided & the migration in general should
+    be smooth.
+    """
+
+    _cast_to: type[R]
+    _client: BaseClient[Any, Any]
+    _parsed_by_type: dict[type[Any], Any]
+    _stream: bool
+    _stream_cls: type[Stream[Any]] | type[AsyncStream[Any]] | None
+    _options: FinalRequestOptions
+
+    http_response: httpx.Response
+
+    retries_taken: int
+    """The number of retries made. If no retries happened this will be `0`"""
+
+    def __init__(
+        self,
+        *,
+        raw: httpx.Response,
+        cast_to: type[R],
+        client: BaseClient[Any, Any],
+        stream: bool,
+        stream_cls: type[Stream[Any]] | type[AsyncStream[Any]] | None,
+        options: FinalRequestOptions,
+        retries_taken: int = 0,
+    ) -> None:
+        self._cast_to = cast_to
+        self._client = client
+        self._parsed_by_type = {}
+        self._stream = stream
+        self._stream_cls = stream_cls
+        self._options = options
+        self.http_response = raw
+        self.retries_taken = retries_taken
+
+    @property
+    def request_id(self) -> str | None:
+        return self.http_response.headers.get("x-request-id")  # type: ignore[no-any-return]
+
+    @overload
+    def parse(self, *, to: type[_T]) -> _T: ...
+
+    @overload
+    def parse(self) -> R: ...
+
+    def parse(self, *, to: type[_T] | None = None) -> R | _T:
+        """Returns the rich python representation of this response's data.
+
+        NOTE: For the async client: this will become a coroutine in the next major version.
+
+        For lower-level control, see `.read()`, `.json()`, `.iter_bytes()`.
+
+        You can customise the type that the response is parsed into through
+        the `to` argument, e.g.
+
+        ```py
+        from openai import BaseModel
+
+
+        class MyModel(BaseModel):
+            foo: str
+
+
+        obj = response.parse(to=MyModel)
+        print(obj.foo)
+        ```
+
+        We support parsing:
+          - `BaseModel`
+          - `dict`
+          - `list`
+          - `Union`
+          - `str`
+          - `int`
+          - `float`
+          - `httpx.Response`
+        """
+        cache_key = to if to is not None else self._cast_to
+        cached = self._parsed_by_type.get(cache_key)
+        if cached is not None:
+            return cached  # type: ignore[no-any-return]
+
+        parsed = self._parse(to=to)
+        if is_given(self._options.post_parser):
+            parsed = self._options.post_parser(parsed)
+
+        if isinstance(parsed, BaseModel):
+            add_request_id(parsed, self.request_id)
+
+        self._parsed_by_type[cache_key] = parsed
+        return cast(R, parsed)
+
+    @property
+    def headers(self) -> httpx.Headers:
+        return self.http_response.headers
+
+    @property
+    def http_request(self) -> httpx.Request:
+        return self.http_response.request
+
+    @property
+    def status_code(self) -> int:
+        return self.http_response.status_code
+
+    @property
+    def url(self) -> httpx.URL:
+        return self.http_response.url
+
+    @property
+    def method(self) -> str:
+        return self.http_request.method
+
+    @property
+    def content(self) -> bytes:
+        """Return the binary response content.
+
+        NOTE: this will be removed in favour of `.read()` in the
+        next major version.
+        """
+        return self.http_response.content
+
+    @property
+    def text(self) -> str:
+        """Return the decoded response content.
+
+        NOTE: this will be turned into a method in the next major version.
+        """
+        return self.http_response.text
+
+    @property
+    def http_version(self) -> str:
+        return self.http_response.http_version
+
+    @property
+    def is_closed(self) -> bool:
+        return self.http_response.is_closed
+
+    @property
+    def elapsed(self) -> datetime.timedelta:
+        """The time taken for the complete request/response cycle to complete."""
+        return self.http_response.elapsed
+
+    def _parse(self, *, to: type[_T] | None = None) -> R | _T:
+        cast_to = to if to is not None else self._cast_to
+
+        # unwrap `TypeAlias('Name', T)` -> `T`
+        if is_type_alias_type(cast_to):
+            cast_to = cast_to.__value__  # type: ignore[unreachable]
+
+        # unwrap `Annotated[T, ...]` -> `T`
+        if cast_to and is_annotated_type(cast_to):
+            cast_to = extract_type_arg(cast_to, 0)
+
+        origin = get_origin(cast_to) or cast_to
+
+        if self._stream:
+            if to:
+                if not is_stream_class_type(to):
+                    raise TypeError(f"Expected custom parse type to be a subclass of {Stream} or {AsyncStream}")
+
+                return cast(
+                    _T,
+                    to(
+                        cast_to=extract_stream_chunk_type(
+                            to,
+                            failure_message="Expected custom stream type to be passed with a type argument, e.g. Stream[ChunkType]",
+                        ),
+                        response=self.http_response,
+                        client=cast(Any, self._client),
+                        options=self._options,
+                    ),
+                )
+
+            if self._stream_cls:
+                return cast(
+                    R,
+                    self._stream_cls(
+                        cast_to=extract_stream_chunk_type(self._stream_cls),
+                        response=self.http_response,
+                        client=cast(Any, self._client),
+                        options=self._options,
+                    ),
+                )
+
+            stream_cls = cast("type[Stream[Any]] | type[AsyncStream[Any]] | None", self._client._default_stream_cls)
+            if stream_cls is None:
+                raise MissingStreamClassError()
+
+            return cast(
+                R,
+                stream_cls(
+                    cast_to=cast_to,
+                    response=self.http_response,
+                    client=cast(Any, self._client),
+                    options=self._options,
+                ),
+            )
+
+        if cast_to is NoneType:
+            return cast(R, None)
+
+        response = self.http_response
+        if cast_to == str:
+            return cast(R, response.text)
+
+        if cast_to == int:
+            return cast(R, int(response.text))
+
+        if cast_to == float:
+            return cast(R, float(response.text))
+
+        if cast_to == bool:
+            return cast(R, response.text.lower() == "true")
+
+        if inspect.isclass(origin) and issubclass(origin, HttpxBinaryResponseContent):
+            return cast(R, cast_to(response))  # type: ignore
+
+        if origin == LegacyAPIResponse:
+            raise RuntimeError("Unexpected state - cast_to is `APIResponse`")
+
+        if inspect.isclass(
+            origin  # pyright: ignore[reportUnknownArgumentType]
+        ) and issubclass(origin, httpx.Response):
+            # Because of the invariance of our ResponseT TypeVar, users can subclass httpx.Response
+            # and pass that class to our request functions. We cannot change the variance to be either
+            # covariant or contravariant as that makes our usage of ResponseT illegal. We could construct
+            # the response class ourselves but that is something that should be supported directly in httpx
+            # as it would be easy to incorrectly construct the Response object due to the multitude of arguments.
+            if cast_to != httpx.Response:
+                raise ValueError(f"Subclasses of httpx.Response cannot be passed to `cast_to`")
+            return cast(R, response)
+
+        if (
+            inspect.isclass(
+                origin  # pyright: ignore[reportUnknownArgumentType]
+            )
+            and not issubclass(origin, BaseModel)
+            and issubclass(origin, pydantic.BaseModel)
+        ):
+            raise TypeError("Pydantic models must subclass our base model type, e.g. `from openai import BaseModel`")
+
+        if (
+            cast_to is not object
+            and not origin is list
+            and not origin is dict
+            and not origin is Union
+            and not issubclass(origin, BaseModel)
+        ):
+            raise RuntimeError(
+                f"Unsupported type, expected {cast_to} to be a subclass of {BaseModel}, {dict}, {list}, {Union}, {NoneType}, {str} or {httpx.Response}."
+            )
+
+        # split is required to handle cases where additional information is included
+        # in the response, e.g. application/json; charset=utf-8
+        content_type, *_ = response.headers.get("content-type", "*").split(";")
+        if not content_type.endswith("json"):
+            if is_basemodel(cast_to):
+                try:
+                    data = response.json()
+                except Exception as exc:
+                    log.debug("Could not read JSON from response data due to %s - %s", type(exc), exc)
+                else:
+                    return self._client._process_response_data(
+                        data=data,
+                        cast_to=cast_to,  # type: ignore
+                        response=response,
+                    )
+
+            if self._client._strict_response_validation:
+                raise APIResponseValidationError(
+                    response=response,
+                    message=f"Expected Content-Type response header to be `application/json` but received `{content_type}` instead.",
+                    body=response.text,
+                )
+
+            # If the API responds with content that isn't JSON then we just return
+            # the (decoded) text without performing any parsing so that you can still
+            # handle the response however you need to.
+            return response.text  # type: ignore
+
+        data = response.json()
+
+        return self._client._process_response_data(
+            data=data,
+            cast_to=cast_to,  # type: ignore
+            response=response,
+        )
+
+    @override
+    def __repr__(self) -> str:
+        return f"<APIResponse [{self.status_code} {self.http_response.reason_phrase}] type={self._cast_to}>"
+
+
+class MissingStreamClassError(TypeError):
+    def __init__(self) -> None:
+        super().__init__(
+            "The `stream` argument was set to `True` but the `stream_cls` argument was not given. See `openai._streaming` for reference",
+        )
+
+
+def to_raw_response_wrapper(func: Callable[P, R]) -> Callable[P, LegacyAPIResponse[R]]:
+    """Higher order function that takes one of our bound API methods and wraps it
+    to support returning the raw `APIResponse` object directly.
+    """
+
+    @functools.wraps(func)
+    def wrapped(*args: P.args, **kwargs: P.kwargs) -> LegacyAPIResponse[R]:
+        extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})}
+        extra_headers[RAW_RESPONSE_HEADER] = "true"
+
+        kwargs["extra_headers"] = extra_headers
+
+        return cast(LegacyAPIResponse[R], func(*args, **kwargs))
+
+    return wrapped
+
+
+def async_to_raw_response_wrapper(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[LegacyAPIResponse[R]]]:
+    """Higher order function that takes one of our bound API methods and wraps it
+    to support returning the raw `APIResponse` object directly.
+    """
+
+    @functools.wraps(func)
+    async def wrapped(*args: P.args, **kwargs: P.kwargs) -> LegacyAPIResponse[R]:
+        extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})}
+        extra_headers[RAW_RESPONSE_HEADER] = "true"
+
+        kwargs["extra_headers"] = extra_headers
+
+        return cast(LegacyAPIResponse[R], await func(*args, **kwargs))
+
+    return wrapped
+
+
+class HttpxBinaryResponseContent:
+    response: httpx.Response
+
+    def __init__(self, response: httpx.Response) -> None:
+        self.response = response
+
+    @property
+    def content(self) -> bytes:
+        return self.response.content
+
+    @property
+    def text(self) -> str:
+        return self.response.text
+
+    @property
+    def encoding(self) -> str | None:
+        return self.response.encoding
+
+    @property
+    def charset_encoding(self) -> str | None:
+        return self.response.charset_encoding
+
+    def json(self, **kwargs: Any) -> Any:
+        return self.response.json(**kwargs)
+
+    def read(self) -> bytes:
+        return self.response.read()
+
+    def iter_bytes(self, chunk_size: int | None = None) -> Iterator[bytes]:
+        return self.response.iter_bytes(chunk_size)
+
+    def iter_text(self, chunk_size: int | None = None) -> Iterator[str]:
+        return self.response.iter_text(chunk_size)
+
+    def iter_lines(self) -> Iterator[str]:
+        return self.response.iter_lines()
+
+    def iter_raw(self, chunk_size: int | None = None) -> Iterator[bytes]:
+        return self.response.iter_raw(chunk_size)
+
+    def write_to_file(
+        self,
+        file: str | os.PathLike[str],
+    ) -> None:
+        """Write the output to the given file.
+
+        Accepts a filename or any path-like object, e.g. pathlib.Path
+
+        Note: if you want to stream the data to the file instead of writing
+        all at once then you should use `.with_streaming_response` when making
+        the API request, e.g. `client.with_streaming_response.foo().stream_to_file('my_filename.txt')`
+        """
+        with open(file, mode="wb") as f:
+            for data in self.response.iter_bytes():
+                f.write(data)
+
+    @deprecated(
+        "Due to a bug, this method doesn't actually stream the response content, `.with_streaming_response.method()` should be used instead"
+    )
+    def stream_to_file(
+        self,
+        file: str | os.PathLike[str],
+        *,
+        chunk_size: int | None = None,
+    ) -> None:
+        with open(file, mode="wb") as f:
+            for data in self.response.iter_bytes(chunk_size):
+                f.write(data)
+
+    def close(self) -> None:
+        return self.response.close()
+
+    async def aread(self) -> bytes:
+        return await self.response.aread()
+
+    async def aiter_bytes(self, chunk_size: int | None = None) -> AsyncIterator[bytes]:
+        return self.response.aiter_bytes(chunk_size)
+
+    async def aiter_text(self, chunk_size: int | None = None) -> AsyncIterator[str]:
+        return self.response.aiter_text(chunk_size)
+
+    async def aiter_lines(self) -> AsyncIterator[str]:
+        return self.response.aiter_lines()
+
+    async def aiter_raw(self, chunk_size: int | None = None) -> AsyncIterator[bytes]:
+        return self.response.aiter_raw(chunk_size)
+
+    @deprecated(
+        "Due to a bug, this method doesn't actually stream the response content, `.with_streaming_response.method()` should be used instead"
+    )
+    async def astream_to_file(
+        self,
+        file: str | os.PathLike[str],
+        *,
+        chunk_size: int | None = None,
+    ) -> None:
+        path = anyio.Path(file)
+        async with await path.open(mode="wb") as f:
+            async for data in self.response.aiter_bytes(chunk_size):
+                await f.write(data)
+
+    async def aclose(self) -> None:
+        return await self.response.aclose()

venv/lib/python3.12/site-packages/openai/_module_client.py

@@ -0,0 +1,189 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+from typing_extensions import override
+
+if TYPE_CHECKING:
+    from .resources.files import Files
+    from .resources.images import Images
+    from .resources.models import Models
+    from .resources.videos import Videos
+    from .resources.batches import Batches
+    from .resources.beta.beta import Beta
+    from .resources.chat.chat import Chat
+    from .resources.embeddings import Embeddings
+    from .resources.admin.admin import Admin
+    from .resources.audio.audio import Audio
+    from .resources.completions import Completions
+    from .resources.evals.evals import Evals
+    from .resources.moderations import Moderations
+    from .resources.skills.skills import Skills
+    from .resources.uploads.uploads import Uploads
+    from .resources.realtime.realtime import Realtime
+    from .resources.webhooks.webhooks import Webhooks
+    from .resources.responses.responses import Responses
+    from .resources.containers.containers import Containers
+    from .resources.fine_tuning.fine_tuning import FineTuning
+    from .resources.conversations.conversations import Conversations
+    from .resources.vector_stores.vector_stores import VectorStores
+
+from . import _load_client
+from ._utils import LazyProxy
+
+
+class ChatProxy(LazyProxy["Chat"]):
+    @override
+    def __load__(self) -> Chat:
+        return _load_client().chat
+
+
+class BetaProxy(LazyProxy["Beta"]):
+    @override
+    def __load__(self) -> Beta:
+        return _load_client().beta
+
+
+class FilesProxy(LazyProxy["Files"]):
+    @override
+    def __load__(self) -> Files:
+        return _load_client().files
+
+
+class AudioProxy(LazyProxy["Audio"]):
+    @override
+    def __load__(self) -> Audio:
+        return _load_client().audio
+
+
+class AdminProxy(LazyProxy["Admin"]):
+    @override
+    def __load__(self) -> Admin:
+        return _load_client().admin
+
+
+class EvalsProxy(LazyProxy["Evals"]):
+    @override
+    def __load__(self) -> Evals:
+        return _load_client().evals
+
+
+class ImagesProxy(LazyProxy["Images"]):
+    @override
+    def __load__(self) -> Images:
+        return _load_client().images
+
+
+class ModelsProxy(LazyProxy["Models"]):
+    @override
+    def __load__(self) -> Models:
+        return _load_client().models
+
+
+class SkillsProxy(LazyProxy["Skills"]):
+    @override
+    def __load__(self) -> Skills:
+        return _load_client().skills
+
+
+class VideosProxy(LazyProxy["Videos"]):
+    @override
+    def __load__(self) -> Videos:
+        return _load_client().videos
+
+
+class BatchesProxy(LazyProxy["Batches"]):
+    @override
+    def __load__(self) -> Batches:
+        return _load_client().batches
+
+
+class UploadsProxy(LazyProxy["Uploads"]):
+    @override
+    def __load__(self) -> Uploads:
+        return _load_client().uploads
+
+
+class WebhooksProxy(LazyProxy["Webhooks"]):
+    @override
+    def __load__(self) -> Webhooks:
+        return _load_client().webhooks
+
+
+class RealtimeProxy(LazyProxy["Realtime"]):
+    @override
+    def __load__(self) -> Realtime:
+        return _load_client().realtime
+
+
+class ResponsesProxy(LazyProxy["Responses"]):
+    @override
+    def __load__(self) -> Responses:
+        return _load_client().responses
+
+
+class EmbeddingsProxy(LazyProxy["Embeddings"]):
+    @override
+    def __load__(self) -> Embeddings:
+        return _load_client().embeddings
+
+
+class ContainersProxy(LazyProxy["Containers"]):
+    @override
+    def __load__(self) -> Containers:
+        return _load_client().containers
+
+
+class CompletionsProxy(LazyProxy["Completions"]):
+    @override
+    def __load__(self) -> Completions:
+        return _load_client().completions
+
+
+class ModerationsProxy(LazyProxy["Moderations"]):
+    @override
+    def __load__(self) -> Moderations:
+        return _load_client().moderations
+
+
+class FineTuningProxy(LazyProxy["FineTuning"]):
+    @override
+    def __load__(self) -> FineTuning:
+        return _load_client().fine_tuning
+
+
+class VectorStoresProxy(LazyProxy["VectorStores"]):
+    @override
+    def __load__(self) -> VectorStores:
+        return _load_client().vector_stores
+
+
+class ConversationsProxy(LazyProxy["Conversations"]):
+    @override
+    def __load__(self) -> Conversations:
+        return _load_client().conversations
+
+
+chat: Chat = ChatProxy().__as_proxied__()
+beta: Beta = BetaProxy().__as_proxied__()
+files: Files = FilesProxy().__as_proxied__()
+audio: Audio = AudioProxy().__as_proxied__()
+admin: Admin = AdminProxy().__as_proxied__()
+evals: Evals = EvalsProxy().__as_proxied__()
+images: Images = ImagesProxy().__as_proxied__()
+models: Models = ModelsProxy().__as_proxied__()
+skills: Skills = SkillsProxy().__as_proxied__()
+videos: Videos = VideosProxy().__as_proxied__()
+batches: Batches = BatchesProxy().__as_proxied__()
+uploads: Uploads = UploadsProxy().__as_proxied__()
+webhooks: Webhooks = WebhooksProxy().__as_proxied__()
+realtime: Realtime = RealtimeProxy().__as_proxied__()
+responses: Responses = ResponsesProxy().__as_proxied__()
+embeddings: Embeddings = EmbeddingsProxy().__as_proxied__()
+containers: Containers = ContainersProxy().__as_proxied__()
+completions: Completions = CompletionsProxy().__as_proxied__()
+moderations: Moderations = ModerationsProxy().__as_proxied__()
+fine_tuning: FineTuning = FineTuningProxy().__as_proxied__()
+vector_stores: VectorStores = VectorStoresProxy().__as_proxied__()
+conversations: Conversations = ConversationsProxy().__as_proxied__()

venv/lib/python3.12/site-packages/openai/_provider.py

@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+from typing import Callable, Protocol, Awaitable
+from weakref import WeakKeyDictionary
+from dataclasses import dataclass
+
+import httpx
+
+from ._models import FinalRequestOptions
+from ._exceptions import OpenAIError
+
+
+class _Provider:
+    """Opaque configuration returned by an OpenAI-owned provider factory."""
+
+    __slots__ = ("__weakref__",)
+
+
+@dataclass
+class _ProviderRuntime:
+    name: str
+    base_url: str | httpx.URL
+    transform_request: Callable[[FinalRequestOptions], FinalRequestOptions] | None = None
+    transform_async_request: Callable[[FinalRequestOptions], Awaitable[FinalRequestOptions]] | None = None
+    prepare_request: Callable[[httpx.Request], None] | None = None
+    prepare_async_request: Callable[[httpx.Request], Awaitable[None]] | None = None
+    normalize_response: Callable[[httpx.Response], httpx.Response] | None = None
+    normalize_async_response: Callable[[httpx.Response], Awaitable[httpx.Response]] | None = None
+
+
+class _ProviderDefinition(Protocol):
+    @property
+    def name(self) -> str: ...
+
+    def configure(self) -> _ProviderRuntime: ...
+
+
+# Provider factories capture configuration in definitions, while every client
+# gets fresh runtime state from ``definition.configure()``. Keeping definitions
+# outside the opaque provider object prevents arbitrary objects (including a
+# directly constructed ``_Provider``) from imitating an OpenAI-owned provider
+# and keeps credentials out of the object's representation. The weak mapping
+# also avoids retaining provider configuration after the public handle is gone.
+_provider_definitions: WeakKeyDictionary[_Provider, _ProviderDefinition] = WeakKeyDictionary()
+
+
+def _create_provider(definition: _ProviderDefinition) -> _Provider:  # pyright: ignore[reportUnusedFunction]
+    provider = _Provider()
+    _provider_definitions[provider] = definition
+    return provider
+
+
+def _provider_name(provider: _Provider) -> str:  # pyright: ignore[reportUnusedFunction]
+    return _get_provider_definition(provider).name
+
+
+def _configure_provider(provider: _Provider) -> _ProviderRuntime:  # pyright: ignore[reportUnusedFunction]
+    return _get_provider_definition(provider).configure()
+
+
+def _get_provider_definition(provider: _Provider) -> _ProviderDefinition:
+    try:
+        return _provider_definitions[provider]
+    except (KeyError, TypeError) as exc:
+        raise OpenAIError("Invalid provider. Providers must be created by an OpenAI provider factory.") from exc

venv/lib/python3.12/site-packages/openai/_qs.py

@@ -0,0 +1,149 @@
+from __future__ import annotations
+
+from typing import Any, List, Tuple, Union, Mapping, TypeVar
+from urllib.parse import parse_qs, urlencode
+from typing_extensions import get_args
+
+from ._types import NotGiven, ArrayFormat, NestedFormat, not_given
+from ._utils import flatten
+
+_T = TypeVar("_T")
+
+PrimitiveData = Union[str, int, float, bool, None]
+# this should be Data = Union[PrimitiveData, "List[Data]", "Tuple[Data]", "Mapping[str, Data]"]
+# https://github.com/microsoft/pyright/issues/3555
+Data = Union[PrimitiveData, List[Any], Tuple[Any], "Mapping[str, Any]"]
+Params = Mapping[str, Data]
+
+
+class Querystring:
+    array_format: ArrayFormat
+    nested_format: NestedFormat
+
+    def __init__(
+        self,
+        *,
+        array_format: ArrayFormat = "repeat",
+        nested_format: NestedFormat = "brackets",
+    ) -> None:
+        self.array_format = array_format
+        self.nested_format = nested_format
+
+    def parse(self, query: str) -> Mapping[str, object]:
+        # Note: custom format syntax is not supported yet
+        return parse_qs(query)
+
+    def stringify(
+        self,
+        params: Params,
+        *,
+        array_format: ArrayFormat | NotGiven = not_given,
+        nested_format: NestedFormat | NotGiven = not_given,
+    ) -> str:
+        return urlencode(
+            self.stringify_items(
+                params,
+                array_format=array_format,
+                nested_format=nested_format,
+            )
+        )
+
+    def stringify_items(
+        self,
+        params: Params,
+        *,
+        array_format: ArrayFormat | NotGiven = not_given,
+        nested_format: NestedFormat | NotGiven = not_given,
+    ) -> list[tuple[str, str]]:
+        opts = Options(
+            qs=self,
+            array_format=array_format,
+            nested_format=nested_format,
+        )
+        return flatten([self._stringify_item(key, value, opts) for key, value in params.items()])
+
+    def _stringify_item(
+        self,
+        key: str,
+        value: Data,
+        opts: Options,
+    ) -> list[tuple[str, str]]:
+        if isinstance(value, Mapping):
+            items: list[tuple[str, str]] = []
+            nested_format = opts.nested_format
+            for subkey, subvalue in value.items():
+                items.extend(
+                    self._stringify_item(
+                        # TODO: error if unknown format
+                        f"{key}.{subkey}" if nested_format == "dots" else f"{key}[{subkey}]",
+                        subvalue,
+                        opts,
+                    )
+                )
+            return items
+
+        if isinstance(value, (list, tuple)):
+            array_format = opts.array_format
+            if array_format == "comma":
+                return [
+                    (
+                        key,
+                        ",".join(self._primitive_value_to_str(item) for item in value if item is not None),
+                    ),
+                ]
+            elif array_format == "repeat":
+                items = []
+                for item in value:
+                    items.extend(self._stringify_item(key, item, opts))
+                return items
+            elif array_format == "indices":
+                items = []
+                for i, item in enumerate(value):
+                    items.extend(self._stringify_item(f"{key}[{i}]", item, opts))
+                return items
+            elif array_format == "brackets":
+                items = []
+                key = key + "[]"
+                for item in value:
+                    items.extend(self._stringify_item(key, item, opts))
+                return items
+            else:
+                raise NotImplementedError(
+                    f"Unknown array_format value: {array_format}, choose from {', '.join(get_args(ArrayFormat))}"
+                )
+
+        serialised = self._primitive_value_to_str(value)
+        if not serialised:
+            return []
+        return [(key, serialised)]
+
+    def _primitive_value_to_str(self, value: PrimitiveData) -> str:
+        # copied from httpx
+        if value is True:
+            return "true"
+        elif value is False:
+            return "false"
+        elif value is None:
+            return ""
+        return str(value)
+
+
+_qs = Querystring()
+parse = _qs.parse
+stringify = _qs.stringify
+stringify_items = _qs.stringify_items
+
+
+class Options:
+    array_format: ArrayFormat
+    nested_format: NestedFormat
+
+    def __init__(
+        self,
+        qs: Querystring = _qs,
+        *,
+        array_format: ArrayFormat | NotGiven = not_given,
+        nested_format: NestedFormat | NotGiven = not_given,
+    ) -> None:
+        self.array_format = qs.array_format if isinstance(array_format, NotGiven) else array_format
+        self.nested_format = qs.nested_format if isinstance(nested_format, NotGiven) else nested_format

venv/lib/python3.12/site-packages/openai/_resource.py

@@ -0,0 +1,43 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+import time
+from typing import TYPE_CHECKING
+
+import anyio
+
+if TYPE_CHECKING:
+    from ._client import OpenAI, AsyncOpenAI
+
+
+class SyncAPIResource:
+    _client: OpenAI
+
+    def __init__(self, client: OpenAI) -> None:
+        self._client = client
+        self._get = client.get
+        self._post = client.post
+        self._patch = client.patch
+        self._put = client.put
+        self._delete = client.delete
+        self._get_api_list = client.get_api_list
+
+    def _sleep(self, seconds: float) -> None:
+        time.sleep(seconds)
+
+
+class AsyncAPIResource:
+    _client: AsyncOpenAI
+
+    def __init__(self, client: AsyncOpenAI) -> None:
+        self._client = client
+        self._get = client.get
+        self._post = client.post
+        self._patch = client.patch
+        self._put = client.put
+        self._delete = client.delete
+        self._get_api_list = client.get_api_list
+
+    async def _sleep(self, seconds: float) -> None:
+        await anyio.sleep(seconds)

venv/lib/python3.12/site-packages/openai/_response.py

@@ -0,0 +1,851 @@
+from __future__ import annotations
+
+import os
+import inspect
+import logging
+import datetime
+import functools
+from types import TracebackType
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Union,
+    Generic,
+    TypeVar,
+    Callable,
+    Iterator,
+    AsyncIterator,
+    cast,
+    overload,
+)
+from typing_extensions import Awaitable, ParamSpec, override, get_origin
+
+import anyio
+import httpx
+import pydantic
+
+from ._types import NoneType
+from ._utils import is_given, extract_type_arg, is_annotated_type, is_type_alias_type, extract_type_var_from_base
+from ._models import BaseModel, is_basemodel, add_request_id
+from ._constants import RAW_RESPONSE_HEADER, OVERRIDE_CAST_TO_HEADER
+from ._streaming import Stream, AsyncStream, is_stream_class_type, extract_stream_chunk_type
+from ._exceptions import OpenAIError, APIResponseValidationError
+
+if TYPE_CHECKING:
+    from ._models import FinalRequestOptions
+    from ._base_client import BaseClient
+
+
+P = ParamSpec("P")
+R = TypeVar("R")
+_T = TypeVar("_T")
+_APIResponseT = TypeVar("_APIResponseT", bound="APIResponse[Any]")
+_AsyncAPIResponseT = TypeVar("_AsyncAPIResponseT", bound="AsyncAPIResponse[Any]")
+
+log: logging.Logger = logging.getLogger(__name__)
+
+
+class BaseAPIResponse(Generic[R]):
+    _cast_to: type[R]
+    _client: BaseClient[Any, Any]
+    _parsed_by_type: dict[type[Any], Any]
+    _is_sse_stream: bool
+    _stream_cls: type[Stream[Any]] | type[AsyncStream[Any]] | None
+    _options: FinalRequestOptions
+
+    http_response: httpx.Response
+
+    retries_taken: int
+    """The number of retries made. If no retries happened this will be `0`"""
+
+    def __init__(
+        self,
+        *,
+        raw: httpx.Response,
+        cast_to: type[R],
+        client: BaseClient[Any, Any],
+        stream: bool,
+        stream_cls: type[Stream[Any]] | type[AsyncStream[Any]] | None,
+        options: FinalRequestOptions,
+        retries_taken: int = 0,
+    ) -> None:
+        self._cast_to = cast_to
+        self._client = client
+        self._parsed_by_type = {}
+        self._is_sse_stream = stream
+        self._stream_cls = stream_cls
+        self._options = options
+        self.http_response = raw
+        self.retries_taken = retries_taken
+
+    @property
+    def headers(self) -> httpx.Headers:
+        return self.http_response.headers
+
+    @property
+    def http_request(self) -> httpx.Request:
+        """Returns the httpx Request instance associated with the current response."""
+        return self.http_response.request
+
+    @property
+    def status_code(self) -> int:
+        return self.http_response.status_code
+
+    @property
+    def url(self) -> httpx.URL:
+        """Returns the URL for which the request was made."""
+        return self.http_response.url
+
+    @property
+    def method(self) -> str:
+        return self.http_request.method
+
+    @property
+    def http_version(self) -> str:
+        return self.http_response.http_version
+
+    @property
+    def elapsed(self) -> datetime.timedelta:
+        """The time taken for the complete request/response cycle to complete."""
+        return self.http_response.elapsed
+
+    @property
+    def is_closed(self) -> bool:
+        """Whether or not the response body has been closed.
+
+        If this is False then there is response data that has not been read yet.
+        You must either fully consume the response body or call `.close()`
+        before discarding the response to prevent resource leaks.
+        """
+        return self.http_response.is_closed
+
+    @override
+    def __repr__(self) -> str:
+        return (
+            f"<{self.__class__.__name__} [{self.status_code} {self.http_response.reason_phrase}] type={self._cast_to}>"
+        )
+
+    def _parse(self, *, to: type[_T] | None = None) -> R | _T:
+        cast_to = to if to is not None else self._cast_to
+
+        # unwrap `TypeAlias('Name', T)` -> `T`
+        if is_type_alias_type(cast_to):
+            cast_to = cast_to.__value__  # type: ignore[unreachable]
+
+        # unwrap `Annotated[T, ...]` -> `T`
+        if cast_to and is_annotated_type(cast_to):
+            cast_to = extract_type_arg(cast_to, 0)
+
+        origin = get_origin(cast_to) or cast_to
+
+        if self._is_sse_stream:
+            if to:
+                if not is_stream_class_type(to):
+                    raise TypeError(f"Expected custom parse type to be a subclass of {Stream} or {AsyncStream}")
+
+                return cast(
+                    _T,
+                    to(
+                        cast_to=extract_stream_chunk_type(
+                            to,
+                            failure_message="Expected custom stream type to be passed with a type argument, e.g. Stream[ChunkType]",
+                        ),
+                        response=self.http_response,
+                        client=cast(Any, self._client),
+                        options=self._options,
+                    ),
+                )
+
+            if self._stream_cls:
+                return cast(
+                    R,
+                    self._stream_cls(
+                        cast_to=extract_stream_chunk_type(self._stream_cls),
+                        response=self.http_response,
+                        client=cast(Any, self._client),
+                        options=self._options,
+                    ),
+                )
+
+            stream_cls = cast("type[Stream[Any]] | type[AsyncStream[Any]] | None", self._client._default_stream_cls)
+            if stream_cls is None:
+                raise MissingStreamClassError()
+
+            return cast(
+                R,
+                stream_cls(
+                    cast_to=cast_to,
+                    response=self.http_response,
+                    client=cast(Any, self._client),
+                    options=self._options,
+                ),
+            )
+
+        if cast_to is NoneType:
+            return cast(R, None)
+
+        response = self.http_response
+        if cast_to == str:
+            return cast(R, response.text)
+
+        if cast_to == bytes:
+            return cast(R, response.content)
+
+        if cast_to == int:
+            return cast(R, int(response.text))
+
+        if cast_to == float:
+            return cast(R, float(response.text))
+
+        if cast_to == bool:
+            return cast(R, response.text.lower() == "true")
+
+        # handle the legacy binary response case
+        if inspect.isclass(cast_to) and cast_to.__name__ == "HttpxBinaryResponseContent":
+            return cast(R, cast_to(response))  # type: ignore
+
+        if origin == APIResponse:
+            raise RuntimeError("Unexpected state - cast_to is `APIResponse`")
+
+        if inspect.isclass(origin) and issubclass(origin, httpx.Response):
+            # Because of the invariance of our ResponseT TypeVar, users can subclass httpx.Response
+            # and pass that class to our request functions. We cannot change the variance to be either
+            # covariant or contravariant as that makes our usage of ResponseT illegal. We could construct
+            # the response class ourselves but that is something that should be supported directly in httpx
+            # as it would be easy to incorrectly construct the Response object due to the multitude of arguments.
+            if cast_to != httpx.Response:
+                raise ValueError(f"Subclasses of httpx.Response cannot be passed to `cast_to`")
+            return cast(R, response)
+
+        if (
+            inspect.isclass(
+                origin  # pyright: ignore[reportUnknownArgumentType]
+            )
+            and not issubclass(origin, BaseModel)
+            and issubclass(origin, pydantic.BaseModel)
+        ):
+            raise TypeError("Pydantic models must subclass our base model type, e.g. `from openai import BaseModel`")
+
+        if (
+            cast_to is not object
+            and not origin is list
+            and not origin is dict
+            and not origin is Union
+            and not issubclass(origin, BaseModel)
+        ):
+            raise RuntimeError(
+                f"Unsupported type, expected {cast_to} to be a subclass of {BaseModel}, {dict}, {list}, {Union}, {NoneType}, {str} or {httpx.Response}."
+            )
+
+        # split is required to handle cases where additional information is included
+        # in the response, e.g. application/json; charset=utf-8
+        content_type, *_ = response.headers.get("content-type", "*").split(";")
+        if not content_type.endswith("json"):
+            if is_basemodel(cast_to):
+                try:
+                    data = response.json()
+                except Exception as exc:
+                    log.debug("Could not read JSON from response data due to %s - %s", type(exc), exc)
+                else:
+                    return self._client._process_response_data(
+                        data=data,
+                        cast_to=cast_to,  # type: ignore
+                        response=response,
+                    )
+
+            if self._client._strict_response_validation:
+                raise APIResponseValidationError(
+                    response=response,
+                    message=f"Expected Content-Type response header to be `application/json` but received `{content_type}` instead.",
+                    body=response.text,
+                )
+
+            # If the API responds with content that isn't JSON then we just return
+            # the (decoded) text without performing any parsing so that you can still
+            # handle the response however you need to.
+            return response.text  # type: ignore
+
+        data = response.json()
+
+        return self._client._process_response_data(
+            data=data,
+            cast_to=cast_to,  # type: ignore
+            response=response,
+        )
+
+
+class APIResponse(BaseAPIResponse[R]):
+    @property
+    def request_id(self) -> str | None:
+        return self.http_response.headers.get("x-request-id")  # type: ignore[no-any-return]
+
+    @overload
+    def parse(self, *, to: type[_T]) -> _T: ...
+
+    @overload
+    def parse(self) -> R: ...
+
+    def parse(self, *, to: type[_T] | None = None) -> R | _T:
+        """Returns the rich python representation of this response's data.
+
+        For lower-level control, see `.read()`, `.json()`, `.iter_bytes()`.
+
+        You can customise the type that the response is parsed into through
+        the `to` argument, e.g.
+
+        ```py
+        from openai import BaseModel
+
+
+        class MyModel(BaseModel):
+            foo: str
+
+
+        obj = response.parse(to=MyModel)
+        print(obj.foo)
+        ```
+
+        We support parsing:
+          - `BaseModel`
+          - `dict`
+          - `list`
+          - `Union`
+          - `str`
+          - `int`
+          - `float`
+          - `httpx.Response`
+        """
+        cache_key = to if to is not None else self._cast_to
+        cached = self._parsed_by_type.get(cache_key)
+        if cached is not None:
+            return cached  # type: ignore[no-any-return]
+
+        if not self._is_sse_stream:
+            self.read()
+
+        parsed = self._parse(to=to)
+        if is_given(self._options.post_parser):
+            parsed = self._options.post_parser(parsed)
+
+        if isinstance(parsed, BaseModel):
+            add_request_id(parsed, self.request_id)
+
+        self._parsed_by_type[cache_key] = parsed
+        return cast(R, parsed)
+
+    def read(self) -> bytes:
+        """Read and return the binary response content."""
+        try:
+            return self.http_response.read()
+        except httpx.StreamConsumed as exc:
+            # The default error raised by httpx isn't very
+            # helpful in our case so we re-raise it with
+            # a different error message.
+            raise StreamAlreadyConsumed() from exc
+
+    def text(self) -> str:
+        """Read and decode the response content into a string."""
+        self.read()
+        return self.http_response.text
+
+    def json(self) -> object:
+        """Read and decode the JSON response content."""
+        self.read()
+        return self.http_response.json()
+
+    def close(self) -> None:
+        """Close the response and release the connection.
+
+        Automatically called if the response body is read to completion.
+        """
+        self.http_response.close()
+
+    def iter_bytes(self, chunk_size: int | None = None) -> Iterator[bytes]:
+        """
+        A byte-iterator over the decoded response content.
+
+        This automatically handles gzip, deflate and brotli encoded responses.
+        """
+        for chunk in self.http_response.iter_bytes(chunk_size):
+            yield chunk
+
+    def iter_text(self, chunk_size: int | None = None) -> Iterator[str]:
+        """A str-iterator over the decoded response content
+        that handles both gzip, deflate, etc but also detects the content's
+        string encoding.
+        """
+        for chunk in self.http_response.iter_text(chunk_size):
+            yield chunk
+
+    def iter_lines(self) -> Iterator[str]:
+        """Like `iter_text()` but will only yield chunks for each line"""
+        for chunk in self.http_response.iter_lines():
+            yield chunk
+
+
+class AsyncAPIResponse(BaseAPIResponse[R]):
+    @property
+    def request_id(self) -> str | None:
+        return self.http_response.headers.get("x-request-id")  # type: ignore[no-any-return]
+
+    @overload
+    async def parse(self, *, to: type[_T]) -> _T: ...
+
+    @overload
+    async def parse(self) -> R: ...
+
+    async def parse(self, *, to: type[_T] | None = None) -> R | _T:
+        """Returns the rich python representation of this response's data.
+
+        For lower-level control, see `.read()`, `.json()`, `.iter_bytes()`.
+
+        You can customise the type that the response is parsed into through
+        the `to` argument, e.g.
+
+        ```py
+        from openai import BaseModel
+
+
+        class MyModel(BaseModel):
+            foo: str
+
+
+        obj = response.parse(to=MyModel)
+        print(obj.foo)
+        ```
+
+        We support parsing:
+          - `BaseModel`
+          - `dict`
+          - `list`
+          - `Union`
+          - `str`
+          - `httpx.Response`
+        """
+        cache_key = to if to is not None else self._cast_to
+        cached = self._parsed_by_type.get(cache_key)
+        if cached is not None:
+            return cached  # type: ignore[no-any-return]
+
+        if not self._is_sse_stream:
+            await self.read()
+
+        parsed = self._parse(to=to)
+        if is_given(self._options.post_parser):
+            parsed = self._options.post_parser(parsed)
+
+        if isinstance(parsed, BaseModel):
+            add_request_id(parsed, self.request_id)
+
+        self._parsed_by_type[cache_key] = parsed
+        return cast(R, parsed)
+
+    async def read(self) -> bytes:
+        """Read and return the binary response content."""
+        try:
+            return await self.http_response.aread()
+        except httpx.StreamConsumed as exc:
+            # the default error raised by httpx isn't very
+            # helpful in our case so we re-raise it with
+            # a different error message
+            raise StreamAlreadyConsumed() from exc
+
+    async def text(self) -> str:
+        """Read and decode the response content into a string."""
+        await self.read()
+        return self.http_response.text
+
+    async def json(self) -> object:
+        """Read and decode the JSON response content."""
+        await self.read()
+        return self.http_response.json()
+
+    async def close(self) -> None:
+        """Close the response and release the connection.
+
+        Automatically called if the response body is read to completion.
+        """
+        await self.http_response.aclose()
+
+    async def iter_bytes(self, chunk_size: int | None = None) -> AsyncIterator[bytes]:
+        """
+        A byte-iterator over the decoded response content.
+
+        This automatically handles gzip, deflate and brotli encoded responses.
+        """
+        async for chunk in self.http_response.aiter_bytes(chunk_size):
+            yield chunk
+
+    async def iter_text(self, chunk_size: int | None = None) -> AsyncIterator[str]:
+        """A str-iterator over the decoded response content
+        that handles both gzip, deflate, etc but also detects the content's
+        string encoding.
+        """
+        async for chunk in self.http_response.aiter_text(chunk_size):
+            yield chunk
+
+    async def iter_lines(self) -> AsyncIterator[str]:
+        """Like `iter_text()` but will only yield chunks for each line"""
+        async for chunk in self.http_response.aiter_lines():
+            yield chunk
+
+
+class BinaryAPIResponse(APIResponse[bytes]):
+    """Subclass of APIResponse providing helpers for dealing with binary data.
+
+    Note: If you want to stream the response data instead of eagerly reading it
+    all at once then you should use `.with_streaming_response` when making
+    the API request, e.g. `.with_streaming_response.get_binary_response()`
+    """
+
+    def write_to_file(
+        self,
+        file: str | os.PathLike[str],
+    ) -> None:
+        """Write the output to the given file.
+
+        Accepts a filename or any path-like object, e.g. pathlib.Path
+
+        Note: if you want to stream the data to the file instead of writing
+        all at once then you should use `.with_streaming_response` when making
+        the API request, e.g. `.with_streaming_response.get_binary_response()`
+        """
+        with open(file, mode="wb") as f:
+            for data in self.iter_bytes():
+                f.write(data)
+
+
+class AsyncBinaryAPIResponse(AsyncAPIResponse[bytes]):
+    """Subclass of APIResponse providing helpers for dealing with binary data.
+
+    Note: If you want to stream the response data instead of eagerly reading it
+    all at once then you should use `.with_streaming_response` when making
+    the API request, e.g. `.with_streaming_response.get_binary_response()`
+    """
+
+    async def write_to_file(
+        self,
+        file: str | os.PathLike[str],
+    ) -> None:
+        """Write the output to the given file.
+
+        Accepts a filename or any path-like object, e.g. pathlib.Path
+
+        Note: if you want to stream the data to the file instead of writing
+        all at once then you should use `.with_streaming_response` when making
+        the API request, e.g. `.with_streaming_response.get_binary_response()`
+        """
+        path = anyio.Path(file)
+        async with await path.open(mode="wb") as f:
+            async for data in self.iter_bytes():
+                await f.write(data)
+
+
+class StreamedBinaryAPIResponse(APIResponse[bytes]):
+    def stream_to_file(
+        self,
+        file: str | os.PathLike[str],
+        *,
+        chunk_size: int | None = None,
+    ) -> None:
+        """Streams the output to the given file.
+
+        Accepts a filename or any path-like object, e.g. pathlib.Path
+        """
+        with open(file, mode="wb") as f:
+            for data in self.iter_bytes(chunk_size):
+                f.write(data)
+
+
+class AsyncStreamedBinaryAPIResponse(AsyncAPIResponse[bytes]):
+    async def stream_to_file(
+        self,
+        file: str | os.PathLike[str],
+        *,
+        chunk_size: int | None = None,
+    ) -> None:
+        """Streams the output to the given file.
+
+        Accepts a filename or any path-like object, e.g. pathlib.Path
+        """
+        path = anyio.Path(file)
+        async with await path.open(mode="wb") as f:
+            async for data in self.iter_bytes(chunk_size):
+                await f.write(data)
+
+
+class MissingStreamClassError(TypeError):
+    def __init__(self) -> None:
+        super().__init__(
+            "The `stream` argument was set to `True` but the `stream_cls` argument was not given. See `openai._streaming` for reference",
+        )
+
+
+class StreamAlreadyConsumed(OpenAIError):
+    """
+    Attempted to read or stream content, but the content has already
+    been streamed.
+
+    This can happen if you use a method like `.iter_lines()` and then attempt
+    to read th entire response body afterwards, e.g.
+
+    ```py
+    response = await client.post(...)
+    async for line in response.iter_lines():
+        ...  # do something with `line`
+
+    content = await response.read()
+    # ^ error
+    ```
+
+    If you want this behaviour you'll need to either manually accumulate the response
+    content or call `await response.read()` before iterating over the stream.
+    """
+
+    def __init__(self) -> None:
+        message = (
+            "Attempted to read or stream some content, but the content has "
+            "already been streamed. "
+            "This could be due to attempting to stream the response "
+            "content more than once."
+            "\n\n"
+            "You can fix this by manually accumulating the response content while streaming "
+            "or by calling `.read()` before starting to stream."
+        )
+        super().__init__(message)
+
+
+class ResponseContextManager(Generic[_APIResponseT]):
+    """Context manager for ensuring that a request is not made
+    until it is entered and that the response will always be closed
+    when the context manager exits
+    """
+
+    def __init__(self, request_func: Callable[[], _APIResponseT]) -> None:
+        self._request_func = request_func
+        self.__response: _APIResponseT | None = None
+
+    def __enter__(self) -> _APIResponseT:
+        self.__response = self._request_func()
+        return self.__response
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        exc_tb: TracebackType | None,
+    ) -> None:
+        if self.__response is not None:
+            self.__response.close()
+
+
+class AsyncResponseContextManager(Generic[_AsyncAPIResponseT]):
+    """Context manager for ensuring that a request is not made
+    until it is entered and that the response will always be closed
+    when the context manager exits
+    """
+
+    def __init__(self, api_request: Awaitable[_AsyncAPIResponseT]) -> None:
+        self._api_request = api_request
+        self.__response: _AsyncAPIResponseT | None = None
+
+    async def __aenter__(self) -> _AsyncAPIResponseT:
+        self.__response = await self._api_request
+        return self.__response
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        exc_tb: TracebackType | None,
+    ) -> None:
+        if self.__response is not None:
+            await self.__response.close()
+
+
+def to_streamed_response_wrapper(func: Callable[P, R]) -> Callable[P, ResponseContextManager[APIResponse[R]]]:
+    """Higher order function that takes one of our bound API methods and wraps it
+    to support streaming and returning the raw `APIResponse` object directly.
+    """
+
+    @functools.wraps(func)
+    def wrapped(*args: P.args, **kwargs: P.kwargs) -> ResponseContextManager[APIResponse[R]]:
+        extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})}
+        extra_headers[RAW_RESPONSE_HEADER] = "stream"
+
+        kwargs["extra_headers"] = extra_headers
+
+        make_request = functools.partial(func, *args, **kwargs)
+
+        return ResponseContextManager(cast(Callable[[], APIResponse[R]], make_request))
+
+    return wrapped
+
+
+def async_to_streamed_response_wrapper(
+    func: Callable[P, Awaitable[R]],
+) -> Callable[P, AsyncResponseContextManager[AsyncAPIResponse[R]]]:
+    """Higher order function that takes one of our bound API methods and wraps it
+    to support streaming and returning the raw `APIResponse` object directly.
+    """
+
+    @functools.wraps(func)
+    def wrapped(*args: P.args, **kwargs: P.kwargs) -> AsyncResponseContextManager[AsyncAPIResponse[R]]:
+        extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})}
+        extra_headers[RAW_RESPONSE_HEADER] = "stream"
+
+        kwargs["extra_headers"] = extra_headers
+
+        make_request = func(*args, **kwargs)
+
+        return AsyncResponseContextManager(cast(Awaitable[AsyncAPIResponse[R]], make_request))
+
+    return wrapped
+
+
+def to_custom_streamed_response_wrapper(
+    func: Callable[P, object],
+    response_cls: type[_APIResponseT],
+) -> Callable[P, ResponseContextManager[_APIResponseT]]:
+    """Higher order function that takes one of our bound API methods and an `APIResponse` class
+    and wraps the method to support streaming and returning the given response class directly.
+
+    Note: the given `response_cls` *must* be concrete, e.g. `class BinaryAPIResponse(APIResponse[bytes])`
+    """
+
+    @functools.wraps(func)
+    def wrapped(*args: P.args, **kwargs: P.kwargs) -> ResponseContextManager[_APIResponseT]:
+        extra_headers: dict[str, Any] = {**(cast(Any, kwargs.get("extra_headers")) or {})}
+        extra_headers[RAW_RESPONSE_HEADER] = "stream"
+        extra_headers[OVERRIDE_CAST_TO_HEADER] = response_cls
+
+        kwargs["extra_headers"] = extra_headers
+
+        make_request = functools.partial(func, *args, **kwargs)
+
+        return ResponseContextManager(cast(Callable[[], _APIResponseT], make_request))
+
+    return wrapped
+
+
+def async_to_custom_streamed_response_wrapper(
+    func: Callable[P, Awaitable[object]],
+    response_cls: type[_AsyncAPIResponseT],
+) -> Callable[P, AsyncResponseContextManager[_AsyncAPIResponseT]]:
+    """Higher order function that takes one of our bound API methods and an `APIResponse` class
+    and wraps the method to support streaming and returning the given response class directly.
+
+    Note: the given `response_cls` *must* be concrete, e.g. `class BinaryAPIResponse(APIResponse[bytes])`
+    """
+
+    @functools.wraps(func)
+    def wrapped(*args: P.args, **kwargs: P.kwargs) -> AsyncResponseContextManager[_AsyncAPIResponseT]:
+        extra_headers: dict[str, Any] = {**(cast(Any, kwargs.get("extra_headers")) or {})}
+        extra_headers[RAW_RESPONSE_HEADER] = "stream"
+        extra_headers[OVERRIDE_CAST_TO_HEADER] = response_cls
+
+        kwargs["extra_headers"] = extra_headers
+
+        make_request = func(*args, **kwargs)
+
+        return AsyncResponseContextManager(cast(Awaitable[_AsyncAPIResponseT], make_request))
+
+    return wrapped
+
+
+def to_raw_response_wrapper(func: Callable[P, R]) -> Callable[P, APIResponse[R]]:
+    """Higher order function that takes one of our bound API methods and wraps it
+    to support returning the raw `APIResponse` object directly.
+    """
+
+    @functools.wraps(func)
+    def wrapped(*args: P.args, **kwargs: P.kwargs) -> APIResponse[R]:
+        extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})}
+        extra_headers[RAW_RESPONSE_HEADER] = "raw"
+
+        kwargs["extra_headers"] = extra_headers
+
+        return cast(APIResponse[R], func(*args, **kwargs))
+
+    return wrapped
+
+
+def async_to_raw_response_wrapper(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[AsyncAPIResponse[R]]]:
+    """Higher order function that takes one of our bound API methods and wraps it
+    to support returning the raw `APIResponse` object directly.
+    """
+
+    @functools.wraps(func)
+    async def wrapped(*args: P.args, **kwargs: P.kwargs) -> AsyncAPIResponse[R]:
+        extra_headers: dict[str, str] = {**(cast(Any, kwargs.get("extra_headers")) or {})}
+        extra_headers[RAW_RESPONSE_HEADER] = "raw"
+
+        kwargs["extra_headers"] = extra_headers
+
+        return cast(AsyncAPIResponse[R], await func(*args, **kwargs))
+
+    return wrapped
+
+
+def to_custom_raw_response_wrapper(
+    func: Callable[P, object],
+    response_cls: type[_APIResponseT],
+) -> Callable[P, _APIResponseT]:
+    """Higher order function that takes one of our bound API methods and an `APIResponse` class
+    and wraps the method to support returning the given response class directly.
+
+    Note: the given `response_cls` *must* be concrete, e.g. `class BinaryAPIResponse(APIResponse[bytes])`
+    """
+
+    @functools.wraps(func)
+    def wrapped(*args: P.args, **kwargs: P.kwargs) -> _APIResponseT:
+        extra_headers: dict[str, Any] = {**(cast(Any, kwargs.get("extra_headers")) or {})}
+        extra_headers[RAW_RESPONSE_HEADER] = "raw"
+        extra_headers[OVERRIDE_CAST_TO_HEADER] = response_cls
+
+        kwargs["extra_headers"] = extra_headers
+
+        return cast(_APIResponseT, func(*args, **kwargs))
+
+    return wrapped
+
+
+def async_to_custom_raw_response_wrapper(
+    func: Callable[P, Awaitable[object]],
+    response_cls: type[_AsyncAPIResponseT],
+) -> Callable[P, Awaitable[_AsyncAPIResponseT]]:
+    """Higher order function that takes one of our bound API methods and an `APIResponse` class
+    and wraps the method to support returning the given response class directly.
+
+    Note: the given `response_cls` *must* be concrete, e.g. `class BinaryAPIResponse(APIResponse[bytes])`
+    """
+
+    @functools.wraps(func)
+    def wrapped(*args: P.args, **kwargs: P.kwargs) -> Awaitable[_AsyncAPIResponseT]:
+        extra_headers: dict[str, Any] = {**(cast(Any, kwargs.get("extra_headers")) or {})}
+        extra_headers[RAW_RESPONSE_HEADER] = "raw"
+        extra_headers[OVERRIDE_CAST_TO_HEADER] = response_cls
+
+        kwargs["extra_headers"] = extra_headers
+
+        return cast(Awaitable[_AsyncAPIResponseT], func(*args, **kwargs))
+
+    return wrapped
+
+
+def extract_response_type(typ: type[BaseAPIResponse[Any]]) -> type:
+    """Given a type like `APIResponse[T]`, returns the generic type variable `T`.
+
+    This also handles the case where a concrete subclass is given, e.g.
+    ```py
+    class MyResponse(APIResponse[bytes]):
+        ...
+
+    extract_response_type(MyResponse) -> bytes
+    ```
+    """
+    return extract_type_var_from_base(
+        typ,
+        generic_bases=cast("tuple[type, ...]", (BaseAPIResponse, APIResponse, AsyncAPIResponse)),
+        index=0,
+    )

venv/lib/python3.12/site-packages/openai/_send_queue.py

@@ -0,0 +1,90 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+import typing
+import threading
+
+from ._exceptions import WebSocketQueueFullError
+
+
+class SendQueue:
+    """Bounded byte-size queue for outgoing WebSocket messages.
+
+    Messages are stored as pre-serialized strings. The queue enforces a
+    maximum byte budget so that unbounded buffering cannot occur during
+    reconnection windows.
+    """
+
+    def __init__(self, max_bytes: int = 1_048_576) -> None:
+        self._queue: list[tuple[str, int]] = []  # (data, byte_length)
+        self._bytes: int = 0
+        self._max_bytes = max_bytes
+        self._lock = threading.Lock()
+
+    def enqueue(self, data: str) -> None:
+        """Append *data* to the queue.
+
+        Raises :class:`WebSocketQueueFullError` if the message would
+        exceed the byte-size limit.
+        """
+        byte_length = len(data.encode("utf-8"))
+        with self._lock:
+            if self._bytes + byte_length > self._max_bytes:
+                raise WebSocketQueueFullError("send queue is full, message discarded")
+            self._queue.append((data, byte_length))
+            self._bytes += byte_length
+
+    def flush_sync(self, send: typing.Callable[[str], object]) -> None:
+        """Send every queued message via *send*.
+
+        If *send* raises, the failing message and all subsequent messages
+        are re-queued and the error is re-raised.
+        """
+        with self._lock:
+            pending = list(self._queue)
+            self._queue.clear()
+            self._bytes = 0
+
+        for i, (data, _byte_length) in enumerate(pending):
+            try:
+                send(data)
+            except Exception:
+                with self._lock:
+                    remaining = pending[i:]
+                    self._queue = remaining + self._queue
+                    self._bytes = sum(bl for _, bl in self._queue)
+                raise
+
+    async def flush_async(self, send: typing.Callable[[str], typing.Awaitable[object]]) -> None:
+        """Async variant of :meth:`flush_sync`."""
+        with self._lock:
+            pending = list(self._queue)
+            self._queue.clear()
+            self._bytes = 0
+
+        for i, (data, _byte_length) in enumerate(pending):
+            try:
+                await send(data)
+            except Exception:
+                with self._lock:
+                    remaining = pending[i:]
+                    self._queue = remaining + self._queue
+                    self._bytes = sum(bl for _, bl in self._queue)
+                raise
+
+    def drain(self) -> list[str]:
+        """Remove and return all queued messages."""
+        with self._lock:
+            items = [data for data, _ in self._queue]
+            self._queue.clear()
+            self._bytes = 0
+            return items
+
+    def __len__(self) -> int:
+        with self._lock:
+            return len(self._queue)
+
+    def __bool__(self) -> bool:
+        with self._lock:
+            return len(self._queue) > 0

venv/lib/python3.12/site-packages/openai/_streaming.py

@@ -0,0 +1,427 @@
+# Note: initially copied from https://github.com/florimondmanca/httpx-sse/blob/master/src/httpx_sse/_decoders.py
+from __future__ import annotations
+
+import json
+import inspect
+from types import TracebackType
+from typing import TYPE_CHECKING, Any, Generic, TypeVar, Iterator, Optional, AsyncIterator, cast
+from typing_extensions import Self, Protocol, TypeGuard, override, get_origin, runtime_checkable
+
+import httpx
+
+from ._utils import is_mapping, extract_type_var_from_base
+from ._exceptions import APIError
+
+if TYPE_CHECKING:
+    from ._client import OpenAI, AsyncOpenAI
+    from ._models import FinalRequestOptions
+
+
+_T = TypeVar("_T")
+
+
+class Stream(Generic[_T]):
+    """Provides the core interface to iterate over a synchronous stream response."""
+
+    response: httpx.Response
+    _options: Optional[FinalRequestOptions] = None
+    _decoder: SSEBytesDecoder
+
+    def __init__(
+        self,
+        *,
+        cast_to: type[_T],
+        response: httpx.Response,
+        client: OpenAI,
+        options: Optional[FinalRequestOptions] = None,
+    ) -> None:
+        self.response = response
+        self._cast_to = cast_to
+        self._client = client
+        self._options = options
+        self._decoder = client._make_sse_decoder()
+        self._iterator = self.__stream__()
+
+    def __next__(self) -> _T:
+        return self._iterator.__next__()
+
+    def __iter__(self) -> Iterator[_T]:
+        for item in self._iterator:
+            yield item
+
+    def _iter_events(self) -> Iterator[ServerSentEvent]:
+        yield from self._decoder.iter_bytes(self.response.iter_bytes())
+
+    def __stream__(self) -> Iterator[_T]:
+        cast_to = cast(Any, self._cast_to)
+        response = self.response
+        process_data = self._client._process_response_data
+        iterator = self._iter_events()
+
+        try:
+            for sse in iterator:
+                if sse.data.startswith("[DONE]"):
+                    break
+
+                # we have to special case the Assistants `thread.` events since we won't have an "event" key in the data
+                if sse.event and sse.event.startswith("thread."):
+                    data = sse.json()
+
+                    if sse.event == "error" and is_mapping(data) and data.get("error"):
+                        message = None
+                        error = data.get("error")
+                        if is_mapping(error):
+                            message = error.get("message")
+                        if not message or not isinstance(message, str):
+                            message = "An error occurred during streaming"
+
+                        raise APIError(
+                            message=message,
+                            request=self.response.request,
+                            body=data["error"],
+                        )
+
+                    yield process_data(data={"data": data, "event": sse.event}, cast_to=cast_to, response=response)
+                else:
+                    data = sse.json()
+                    if is_mapping(data) and data.get("error"):
+                        message = None
+                        error = data.get("error")
+                        if is_mapping(error):
+                            message = error.get("message")
+                        if not message or not isinstance(message, str):
+                            message = "An error occurred during streaming"
+
+                        raise APIError(
+                            message=message,
+                            request=self.response.request,
+                            body=data["error"],
+                        )
+
+                    yield process_data(
+                        data={"data": data, "event": sse.event}
+                        if self._options is not None and self._options.synthesize_event_and_data
+                        else data,
+                        cast_to=cast_to,
+                        response=response,
+                    )
+        finally:
+            # Ensure the response is closed even if the consumer doesn't read all data
+            response.close()
+
+    def __enter__(self) -> Self:
+        return self
+
+    def __exit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        exc_tb: TracebackType | None,
+    ) -> None:
+        self.close()
+
+    def close(self) -> None:
+        """
+        Close the response and release the connection.
+
+        Automatically called if the response body is read to completion.
+        """
+        self.response.close()
+
+
+class AsyncStream(Generic[_T]):
+    """Provides the core interface to iterate over an asynchronous stream response."""
+
+    response: httpx.Response
+    _options: Optional[FinalRequestOptions] = None
+    _decoder: SSEDecoder | SSEBytesDecoder
+
+    def __init__(
+        self,
+        *,
+        cast_to: type[_T],
+        response: httpx.Response,
+        client: AsyncOpenAI,
+        options: Optional[FinalRequestOptions] = None,
+    ) -> None:
+        self.response = response
+        self._cast_to = cast_to
+        self._client = client
+        self._options = options
+        self._decoder = client._make_sse_decoder()
+        self._iterator = self.__stream__()
+
+    async def __anext__(self) -> _T:
+        return await self._iterator.__anext__()
+
+    async def __aiter__(self) -> AsyncIterator[_T]:
+        async for item in self._iterator:
+            yield item
+
+    async def _iter_events(self) -> AsyncIterator[ServerSentEvent]:
+        async for sse in self._decoder.aiter_bytes(self.response.aiter_bytes()):
+            yield sse
+
+    async def __stream__(self) -> AsyncIterator[_T]:
+        cast_to = cast(Any, self._cast_to)
+        response = self.response
+        process_data = self._client._process_response_data
+        iterator = self._iter_events()
+
+        try:
+            async for sse in iterator:
+                if sse.data.startswith("[DONE]"):
+                    break
+
+                # we have to special case the Assistants `thread.` events since we won't have an "event" key in the data
+                if sse.event and sse.event.startswith("thread."):
+                    data = sse.json()
+
+                    if sse.event == "error" and is_mapping(data) and data.get("error"):
+                        message = None
+                        error = data.get("error")
+                        if is_mapping(error):
+                            message = error.get("message")
+                        if not message or not isinstance(message, str):
+                            message = "An error occurred during streaming"
+
+                        raise APIError(
+                            message=message,
+                            request=self.response.request,
+                            body=data["error"],
+                        )
+
+                    yield process_data(data={"data": data, "event": sse.event}, cast_to=cast_to, response=response)
+                else:
+                    data = sse.json()
+                    if is_mapping(data) and data.get("error"):
+                        message = None
+                        error = data.get("error")
+                        if is_mapping(error):
+                            message = error.get("message")
+                        if not message or not isinstance(message, str):
+                            message = "An error occurred during streaming"
+
+                        raise APIError(
+                            message=message,
+                            request=self.response.request,
+                            body=data["error"],
+                        )
+
+                    yield process_data(
+                        data={"data": data, "event": sse.event}
+                        if self._options is not None and self._options.synthesize_event_and_data
+                        else data,
+                        cast_to=cast_to,
+                        response=response,
+                    )
+        finally:
+            # Ensure the response is closed even if the consumer doesn't read all data
+            await response.aclose()
+
+    async def __aenter__(self) -> Self:
+        return self
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc: BaseException | None,
+        exc_tb: TracebackType | None,
+    ) -> None:
+        await self.close()
+
+    async def close(self) -> None:
+        """
+        Close the response and release the connection.
+
+        Automatically called if the response body is read to completion.
+        """
+        await self.response.aclose()
+
+
+class ServerSentEvent:
+    def __init__(
+        self,
+        *,
+        event: str | None = None,
+        data: str | None = None,
+        id: str | None = None,
+        retry: int | None = None,
+    ) -> None:
+        if data is None:
+            data = ""
+
+        self._id = id
+        self._data = data
+        self._event = event or None
+        self._retry = retry
+
+    @property
+    def event(self) -> str | None:
+        return self._event
+
+    @property
+    def id(self) -> str | None:
+        return self._id
+
+    @property
+    def retry(self) -> int | None:
+        return self._retry
+
+    @property
+    def data(self) -> str:
+        return self._data
+
+    def json(self) -> Any:
+        return json.loads(self.data)
+
+    @override
+    def __repr__(self) -> str:
+        return f"ServerSentEvent(event={self.event}, data={self.data}, id={self.id}, retry={self.retry})"
+
+
+class SSEDecoder:
+    _data: list[str]
+    _event: str | None
+    _retry: int | None
+    _last_event_id: str | None
+
+    def __init__(self) -> None:
+        self._event = None
+        self._data = []
+        self._last_event_id = None
+        self._retry = None
+
+    def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[ServerSentEvent]:
+        """Given an iterator that yields raw binary data, iterate over it & yield every event encountered"""
+        for chunk in self._iter_chunks(iterator):
+            # Split before decoding so splitlines() only uses \r and \n
+            for raw_line in chunk.splitlines():
+                line = raw_line.decode("utf-8")
+                sse = self.decode(line)
+                if sse:
+                    yield sse
+
+    def _iter_chunks(self, iterator: Iterator[bytes]) -> Iterator[bytes]:
+        """Given an iterator that yields raw binary data, iterate over it and yield individual SSE chunks"""
+        data = b""
+        for chunk in iterator:
+            for line in chunk.splitlines(keepends=True):
+                data += line
+                if data.endswith((b"\r\r", b"\n\n", b"\r\n\r\n")):
+                    yield data
+                    data = b""
+        if data:
+            yield data
+
+    async def aiter_bytes(self, iterator: AsyncIterator[bytes]) -> AsyncIterator[ServerSentEvent]:
+        """Given an iterator that yields raw binary data, iterate over it & yield every event encountered"""
+        async for chunk in self._aiter_chunks(iterator):
+            # Split before decoding so splitlines() only uses \r and \n
+            for raw_line in chunk.splitlines():
+                line = raw_line.decode("utf-8")
+                sse = self.decode(line)
+                if sse:
+                    yield sse
+
+    async def _aiter_chunks(self, iterator: AsyncIterator[bytes]) -> AsyncIterator[bytes]:
+        """Given an iterator that yields raw binary data, iterate over it and yield individual SSE chunks"""
+        data = b""
+        async for chunk in iterator:
+            for line in chunk.splitlines(keepends=True):
+                data += line
+                if data.endswith((b"\r\r", b"\n\n", b"\r\n\r\n")):
+                    yield data
+                    data = b""
+        if data:
+            yield data
+
+    def decode(self, line: str) -> ServerSentEvent | None:
+        # See: https://html.spec.whatwg.org/multipage/server-sent-events.html#event-stream-interpretation  # noqa: E501
+
+        if not line:
+            if not self._event and not self._data and not self._last_event_id and self._retry is None:
+                return None
+
+            sse = ServerSentEvent(
+                event=self._event,
+                data="\n".join(self._data),
+                id=self._last_event_id,
+                retry=self._retry,
+            )
+
+            # NOTE: as per the SSE spec, do not reset last_event_id.
+            self._event = None
+            self._data = []
+            self._retry = None
+
+            return sse
+
+        if line.startswith(":"):
+            return None
+
+        fieldname, _, value = line.partition(":")
+
+        if value.startswith(" "):
+            value = value[1:]
+
+        if fieldname == "event":
+            self._event = value
+        elif fieldname == "data":
+            self._data.append(value)
+        elif fieldname == "id":
+            if "\0" in value:
+                pass
+            else:
+                self._last_event_id = value
+        elif fieldname == "retry":
+            try:
+                self._retry = int(value)
+            except (TypeError, ValueError):
+                pass
+        else:
+            pass  # Field is ignored.
+
+        return None
+
+
+@runtime_checkable
+class SSEBytesDecoder(Protocol):
+    def iter_bytes(self, iterator: Iterator[bytes]) -> Iterator[ServerSentEvent]:
+        """Given an iterator that yields raw binary data, iterate over it & yield every event encountered"""
+        ...
+
+    def aiter_bytes(self, iterator: AsyncIterator[bytes]) -> AsyncIterator[ServerSentEvent]:
+        """Given an async iterator that yields raw binary data, iterate over it & yield every event encountered"""
+        ...
+
+
+def is_stream_class_type(typ: type) -> TypeGuard[type[Stream[object]] | type[AsyncStream[object]]]:
+    """TypeGuard for determining whether or not the given type is a subclass of `Stream` / `AsyncStream`"""
+    origin = get_origin(typ) or typ
+    return inspect.isclass(origin) and issubclass(origin, (Stream, AsyncStream))
+
+
+def extract_stream_chunk_type(
+    stream_cls: type,
+    *,
+    failure_message: str | None = None,
+) -> type:
+    """Given a type like `Stream[T]`, returns the generic type variable `T`.
+
+    This also handles the case where a concrete subclass is given, e.g.
+    ```py
+    class MyStream(Stream[bytes]):
+        ...
+
+    extract_stream_chunk_type(MyStream) -> bytes
+    ```
+    """
+    from ._base_client import Stream, AsyncStream
+
+    return extract_type_var_from_base(
+        stream_cls,
+        index=0,
+        generic_bases=cast("tuple[type, ...]", (Stream, AsyncStream)),
+        failure_message=failure_message,
+    )

venv/lib/python3.12/site-packages/openai/_types.py

@@ -0,0 +1,279 @@
+from __future__ import annotations
+
+from os import PathLike
+from typing import (
+    IO,
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    List,
+    Type,
+    Tuple,
+    Union,
+    Mapping,
+    TypeVar,
+    Callable,
+    Iterable,
+    Iterator,
+    Optional,
+    Sequence,
+    AsyncIterable,
+)
+from typing_extensions import (
+    Set,
+    Literal,
+    Protocol,
+    TypeAlias,
+    TypedDict,
+    SupportsIndex,
+    overload,
+    override,
+    runtime_checkable,
+)
+
+import httpx
+import pydantic
+from httpx import URL, Proxy, Timeout, Response, BaseTransport, AsyncBaseTransport
+
+if TYPE_CHECKING:
+    from ._models import BaseModel, SecurityOptions
+    from ._response import APIResponse, AsyncAPIResponse
+    from ._legacy_response import HttpxBinaryResponseContent
+
+Transport = BaseTransport
+AsyncTransport = AsyncBaseTransport
+Query = Mapping[str, object]
+Body = object
+AnyMapping = Mapping[str, object]
+ModelT = TypeVar("ModelT", bound=pydantic.BaseModel)
+_T = TypeVar("_T")
+
+ArrayFormat = Literal["comma", "repeat", "indices", "brackets"]
+NestedFormat = Literal["dots", "brackets"]
+
+
+# Approximates httpx internal ProxiesTypes and RequestFiles types
+# while adding support for `PathLike` instances
+ProxiesDict = Dict["str | URL", Union[None, str, URL, Proxy]]
+ProxiesTypes = Union[str, Proxy, ProxiesDict]
+if TYPE_CHECKING:
+    Base64FileInput = Union[IO[bytes], PathLike[str]]
+    FileContent = Union[IO[bytes], bytes, PathLike[str]]
+else:
+    Base64FileInput = Union[IO[bytes], PathLike]
+    FileContent = Union[IO[bytes], bytes, PathLike]  # PathLike is not subscriptable in Python 3.8.
+
+
+# Used for sending raw binary data / streaming data in request bodies
+# e.g. for file uploads without multipart encoding
+BinaryTypes = Union[bytes, bytearray, IO[bytes], Iterable[bytes]]
+AsyncBinaryTypes = Union[bytes, bytearray, IO[bytes], AsyncIterable[bytes]]
+
+FileTypes = Union[
+    # file (or bytes)
+    FileContent,
+    # (filename, file (or bytes))
+    Tuple[Optional[str], FileContent],
+    # (filename, file (or bytes), content_type)
+    Tuple[Optional[str], FileContent, Optional[str]],
+    # (filename, file (or bytes), content_type, headers)
+    Tuple[Optional[str], FileContent, Optional[str], Mapping[str, str]],
+]
+RequestFiles = Union[Mapping[str, FileTypes], Sequence[Tuple[str, FileTypes]]]
+
+# duplicate of the above but without our custom file support
+HttpxFileContent = Union[IO[bytes], bytes]
+HttpxFileTypes = Union[
+    # file (or bytes)
+    HttpxFileContent,
+    # (filename, file (or bytes))
+    Tuple[Optional[str], HttpxFileContent],
+    # (filename, file (or bytes), content_type)
+    Tuple[Optional[str], HttpxFileContent, Optional[str]],
+    # (filename, file (or bytes), content_type, headers)
+    Tuple[Optional[str], HttpxFileContent, Optional[str], Mapping[str, str]],
+]
+HttpxRequestFiles = Union[Mapping[str, HttpxFileTypes], Sequence[Tuple[str, HttpxFileTypes]]]
+
+# Workaround to support (cast_to: Type[ResponseT]) -> ResponseT
+# where ResponseT includes `None`. In order to support directly
+# passing `None`, overloads would have to be defined for every
+# method that uses `ResponseT` which would lead to an unacceptable
+# amount of code duplication and make it unreadable. See _base_client.py
+# for example usage.
+#
+# This unfortunately means that you will either have
+# to import this type and pass it explicitly:
+#
+# from openai import NoneType
+# client.get('/foo', cast_to=NoneType)
+#
+# or build it yourself:
+#
+# client.get('/foo', cast_to=type(None))
+if TYPE_CHECKING:
+    NoneType: Type[None]
+else:
+    NoneType = type(None)
+
+
+class RequestOptions(TypedDict, total=False):
+    headers: Headers
+    max_retries: int
+    timeout: float | Timeout | None
+    params: Query
+    extra_json: AnyMapping
+    idempotency_key: str
+    follow_redirects: bool
+    security: SecurityOptions
+    synthesize_event_and_data: bool
+
+
+# Sentinel class used until PEP 0661 is accepted
+class NotGiven:
+    """
+    For parameters with a meaningful None value, we need to distinguish between
+    the user explicitly passing None, and the user not passing the parameter at
+    all.
+
+    User code shouldn't need to use not_given directly.
+
+    For example:
+
+    ```py
+    def create(timeout: Timeout | None | NotGiven = not_given): ...
+
+
+    create(timeout=1)  # 1s timeout
+    create(timeout=None)  # No timeout
+    create()  # Default timeout behavior
+    ```
+    """
+
+    def __bool__(self) -> Literal[False]:
+        return False
+
+    @override
+    def __repr__(self) -> str:
+        return "NOT_GIVEN"
+
+
+not_given = NotGiven()
+# for backwards compatibility:
+NOT_GIVEN = NotGiven()
+
+
+class Omit:
+    """
+    To explicitly omit something from being sent in a request, use `omit`.
+
+    ```py
+    # as the default `Content-Type` header is `application/json` that will be sent
+    client.post("/upload/files", files={"file": b"my raw file content"})
+
+    # you can't explicitly override the header as it has to be dynamically generated
+    # to look something like: 'multipart/form-data; boundary=0d8382fcf5f8c3be01ca2e11002d2983'
+    client.post(..., headers={"Content-Type": "multipart/form-data"})
+
+    # instead you can remove the default `application/json` header by passing omit
+    client.post(..., headers={"Content-Type": omit})
+    ```
+    """
+
+    def __bool__(self) -> Literal[False]:
+        return False
+
+
+omit = Omit()
+
+Omittable = Union[_T, Omit]
+
+
+@runtime_checkable
+class ModelBuilderProtocol(Protocol):
+    @classmethod
+    def build(
+        cls: type[_T],
+        *,
+        response: Response,
+        data: object,
+    ) -> _T: ...
+
+
+Headers = Mapping[str, Union[str, Omit]]
+
+
+class HeadersLikeProtocol(Protocol):
+    def get(self, __key: str) -> str | None: ...
+
+
+HeadersLike = Union[Headers, HeadersLikeProtocol]
+
+ResponseT = TypeVar(
+    "ResponseT",
+    bound=Union[
+        object,
+        str,
+        None,
+        "BaseModel",
+        List[Any],
+        Dict[str, Any],
+        Response,
+        ModelBuilderProtocol,
+        "APIResponse[Any]",
+        "AsyncAPIResponse[Any]",
+        "HttpxBinaryResponseContent",
+    ],
+)
+
+StrBytesIntFloat = Union[str, bytes, int, float]
+
+# Note: copied from Pydantic
+# https://github.com/pydantic/pydantic/blob/6f31f8f68ef011f84357330186f603ff295312fd/pydantic/main.py#L79
+IncEx: TypeAlias = Union[Set[int], Set[str], Mapping[int, Union["IncEx", bool]], Mapping[str, Union["IncEx", bool]]]
+
+PostParser = Callable[[Any], Any]
+
+
+@runtime_checkable
+class InheritsGeneric(Protocol):
+    """Represents a type that has inherited from `Generic`
+
+    The `__orig_bases__` property can be used to determine the resolved
+    type variable for a given base class.
+    """
+
+    __orig_bases__: tuple[_GenericAlias]
+
+
+class _GenericAlias(Protocol):
+    __origin__: type[object]
+
+
+class HttpxSendArgs(TypedDict, total=False):
+    auth: httpx.Auth
+    follow_redirects: bool
+
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+if TYPE_CHECKING:
+    # This works because str.__contains__ does not accept object (either in typeshed or at runtime)
+    # https://github.com/hauntsaninja/useful_types/blob/5e9710f3875107d068e7679fd7fec9cfab0eff3b/useful_types/__init__.py#L285
+    #
+    # Note: index() and count() methods are intentionally omitted to allow pyright to properly
+    # infer TypedDict types when dict literals are used in lists assigned to SequenceNotStr.
+    class SequenceNotStr(Protocol[_T_co]):
+        @overload
+        def __getitem__(self, index: SupportsIndex, /) -> _T_co: ...
+        @overload
+        def __getitem__(self, index: slice, /) -> Sequence[_T_co]: ...
+        def __contains__(self, value: object, /) -> bool: ...
+        def __len__(self) -> int: ...
+        def __iter__(self) -> Iterator[_T_co]: ...
+        def __reversed__(self) -> Iterator[_T_co]: ...
+else:
+    # just point this to a normal `Sequence` at runtime to avoid having to special case
+    # deserializing our custom sequence type
+    SequenceNotStr = Sequence

venv/lib/python3.12/site-packages/openai/_utils/__init__.py

@@ -0,0 +1,67 @@
+from ._logs import SensitiveHeadersFilter as SensitiveHeadersFilter
+from ._path import path_template as path_template
+from ._sync import asyncify as asyncify
+from ._proxy import LazyProxy as LazyProxy
+from ._utils import (
+    flatten as flatten,
+    is_dict as is_dict,
+    is_list as is_list,
+    is_given as is_given,
+    is_tuple as is_tuple,
+    json_safe as json_safe,
+    lru_cache as lru_cache,
+    is_mapping as is_mapping,
+    is_tuple_t as is_tuple_t,
+    is_iterable as is_iterable,
+    is_sequence as is_sequence,
+    coerce_float as coerce_float,
+    is_mapping_t as is_mapping_t,
+    removeprefix as removeprefix,
+    removesuffix as removesuffix,
+    extract_files as extract_files,
+    is_sequence_t as is_sequence_t,
+    required_args as required_args,
+    coerce_boolean as coerce_boolean,
+    coerce_integer as coerce_integer,
+    file_from_path as file_from_path,
+    is_azure_client as is_azure_client,
+    strip_not_given as strip_not_given,
+    get_async_library as get_async_library,
+    maybe_coerce_float as maybe_coerce_float,
+    get_required_header as get_required_header,
+    maybe_coerce_boolean as maybe_coerce_boolean,
+    maybe_coerce_integer as maybe_coerce_integer,
+    is_async_azure_client as is_async_azure_client,
+)
+from ._compat import (
+    get_args as get_args,
+    is_union as is_union,
+    get_origin as get_origin,
+    is_typeddict as is_typeddict,
+    is_literal_type as is_literal_type,
+)
+from ._typing import (
+    is_list_type as is_list_type,
+    is_union_type as is_union_type,
+    extract_type_arg as extract_type_arg,
+    is_iterable_type as is_iterable_type,
+    is_required_type as is_required_type,
+    is_sequence_type as is_sequence_type,
+    is_annotated_type as is_annotated_type,
+    is_type_alias_type as is_type_alias_type,
+    strip_annotated_type as strip_annotated_type,
+    extract_type_var_from_base as extract_type_var_from_base,
+)
+from ._streams import consume_sync_iterator as consume_sync_iterator, consume_async_iterator as consume_async_iterator
+from ._transform import (
+    PropertyInfo as PropertyInfo,
+    transform as transform,
+    async_transform as async_transform,
+    maybe_transform as maybe_transform,
+    async_maybe_transform as async_maybe_transform,
+)
+from ._reflection import (
+    function_has_argument as function_has_argument,
+    assert_signatures_in_sync as assert_signatures_in_sync,
+)
+from ._datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime

venv/lib/python3.12/site-packages/openai/_utils/_compat.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import sys
+import typing_extensions
+from typing import Any, Type, Union, Literal, Optional
+from datetime import date, datetime
+from typing_extensions import get_args as _get_args, get_origin as _get_origin
+
+from .._types import StrBytesIntFloat
+from ._datetime_parse import parse_date as _parse_date, parse_datetime as _parse_datetime
+
+_LITERAL_TYPES = {Literal, typing_extensions.Literal}
+
+
+def get_args(tp: type[Any]) -> tuple[Any, ...]:
+    return _get_args(tp)
+
+
+def get_origin(tp: type[Any]) -> type[Any] | None:
+    return _get_origin(tp)
+
+
+def is_union(tp: Optional[Type[Any]]) -> bool:
+    if sys.version_info < (3, 10):
+        return tp is Union  # type: ignore[comparison-overlap]
+    else:
+        import types
+
+        return tp is Union or tp is types.UnionType  # type: ignore[comparison-overlap]
+
+
+def is_typeddict(tp: Type[Any]) -> bool:
+    return typing_extensions.is_typeddict(tp)
+
+
+def is_literal_type(tp: Type[Any]) -> bool:
+    return get_origin(tp) in _LITERAL_TYPES
+
+
+def parse_date(value: Union[date, StrBytesIntFloat]) -> date:
+    return _parse_date(value)
+
+
+def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime:
+    return _parse_datetime(value)

venv/lib/python3.12/site-packages/openai/_utils/_datetime_parse.py

@@ -0,0 +1,136 @@
+"""
+This file contains code from https://github.com/pydantic/pydantic/blob/main/pydantic/v1/datetime_parse.py
+without the Pydantic v1 specific errors.
+"""
+
+from __future__ import annotations
+
+import re
+from typing import Dict, Union, Optional
+from datetime import date, datetime, timezone, timedelta
+
+from .._types import StrBytesIntFloat
+
+date_expr = r"(?P<year>\d{4})-(?P<month>\d{1,2})-(?P<day>\d{1,2})"
+time_expr = (
+    r"(?P<hour>\d{1,2}):(?P<minute>\d{1,2})"
+    r"(?::(?P<second>\d{1,2})(?:\.(?P<microsecond>\d{1,6})\d{0,6})?)?"
+    r"(?P<tzinfo>Z|[+-]\d{2}(?::?\d{2})?)?$"
+)
+
+date_re = re.compile(f"{date_expr}$")
+datetime_re = re.compile(f"{date_expr}[T ]{time_expr}")
+
+
+EPOCH = datetime(1970, 1, 1)
+# if greater than this, the number is in ms, if less than or equal it's in seconds
+# (in seconds this is 11th October 2603, in ms it's 20th August 1970)
+MS_WATERSHED = int(2e10)
+# slightly more than datetime.max in ns - (datetime.max - EPOCH).total_seconds() * 1e9
+MAX_NUMBER = int(3e20)
+
+
+def _get_numeric(value: StrBytesIntFloat, native_expected_type: str) -> Union[None, int, float]:
+    if isinstance(value, (int, float)):
+        return value
+    try:
+        return float(value)
+    except ValueError:
+        return None
+    except TypeError:
+        raise TypeError(f"invalid type; expected {native_expected_type}, string, bytes, int or float") from None
+
+
+def _from_unix_seconds(seconds: Union[int, float]) -> datetime:
+    if seconds > MAX_NUMBER:
+        return datetime.max
+    elif seconds < -MAX_NUMBER:
+        return datetime.min
+
+    while abs(seconds) > MS_WATERSHED:
+        seconds /= 1000
+    dt = EPOCH + timedelta(seconds=seconds)
+    return dt.replace(tzinfo=timezone.utc)
+
+
+def _parse_timezone(value: Optional[str]) -> Union[None, int, timezone]:
+    if value == "Z":
+        return timezone.utc
+    elif value is not None:
+        offset_mins = int(value[-2:]) if len(value) > 3 else 0
+        offset = 60 * int(value[1:3]) + offset_mins
+        if value[0] == "-":
+            offset = -offset
+        return timezone(timedelta(minutes=offset))
+    else:
+        return None
+
+
+def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime:
+    """
+    Parse a datetime/int/float/string and return a datetime.datetime.
+
+    This function supports time zone offsets. When the input contains one,
+    the output uses a timezone with a fixed offset from UTC.
+
+    Raise ValueError if the input is well formatted but not a valid datetime.
+    Raise ValueError if the input isn't well formatted.
+    """
+    if isinstance(value, datetime):
+        return value
+
+    number = _get_numeric(value, "datetime")
+    if number is not None:
+        return _from_unix_seconds(number)
+
+    if isinstance(value, bytes):
+        value = value.decode()
+
+    assert not isinstance(value, (float, int))
+
+    match = datetime_re.match(value)
+    if match is None:
+        raise ValueError("invalid datetime format")
+
+    kw = match.groupdict()
+    if kw["microsecond"]:
+        kw["microsecond"] = kw["microsecond"].ljust(6, "0")
+
+    tzinfo = _parse_timezone(kw.pop("tzinfo"))
+    kw_: Dict[str, Union[None, int, timezone]] = {k: int(v) for k, v in kw.items() if v is not None}
+    kw_["tzinfo"] = tzinfo
+
+    return datetime(**kw_)  # type: ignore
+
+
+def parse_date(value: Union[date, StrBytesIntFloat]) -> date:
+    """
+    Parse a date/int/float/string and return a datetime.date.
+
+    Raise ValueError if the input is well formatted but not a valid date.
+    Raise ValueError if the input isn't well formatted.
+    """
+    if isinstance(value, date):
+        if isinstance(value, datetime):
+            return value.date()
+        else:
+            return value
+
+    number = _get_numeric(value, "date")
+    if number is not None:
+        return _from_unix_seconds(number).date()
+
+    if isinstance(value, bytes):
+        value = value.decode()
+
+    assert not isinstance(value, (float, int))
+    match = date_re.match(value)
+    if match is None:
+        raise ValueError("invalid date format")
+
+    kw = {k: int(v) for k, v in match.groupdict().items()}
+
+    try:
+        return date(**kw)
+    except ValueError:
+        raise ValueError("invalid date format") from None

venv/lib/python3.12/site-packages/openai/_utils/_json.py

@@ -0,0 +1,35 @@
+import json
+from typing import Any
+from datetime import datetime
+from typing_extensions import override
+
+import pydantic
+
+from .._compat import model_dump
+
+
+def openapi_dumps(obj: Any) -> bytes:
+    """
+    Serialize an object to UTF-8 encoded JSON bytes.
+
+    Extends the standard json.dumps with support for additional types
+    commonly used in the SDK, such as `datetime`, `pydantic.BaseModel`, etc.
+    """
+    return json.dumps(
+        obj,
+        cls=_CustomEncoder,
+        # Uses the same defaults as httpx's JSON serialization
+        ensure_ascii=False,
+        separators=(",", ":"),
+        allow_nan=False,
+    ).encode()
+
+
+class _CustomEncoder(json.JSONEncoder):
+    @override
+    def default(self, o: Any) -> Any:
+        if isinstance(o, datetime):
+            return o.isoformat()
+        if isinstance(o, pydantic.BaseModel):
+            return model_dump(o, exclude_unset=True, mode="json", by_alias=True)
+        return super().default(o)

venv/lib/python3.12/site-packages/openai/_utils/_logs.py

@@ -0,0 +1,42 @@
+import os
+import logging
+from typing_extensions import override
+
+from ._utils import is_dict
+
+logger: logging.Logger = logging.getLogger("openai")
+httpx_logger: logging.Logger = logging.getLogger("httpx")
+
+
+SENSITIVE_HEADERS = {"api-key", "authorization", "x-amz-security-token"}
+
+
+def _basic_config() -> None:
+    # e.g. [2023-10-05 14:12:26 - openai._base_client:818 - DEBUG] HTTP Request: POST http://127.0.0.1:4010/foo/bar "200 OK"
+    logging.basicConfig(
+        format="[%(asctime)s - %(name)s:%(lineno)d - %(levelname)s] %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+
+
+def setup_logging() -> None:
+    env = os.environ.get("OPENAI_LOG")
+    if env == "debug":
+        _basic_config()
+        logger.setLevel(logging.DEBUG)
+        httpx_logger.setLevel(logging.DEBUG)
+    elif env == "info":
+        _basic_config()
+        logger.setLevel(logging.INFO)
+        httpx_logger.setLevel(logging.INFO)
+
+
+class SensitiveHeadersFilter(logging.Filter):
+    @override
+    def filter(self, record: logging.LogRecord) -> bool:
+        if is_dict(record.args) and "headers" in record.args and is_dict(record.args["headers"]):
+            headers = record.args["headers"] = {**record.args["headers"]}
+            for header in headers:
+                if str(header).lower() in SENSITIVE_HEADERS:
+                    headers[header] = "<redacted>"
+        return True

venv/lib/python3.12/site-packages/openai/_utils/_path.py

@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+import re
+from typing import (
+    Any,
+    Mapping,
+    Callable,
+)
+from urllib.parse import quote
+
+# Matches '.' or '..' where each dot is either literal or percent-encoded (%2e / %2E).
+_DOT_SEGMENT_RE = re.compile(r"^(?:\.|%2[eE]){1,2}$")
+
+_PLACEHOLDER_RE = re.compile(r"\{(\w+)\}")
+
+
+def _quote_path_segment_part(value: str) -> str:
+    """Percent-encode `value` for use in a URI path segment.
+
+    Considers characters not in `pchar` set from RFC 3986 §3.3 to be unsafe.
+    https://datatracker.ietf.org/doc/html/rfc3986#section-3.3
+    """
+    # quote() already treats unreserved characters (letters, digits, and -._~)
+    # as safe, so we only need to add sub-delims, ':', and '@'.
+    # Notably, unlike the default `safe` for quote(), / is unsafe and must be quoted.
+    return quote(value, safe="!$&'()*+,;=:@")
+
+
+def _quote_query_part(value: str) -> str:
+    """Percent-encode `value` for use in a URI query string.
+
+    Considers &, = and characters not in `query` set from RFC 3986 §3.4 to be unsafe.
+    https://datatracker.ietf.org/doc/html/rfc3986#section-3.4
+    """
+    return quote(value, safe="!$'()*+,;:@/?")
+
+
+def _quote_fragment_part(value: str) -> str:
+    """Percent-encode `value` for use in a URI fragment.
+
+    Considers characters not in `fragment` set from RFC 3986 §3.5 to be unsafe.
+    https://datatracker.ietf.org/doc/html/rfc3986#section-3.5
+    """
+    return quote(value, safe="!$&'()*+,;=:@/?")
+
+
+def _interpolate(
+    template: str,
+    values: Mapping[str, Any],
+    quoter: Callable[[str], str],
+) -> str:
+    """Replace {name} placeholders in `template`, quoting each value with `quoter`.
+
+    Placeholder names are looked up in `values`.
+
+    Raises:
+        KeyError: If a placeholder is not found in `values`.
+    """
+    # re.split with a capturing group returns alternating
+    # [text, name, text, name, ..., text] elements.
+    parts = _PLACEHOLDER_RE.split(template)
+
+    for i in range(1, len(parts), 2):
+        name = parts[i]
+        if name not in values:
+            raise KeyError(f"a value for placeholder {{{name}}} was not provided")
+        val = values[name]
+        if val is None:
+            parts[i] = "null"
+        elif isinstance(val, bool):
+            parts[i] = "true" if val else "false"
+        else:
+            parts[i] = quoter(str(values[name]))
+
+    return "".join(parts)
+
+
+def path_template(template: str, /, **kwargs: Any) -> str:
+    """Interpolate {name} placeholders in `template` from keyword arguments.
+
+    Args:
+        template: The template string containing {name} placeholders.
+        **kwargs: Keyword arguments to interpolate into the template.
+
+    Returns:
+        The template with placeholders interpolated and percent-encoded.
+
+        Safe characters for percent-encoding are dependent on the URI component.
+        Placeholders in path and fragment portions are percent-encoded where the `segment`
+        and `fragment` sets from RFC 3986 respectively are considered safe.
+        Placeholders in the query portion are percent-encoded where the `query` set from
+        RFC 3986 §3.3 is considered safe except for = and & characters.
+
+    Raises:
+        KeyError: If a placeholder is not found in `kwargs`.
+        ValueError: If resulting path contains /./ or /../ segments (including percent-encoded dot-segments).
+    """
+    # Split the template into path, query, and fragment portions.
+    fragment_template: str | None = None
+    query_template: str | None = None
+
+    rest = template
+    if "#" in rest:
+        rest, fragment_template = rest.split("#", 1)
+    if "?" in rest:
+        rest, query_template = rest.split("?", 1)
+    path_template = rest
+
+    # Interpolate each portion with the appropriate quoting rules.
+    path_result = _interpolate(path_template, kwargs, _quote_path_segment_part)
+
+    # Reject dot-segments (. and ..) in the final assembled path.  The check
+    # runs after interpolation so that adjacent placeholders or a mix of static
+    # text and placeholders that together form a dot-segment are caught.
+    # Also reject percent-encoded dot-segments to protect against incorrectly
+    # implemented normalization in servers/proxies.
+    for segment in path_result.split("/"):
+        if _DOT_SEGMENT_RE.match(segment):
+            raise ValueError(f"Constructed path {path_result!r} contains dot-segment {segment!r} which is not allowed")
+
+    result = path_result
+    if query_template is not None:
+        result += "?" + _interpolate(query_template, kwargs, _quote_query_part)
+    if fragment_template is not None:
+        result += "#" + _interpolate(fragment_template, kwargs, _quote_fragment_part)
+
+    return result

venv/lib/python3.12/site-packages/openai/_utils/_proxy.py

@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import Generic, TypeVar, Iterable, cast
+from typing_extensions import override
+
+T = TypeVar("T")
+
+
+class LazyProxy(Generic[T], ABC):
+    """Implements data methods to pretend that an instance is another instance.
+
+    This includes forwarding attribute access and other methods.
+    """
+
+    # Note: we have to special case proxies that themselves return proxies
+    # to support using a proxy as a catch-all for any random access, e.g. `proxy.foo.bar.baz`
+
+    def __getattr__(self, attr: str) -> object:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return proxied  # pyright: ignore
+        return getattr(proxied, attr)
+
+    @override
+    def __repr__(self) -> str:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return proxied.__class__.__name__
+        return repr(self.__get_proxied__())
+
+    @override
+    def __str__(self) -> str:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return proxied.__class__.__name__
+        return str(proxied)
+
+    @override
+    def __dir__(self) -> Iterable[str]:
+        proxied = self.__get_proxied__()
+        if isinstance(proxied, LazyProxy):
+            return []
+        return proxied.__dir__()
+
+    @property  # type: ignore
+    @override
+    def __class__(self) -> type:  # pyright: ignore
+        try:
+            proxied = self.__get_proxied__()
+        except Exception:
+            return type(self)
+        if issubclass(type(proxied), LazyProxy):
+            return type(proxied)
+        return proxied.__class__
+
+    def __get_proxied__(self) -> T:
+        return self.__load__()
+
+    def __as_proxied__(self) -> T:
+        """Helper method that returns the current proxy, typed as the loaded object"""
+        return cast(T, self)
+
+    @abstractmethod
+    def __load__(self) -> T: ...

venv/lib/python3.12/site-packages/openai/_utils/_reflection.py

@@ -0,0 +1,45 @@
+from __future__ import annotations
+
+import inspect
+from typing import Any, Callable
+
+
+def function_has_argument(func: Callable[..., Any], arg_name: str) -> bool:
+    """Returns whether or not the given function has a specific parameter"""
+    sig = inspect.signature(func)
+    return arg_name in sig.parameters
+
+
+def assert_signatures_in_sync(
+    source_func: Callable[..., Any],
+    check_func: Callable[..., Any],
+    *,
+    exclude_params: set[str] = set(),
+    description: str = "",
+) -> None:
+    """Ensure that the signature of the second function matches the first."""
+
+    check_sig = inspect.signature(check_func)
+    source_sig = inspect.signature(source_func)
+
+    errors: list[str] = []
+
+    for name, source_param in source_sig.parameters.items():
+        if name in exclude_params:
+            continue
+
+        custom_param = check_sig.parameters.get(name)
+        if not custom_param:
+            errors.append(f"the `{name}` param is missing")
+            continue
+
+        if custom_param.annotation != source_param.annotation:
+            errors.append(
+                f"types for the `{name}` param are do not match; source={repr(source_param.annotation)} checking={repr(custom_param.annotation)}"
+            )
+            continue
+
+    if errors:
+        raise AssertionError(
+            f"{len(errors)} errors encountered when comparing signatures{description}:\n\n" + "\n\n".join(errors)
+        )

venv/lib/python3.12/site-packages/openai/_utils/_resources_proxy.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from typing import Any
+from typing_extensions import override
+
+from ._proxy import LazyProxy
+
+
+class ResourcesProxy(LazyProxy[Any]):
+    """A proxy for the `openai.resources` module.
+
+    This is used so that we can lazily import `openai.resources` only when
+    needed *and* so that users can just import `openai` and reference `openai.resources`
+    """
+
+    @override
+    def __load__(self) -> Any:
+        import importlib
+
+        mod = importlib.import_module("openai.resources")
+        return mod
+
+
+resources = ResourcesProxy().__as_proxied__()

venv/lib/python3.12/site-packages/openai/_utils/_streams.py

@@ -0,0 +1,12 @@
+from typing import Any
+from typing_extensions import Iterator, AsyncIterator
+
+
+def consume_sync_iterator(iterator: Iterator[Any]) -> None:
+    for _ in iterator:
+        ...
+
+
+async def consume_async_iterator(iterator: AsyncIterator[Any]) -> None:
+    async for _ in iterator:
+        ...

venv/lib/python3.12/site-packages/openai/_utils/_sync.py

@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+import asyncio
+import functools
+from typing import TypeVar, Callable, Awaitable
+from typing_extensions import ParamSpec
+
+import anyio
+import sniffio
+import anyio.to_thread
+
+T_Retval = TypeVar("T_Retval")
+T_ParamSpec = ParamSpec("T_ParamSpec")
+
+
+async def to_thread(
+    func: Callable[T_ParamSpec, T_Retval], /, *args: T_ParamSpec.args, **kwargs: T_ParamSpec.kwargs
+) -> T_Retval:
+    if sniffio.current_async_library() == "asyncio":
+        return await asyncio.to_thread(func, *args, **kwargs)
+
+    return await anyio.to_thread.run_sync(
+        functools.partial(func, *args, **kwargs),
+    )
+
+
+# inspired by `asyncer`, https://github.com/tiangolo/asyncer
+def asyncify(function: Callable[T_ParamSpec, T_Retval]) -> Callable[T_ParamSpec, Awaitable[T_Retval]]:
+    """
+    Take a blocking function and create an async one that receives the same
+    positional and keyword arguments.
+
+    Usage:
+
+    ```python
+    def blocking_func(arg1, arg2, kwarg1=None):
+        # blocking code
+        return result
+
+
+    result = asyncify(blocking_function)(arg1, arg2, kwarg1=value1)
+    ```
+
+    ## Arguments
+
+    `function`: a blocking regular callable (e.g. a function)
+
+    ## Return
+
+    An async function that takes the same positional and keyword arguments as the
+    original one, that when called runs the same original function in a thread worker
+    and returns the result.
+    """
+
+    async def wrapper(*args: T_ParamSpec.args, **kwargs: T_ParamSpec.kwargs) -> T_Retval:
+        return await to_thread(function, *args, **kwargs)
+
+    return wrapper

venv/lib/python3.12/site-packages/openai/_utils/_transform.py

@@ -0,0 +1,457 @@
+from __future__ import annotations
+
+import io
+import base64
+import pathlib
+from typing import Any, Mapping, TypeVar, cast
+from datetime import date, datetime
+from typing_extensions import Literal, get_args, override, get_type_hints as _get_type_hints
+
+import anyio
+import pydantic
+
+from ._utils import (
+    is_list,
+    is_given,
+    lru_cache,
+    is_mapping,
+    is_iterable,
+    is_sequence,
+)
+from .._files import is_base64_file_input
+from ._compat import get_origin, is_typeddict
+from ._typing import (
+    is_list_type,
+    is_union_type,
+    extract_type_arg,
+    is_iterable_type,
+    is_required_type,
+    is_sequence_type,
+    is_annotated_type,
+    strip_annotated_type,
+)
+
+_T = TypeVar("_T")
+
+
+# TODO: support for drilling globals() and locals()
+# TODO: ensure works correctly with forward references in all cases
+
+
+PropertyFormat = Literal["iso8601", "base64", "custom"]
+
+
+class PropertyInfo:
+    """Metadata class to be used in Annotated types to provide information about a given type.
+
+    For example:
+
+    class MyParams(TypedDict):
+        account_holder_name: Annotated[str, PropertyInfo(alias='accountHolderName')]
+
+    This means that {'account_holder_name': 'Robert'} will be transformed to {'accountHolderName': 'Robert'} before being sent to the API.
+    """
+
+    alias: str | None
+    format: PropertyFormat | None
+    format_template: str | None
+    discriminator: str | None
+
+    def __init__(
+        self,
+        *,
+        alias: str | None = None,
+        format: PropertyFormat | None = None,
+        format_template: str | None = None,
+        discriminator: str | None = None,
+    ) -> None:
+        self.alias = alias
+        self.format = format
+        self.format_template = format_template
+        self.discriminator = discriminator
+
+    @override
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(alias='{self.alias}', format={self.format}, format_template='{self.format_template}', discriminator='{self.discriminator}')"
+
+
+def maybe_transform(
+    data: object,
+    expected_type: object,
+) -> Any | None:
+    """Wrapper over `transform()` that allows `None` to be passed.
+
+    See `transform()` for more details.
+    """
+    if data is None:
+        return None
+    return transform(data, expected_type)
+
+
+# Wrapper over _transform_recursive providing fake types
+def transform(
+    data: _T,
+    expected_type: object,
+) -> _T:
+    """Transform dictionaries based off of type information from the given type, for example:
+
+    ```py
+    class Params(TypedDict, total=False):
+        card_id: Required[Annotated[str, PropertyInfo(alias="cardID")]]
+
+
+    transformed = transform({"card_id": "<my card ID>"}, Params)
+    # {'cardID': '<my card ID>'}
+    ```
+
+    Any keys / data that does not have type information given will be included as is.
+
+    It should be noted that the transformations that this function does are not represented in the type system.
+    """
+    transformed = _transform_recursive(data, annotation=cast(type, expected_type))
+    return cast(_T, transformed)
+
+
+@lru_cache(maxsize=8096)
+def _get_annotated_type(type_: type) -> type | None:
+    """If the given type is an `Annotated` type then it is returned, if not `None` is returned.
+
+    This also unwraps the type when applicable, e.g. `Required[Annotated[T, ...]]`
+    """
+    if is_required_type(type_):
+        # Unwrap `Required[Annotated[T, ...]]` to `Annotated[T, ...]`
+        type_ = get_args(type_)[0]
+
+    if is_annotated_type(type_):
+        return type_
+
+    return None
+
+
+def _maybe_transform_key(key: str, type_: type) -> str:
+    """Transform the given `data` based on the annotations provided in `type_`.
+
+    Note: this function only looks at `Annotated` types that contain `PropertyInfo` metadata.
+    """
+    annotated_type = _get_annotated_type(type_)
+    if annotated_type is None:
+        # no `Annotated` definition for this type, no transformation needed
+        return key
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.alias is not None:
+            return annotation.alias
+
+    return key
+
+
+def _no_transform_needed(annotation: type) -> bool:
+    return annotation == float or annotation == int
+
+
+def _transform_recursive(
+    data: object,
+    *,
+    annotation: type,
+    inner_type: type | None = None,
+) -> object:
+    """Transform the given data against the expected type.
+
+    Args:
+        annotation: The direct type annotation given to the particular piece of data.
+            This may or may not be wrapped in metadata types, e.g. `Required[T]`, `Annotated[T, ...]` etc
+
+        inner_type: If applicable, this is the "inside" type. This is useful in certain cases where the outside type
+            is a container type such as `List[T]`. In that case `inner_type` should be set to `T` so that each entry in
+            the list can be transformed using the metadata from the container type.
+
+            Defaults to the same value as the `annotation` argument.
+    """
+    from .._compat import model_dump
+
+    if inner_type is None:
+        inner_type = annotation
+
+    stripped_type = strip_annotated_type(inner_type)
+    origin = get_origin(stripped_type) or stripped_type
+    if is_typeddict(stripped_type) and is_mapping(data):
+        return _transform_typeddict(data, stripped_type)
+
+    if origin == dict and is_mapping(data):
+        items_type = get_args(stripped_type)[1]
+        return {key: _transform_recursive(value, annotation=items_type) for key, value in data.items()}
+
+    if (
+        # List[T]
+        (is_list_type(stripped_type) and is_list(data))
+        # Iterable[T]
+        or (is_iterable_type(stripped_type) and is_iterable(data) and not isinstance(data, str))
+        # Sequence[T]
+        or (is_sequence_type(stripped_type) and is_sequence(data) and not isinstance(data, str))
+    ):
+        # dicts are technically iterable, but it is an iterable on the keys of the dict and is not usually
+        # intended as an iterable, so we don't transform it.
+        if isinstance(data, dict):
+            return cast(object, data)
+
+        inner_type = extract_type_arg(stripped_type, 0)
+        if _no_transform_needed(inner_type):
+            # for some types there is no need to transform anything, so we can get a small
+            # perf boost from skipping that work.
+            #
+            # but we still need to convert to a list to ensure the data is json-serializable
+            if is_list(data):
+                return data
+            return list(data)
+
+        return [_transform_recursive(d, annotation=annotation, inner_type=inner_type) for d in data]
+
+    if is_union_type(stripped_type):
+        # For union types we run the transformation against all subtypes to ensure that everything is transformed.
+        #
+        # TODO: there may be edge cases where the same normalized field name will transform to two different names
+        # in different subtypes.
+        for subtype in get_args(stripped_type):
+            data = _transform_recursive(data, annotation=annotation, inner_type=subtype)
+        return data
+
+    if isinstance(data, pydantic.BaseModel):
+        return model_dump(data, exclude_unset=True, mode="json", exclude=getattr(data, "__api_exclude__", None))
+
+    annotated_type = _get_annotated_type(annotation)
+    if annotated_type is None:
+        return data
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.format is not None:
+            return _format_data(data, annotation.format, annotation.format_template)
+
+    return data
+
+
+def _format_data(data: object, format_: PropertyFormat, format_template: str | None) -> object:
+    if isinstance(data, (date, datetime)):
+        if format_ == "iso8601":
+            return data.isoformat()
+
+        if format_ == "custom" and format_template is not None:
+            return data.strftime(format_template)
+
+    if format_ == "base64" and is_base64_file_input(data):
+        binary: str | bytes | None = None
+
+        if isinstance(data, pathlib.Path):
+            binary = data.read_bytes()
+        elif isinstance(data, io.IOBase):
+            binary = data.read()
+
+            if isinstance(binary, str):  # type: ignore[unreachable]
+                binary = binary.encode()
+
+        if not isinstance(binary, bytes):
+            raise RuntimeError(f"Could not read bytes from {data}; Received {type(binary)}")
+
+        return base64.b64encode(binary).decode("ascii")
+
+    return data
+
+
+def _transform_typeddict(
+    data: Mapping[str, object],
+    expected_type: type,
+) -> Mapping[str, object]:
+    result: dict[str, object] = {}
+    annotations = get_type_hints(expected_type, include_extras=True)
+    for key, value in data.items():
+        if not is_given(value):
+            # we don't need to include omitted values here as they'll
+            # be stripped out before the request is sent anyway
+            continue
+
+        type_ = annotations.get(key)
+        if type_ is None:
+            # we do not have a type annotation for this field, leave it as is
+            result[key] = value
+        else:
+            result[_maybe_transform_key(key, type_)] = _transform_recursive(value, annotation=type_)
+    return result
+
+
+async def async_maybe_transform(
+    data: object,
+    expected_type: object,
+) -> Any | None:
+    """Wrapper over `async_transform()` that allows `None` to be passed.
+
+    See `async_transform()` for more details.
+    """
+    if data is None:
+        return None
+    return await async_transform(data, expected_type)
+
+
+async def async_transform(
+    data: _T,
+    expected_type: object,
+) -> _T:
+    """Transform dictionaries based off of type information from the given type, for example:
+
+    ```py
+    class Params(TypedDict, total=False):
+        card_id: Required[Annotated[str, PropertyInfo(alias="cardID")]]
+
+
+    transformed = transform({"card_id": "<my card ID>"}, Params)
+    # {'cardID': '<my card ID>'}
+    ```
+
+    Any keys / data that does not have type information given will be included as is.
+
+    It should be noted that the transformations that this function does are not represented in the type system.
+    """
+    transformed = await _async_transform_recursive(data, annotation=cast(type, expected_type))
+    return cast(_T, transformed)
+
+
+async def _async_transform_recursive(
+    data: object,
+    *,
+    annotation: type,
+    inner_type: type | None = None,
+) -> object:
+    """Transform the given data against the expected type.
+
+    Args:
+        annotation: The direct type annotation given to the particular piece of data.
+            This may or may not be wrapped in metadata types, e.g. `Required[T]`, `Annotated[T, ...]` etc
+
+        inner_type: If applicable, this is the "inside" type. This is useful in certain cases where the outside type
+            is a container type such as `List[T]`. In that case `inner_type` should be set to `T` so that each entry in
+            the list can be transformed using the metadata from the container type.
+
+            Defaults to the same value as the `annotation` argument.
+    """
+    from .._compat import model_dump
+
+    if inner_type is None:
+        inner_type = annotation
+
+    stripped_type = strip_annotated_type(inner_type)
+    origin = get_origin(stripped_type) or stripped_type
+    if is_typeddict(stripped_type) and is_mapping(data):
+        return await _async_transform_typeddict(data, stripped_type)
+
+    if origin == dict and is_mapping(data):
+        items_type = get_args(stripped_type)[1]
+        return {key: _transform_recursive(value, annotation=items_type) for key, value in data.items()}
+
+    if (
+        # List[T]
+        (is_list_type(stripped_type) and is_list(data))
+        # Iterable[T]
+        or (is_iterable_type(stripped_type) and is_iterable(data) and not isinstance(data, str))
+        # Sequence[T]
+        or (is_sequence_type(stripped_type) and is_sequence(data) and not isinstance(data, str))
+    ):
+        # dicts are technically iterable, but it is an iterable on the keys of the dict and is not usually
+        # intended as an iterable, so we don't transform it.
+        if isinstance(data, dict):
+            return cast(object, data)
+
+        inner_type = extract_type_arg(stripped_type, 0)
+        if _no_transform_needed(inner_type):
+            # for some types there is no need to transform anything, so we can get a small
+            # perf boost from skipping that work.
+            #
+            # but we still need to convert to a list to ensure the data is json-serializable
+            if is_list(data):
+                return data
+            return list(data)
+
+        return [await _async_transform_recursive(d, annotation=annotation, inner_type=inner_type) for d in data]
+
+    if is_union_type(stripped_type):
+        # For union types we run the transformation against all subtypes to ensure that everything is transformed.
+        #
+        # TODO: there may be edge cases where the same normalized field name will transform to two different names
+        # in different subtypes.
+        for subtype in get_args(stripped_type):
+            data = await _async_transform_recursive(data, annotation=annotation, inner_type=subtype)
+        return data
+
+    if isinstance(data, pydantic.BaseModel):
+        return model_dump(data, exclude_unset=True, mode="json")
+
+    annotated_type = _get_annotated_type(annotation)
+    if annotated_type is None:
+        return data
+
+    # ignore the first argument as it is the actual type
+    annotations = get_args(annotated_type)[1:]
+    for annotation in annotations:
+        if isinstance(annotation, PropertyInfo) and annotation.format is not None:
+            return await _async_format_data(data, annotation.format, annotation.format_template)
+
+    return data
+
+
+async def _async_format_data(data: object, format_: PropertyFormat, format_template: str | None) -> object:
+    if isinstance(data, (date, datetime)):
+        if format_ == "iso8601":
+            return data.isoformat()
+
+        if format_ == "custom" and format_template is not None:
+            return data.strftime(format_template)
+
+    if format_ == "base64" and is_base64_file_input(data):
+        binary: str | bytes | None = None
+
+        if isinstance(data, pathlib.Path):
+            binary = await anyio.Path(data).read_bytes()
+        elif isinstance(data, io.IOBase):
+            binary = data.read()
+
+            if isinstance(binary, str):  # type: ignore[unreachable]
+                binary = binary.encode()
+
+        if not isinstance(binary, bytes):
+            raise RuntimeError(f"Could not read bytes from {data}; Received {type(binary)}")
+
+        return base64.b64encode(binary).decode("ascii")
+
+    return data
+
+
+async def _async_transform_typeddict(
+    data: Mapping[str, object],
+    expected_type: type,
+) -> Mapping[str, object]:
+    result: dict[str, object] = {}
+    annotations = get_type_hints(expected_type, include_extras=True)
+    for key, value in data.items():
+        if not is_given(value):
+            # we don't need to include omitted values here as they'll
+            # be stripped out before the request is sent anyway
+            continue
+
+        type_ = annotations.get(key)
+        if type_ is None:
+            # we do not have a type annotation for this field, leave it as is
+            result[key] = value
+        else:
+            result[_maybe_transform_key(key, type_)] = await _async_transform_recursive(value, annotation=type_)
+    return result
+
+
+@lru_cache(maxsize=8096)
+def get_type_hints(
+    obj: Any,
+    globalns: dict[str, Any] | None = None,
+    localns: Mapping[str, Any] | None = None,
+    include_extras: bool = False,
+) -> dict[str, Any]:
+    return _get_type_hints(obj, globalns=globalns, localns=localns, include_extras=include_extras)

venv/lib/python3.12/site-packages/openai/_utils/_typing.py

@@ -0,0 +1,156 @@
+from __future__ import annotations
+
+import sys
+import typing
+import typing_extensions
+from typing import Any, TypeVar, Iterable, cast
+from collections import abc as _c_abc
+from typing_extensions import (
+    TypeIs,
+    Required,
+    Annotated,
+    get_args,
+    get_origin,
+)
+
+from ._utils import lru_cache
+from .._types import InheritsGeneric
+from ._compat import is_union as _is_union
+
+
+def is_annotated_type(typ: type) -> bool:
+    return get_origin(typ) == Annotated
+
+
+def is_list_type(typ: type) -> bool:
+    return (get_origin(typ) or typ) == list
+
+
+def is_sequence_type(typ: type) -> bool:
+    origin = get_origin(typ) or typ
+    return origin == typing_extensions.Sequence or origin == typing.Sequence or origin == _c_abc.Sequence
+
+
+def is_iterable_type(typ: type) -> bool:
+    """If the given type is `typing.Iterable[T]`"""
+    origin = get_origin(typ) or typ
+    return origin == Iterable or origin == _c_abc.Iterable
+
+
+def is_union_type(typ: type) -> bool:
+    return _is_union(get_origin(typ))
+
+
+def is_required_type(typ: type) -> bool:
+    return get_origin(typ) == Required
+
+
+def is_typevar(typ: type) -> bool:
+    # type ignore is required because type checkers
+    # think this expression will always return False
+    return type(typ) == TypeVar  # type: ignore
+
+
+_TYPE_ALIAS_TYPES: tuple[type[typing_extensions.TypeAliasType], ...] = (typing_extensions.TypeAliasType,)
+if sys.version_info >= (3, 12):
+    _TYPE_ALIAS_TYPES = (*_TYPE_ALIAS_TYPES, typing.TypeAliasType)
+
+
+def is_type_alias_type(tp: Any, /) -> TypeIs[typing_extensions.TypeAliasType]:
+    """Return whether the provided argument is an instance of `TypeAliasType`.
+
+    ```python
+    type Int = int
+    is_type_alias_type(Int)
+    # > True
+    Str = TypeAliasType("Str", str)
+    is_type_alias_type(Str)
+    # > True
+    ```
+    """
+    return isinstance(tp, _TYPE_ALIAS_TYPES)
+
+
+# Extracts T from Annotated[T, ...] or from Required[Annotated[T, ...]]
+@lru_cache(maxsize=8096)
+def strip_annotated_type(typ: type) -> type:
+    if is_required_type(typ) or is_annotated_type(typ):
+        return strip_annotated_type(cast(type, get_args(typ)[0]))
+
+    return typ
+
+
+def extract_type_arg(typ: type, index: int) -> type:
+    args = get_args(typ)
+    try:
+        return cast(type, args[index])
+    except IndexError as err:
+        raise RuntimeError(f"Expected type {typ} to have a type argument at index {index} but it did not") from err
+
+
+def extract_type_var_from_base(
+    typ: type,
+    *,
+    generic_bases: tuple[type, ...],
+    index: int,
+    failure_message: str | None = None,
+) -> type:
+    """Given a type like `Foo[T]`, returns the generic type variable `T`.
+
+    This also handles the case where a concrete subclass is given, e.g.
+    ```py
+    class MyResponse(Foo[bytes]):
+        ...
+
+    extract_type_var(MyResponse, bases=(Foo,), index=0) -> bytes
+    ```
+
+    And where a generic subclass is given:
+    ```py
+    _T = TypeVar('_T')
+    class MyResponse(Foo[_T]):
+        ...
+
+    extract_type_var(MyResponse[bytes], bases=(Foo,), index=0) -> bytes
+    ```
+    """
+    cls = cast(object, get_origin(typ) or typ)
+    if cls in generic_bases:  # pyright: ignore[reportUnnecessaryContains]
+        # we're given the class directly
+        return extract_type_arg(typ, index)
+
+    # if a subclass is given
+    # ---
+    # this is needed as __orig_bases__ is not present in the typeshed stubs
+    # because it is intended to be for internal use only, however there does
+    # not seem to be a way to resolve generic TypeVars for inherited subclasses
+    # without using it.
+    if isinstance(cls, InheritsGeneric):
+        target_base_class: Any | None = None
+        for base in cls.__orig_bases__:
+            if base.__origin__ in generic_bases:
+                target_base_class = base
+                break
+
+        if target_base_class is None:
+            raise RuntimeError(
+                "Could not find the generic base class;\n"
+                "This should never happen;\n"
+                f"Does {cls} inherit from one of {generic_bases} ?"
+            )
+
+        extracted = extract_type_arg(target_base_class, index)
+        if is_typevar(extracted):
+            # If the extracted type argument is itself a type variable
+            # then that means the subclass itself is generic, so we have
+            # to resolve the type argument from the class itself, not
+            # the base class.
+            #
+            # Note: if there is more than 1 type argument, the subclass could
+            # change the ordering of the type arguments, this is not currently
+            # supported.
+            return extract_type_arg(typ, index)
+
+        return extracted
+
+    raise RuntimeError(failure_message or f"Could not resolve inner type variable at index {index} for {typ}")

venv/lib/python3.12/site-packages/openai/_utils/_utils.py

@@ -0,0 +1,449 @@
+from __future__ import annotations
+
+import os
+import re
+import inspect
+import functools
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Tuple,
+    Mapping,
+    TypeVar,
+    Callable,
+    Iterable,
+    Sequence,
+    cast,
+    overload,
+)
+from pathlib import Path
+from datetime import date, datetime
+from typing_extensions import TypeGuard, get_args
+
+import sniffio
+
+from .._types import Omit, NotGiven, FileTypes, ArrayFormat, HeadersLike
+
+_T = TypeVar("_T")
+_TupleT = TypeVar("_TupleT", bound=Tuple[object, ...])
+_MappingT = TypeVar("_MappingT", bound=Mapping[str, object])
+_SequenceT = TypeVar("_SequenceT", bound=Sequence[object])
+CallableT = TypeVar("CallableT", bound=Callable[..., Any])
+
+if TYPE_CHECKING:
+    from ..lib.azure import AzureOpenAI, AsyncAzureOpenAI
+
+
+def flatten(t: Iterable[Iterable[_T]]) -> list[_T]:
+    return [item for sublist in t for item in sublist]
+
+
+def extract_files(
+    # TODO: this needs to take Dict but variance issues.....
+    # create protocol type ?
+    query: Mapping[str, object],
+    *,
+    paths: Sequence[Sequence[str]],
+    array_format: ArrayFormat = "brackets",
+) -> list[tuple[str, FileTypes]]:
+    """Recursively extract files from the given dictionary based on specified paths.
+
+    A path may look like this ['foo', 'files', '<array>', 'data'].
+
+    ``array_format`` controls how ``<array>`` segments contribute to the emitted
+    field name. Supported values: ``"brackets"`` (``foo[]``), ``"repeat"`` and
+    ``"comma"`` (``foo``), ``"indices"`` (``foo[0]``, ``foo[1]``).
+
+    Note: this mutates the given dictionary.
+    """
+    files: list[tuple[str, FileTypes]] = []
+    for path in paths:
+        files.extend(_extract_items(query, path, index=0, flattened_key=None, array_format=array_format))
+    return files
+
+
+def _array_suffix(array_format: ArrayFormat, array_index: int) -> str:
+    if array_format == "brackets":
+        return "[]"
+    if array_format == "indices":
+        return f"[{array_index}]"
+    if array_format == "repeat" or array_format == "comma":
+        # Both repeat the bare field name for each file part; there is no
+        # meaningful way to comma-join binary parts.
+        return ""
+    raise NotImplementedError(
+        f"Unknown array_format value: {array_format}, choose from {', '.join(get_args(ArrayFormat))}"
+    )
+
+
+def _extract_items(
+    obj: object,
+    path: Sequence[str],
+    *,
+    index: int,
+    flattened_key: str | None,
+    array_format: ArrayFormat,
+) -> list[tuple[str, FileTypes]]:
+    try:
+        key = path[index]
+    except IndexError:
+        if not is_given(obj):
+            # no value was provided - we can safely ignore
+            return []
+
+        # cyclical import
+        from .._files import assert_is_file_content
+
+        # We have exhausted the path, return the entry we found.
+        assert flattened_key is not None
+
+        if is_list(obj):
+            files: list[tuple[str, FileTypes]] = []
+            for array_index, entry in enumerate(obj):
+                suffix = _array_suffix(array_format, array_index)
+                emitted_key = (flattened_key + suffix) if flattened_key else suffix
+                assert_is_file_content(entry, key=emitted_key)
+                files.append((emitted_key, cast(FileTypes, entry)))
+            return files
+
+        assert_is_file_content(obj, key=flattened_key)
+        return [(flattened_key, cast(FileTypes, obj))]
+
+    index += 1
+    if is_dict(obj):
+        try:
+            # Remove the field if there are no more dict keys in the path,
+            # only "<array>" traversal markers or end.
+            if all(p == "<array>" for p in path[index:]):
+                item = obj.pop(key)
+            else:
+                item = obj[key]
+        except KeyError:
+            # Key was not present in the dictionary, this is not indicative of an error
+            # as the given path may not point to a required field. We also do not want
+            # to enforce required fields as the API may differ from the spec in some cases.
+            return []
+        if flattened_key is None:
+            flattened_key = key
+        else:
+            flattened_key += f"[{key}]"
+        return _extract_items(
+            item,
+            path,
+            index=index,
+            flattened_key=flattened_key,
+            array_format=array_format,
+        )
+    elif is_list(obj):
+        if key != "<array>":
+            return []
+
+        return flatten(
+            [
+                _extract_items(
+                    item,
+                    path,
+                    index=index,
+                    flattened_key=(
+                        (flattened_key if flattened_key is not None else "") + _array_suffix(array_format, array_index)
+                    ),
+                    array_format=array_format,
+                )
+                for array_index, item in enumerate(obj)
+            ]
+        )
+
+    # Something unexpected was passed, just ignore it.
+    return []
+
+
+def is_given(obj: _T | NotGiven | Omit) -> TypeGuard[_T]:
+    return not isinstance(obj, NotGiven) and not isinstance(obj, Omit)
+
+
+# Type safe methods for narrowing types with TypeVars.
+# The default narrowing for isinstance(obj, dict) is dict[unknown, unknown],
+# however this cause Pyright to rightfully report errors. As we know we don't
+# care about the contained types we can safely use `object` in its place.
+#
+# There are two separate functions defined, `is_*` and `is_*_t` for different use cases.
+# `is_*` is for when you're dealing with an unknown input
+# `is_*_t` is for when you're narrowing a known union type to a specific subset
+
+
+def is_tuple(obj: object) -> TypeGuard[tuple[object, ...]]:
+    return isinstance(obj, tuple)
+
+
+def is_tuple_t(obj: _TupleT | object) -> TypeGuard[_TupleT]:
+    return isinstance(obj, tuple)
+
+
+def is_sequence(obj: object) -> TypeGuard[Sequence[object]]:
+    return isinstance(obj, Sequence)
+
+
+def is_sequence_t(obj: _SequenceT | object) -> TypeGuard[_SequenceT]:
+    return isinstance(obj, Sequence)
+
+
+def is_mapping(obj: object) -> TypeGuard[Mapping[str, object]]:
+    return isinstance(obj, Mapping)
+
+
+def is_mapping_t(obj: _MappingT | object) -> TypeGuard[_MappingT]:
+    return isinstance(obj, Mapping)
+
+
+def is_dict(obj: object) -> TypeGuard[dict[object, object]]:
+    return isinstance(obj, dict)
+
+
+def is_list(obj: object) -> TypeGuard[list[object]]:
+    return isinstance(obj, list)
+
+
+def is_iterable(obj: object) -> TypeGuard[Iterable[object]]:
+    return isinstance(obj, Iterable)
+
+
+# copied from https://github.com/Rapptz/RoboDanny
+def human_join(seq: Sequence[str], *, delim: str = ", ", final: str = "or") -> str:
+    size = len(seq)
+    if size == 0:
+        return ""
+
+    if size == 1:
+        return seq[0]
+
+    if size == 2:
+        return f"{seq[0]} {final} {seq[1]}"
+
+    return delim.join(seq[:-1]) + f" {final} {seq[-1]}"
+
+
+def quote(string: str) -> str:
+    """Add single quotation marks around the given string. Does *not* do any escaping."""
+    return f"'{string}'"
+
+
+def required_args(*variants: Sequence[str]) -> Callable[[CallableT], CallableT]:
+    """Decorator to enforce a given set of arguments or variants of arguments are passed to the decorated function.
+
+    Useful for enforcing runtime validation of overloaded functions.
+
+    Example usage:
+    ```py
+    @overload
+    def foo(*, a: str) -> str: ...
+
+
+    @overload
+    def foo(*, b: bool) -> str: ...
+
+
+    # This enforces the same constraints that a static type checker would
+    # i.e. that either a or b must be passed to the function
+    @required_args(["a"], ["b"])
+    def foo(*, a: str | None = None, b: bool | None = None) -> str: ...
+    ```
+    """
+
+    def inner(func: CallableT) -> CallableT:
+        params = inspect.signature(func).parameters
+        positional = [
+            name
+            for name, param in params.items()
+            if param.kind
+            in {
+                param.POSITIONAL_ONLY,
+                param.POSITIONAL_OR_KEYWORD,
+            }
+        ]
+
+        @functools.wraps(func)
+        def wrapper(*args: object, **kwargs: object) -> object:
+            given_params: set[str] = set()
+            for i, _ in enumerate(args):
+                try:
+                    given_params.add(positional[i])
+                except IndexError:
+                    raise TypeError(
+                        f"{func.__name__}() takes {len(positional)} argument(s) but {len(args)} were given"
+                    ) from None
+
+            for key in kwargs.keys():
+                given_params.add(key)
+
+            for variant in variants:
+                matches = all((param in given_params for param in variant))
+                if matches:
+                    break
+            else:  # no break
+                if len(variants) > 1:
+                    variations = human_join(
+                        ["(" + human_join([quote(arg) for arg in variant], final="and") + ")" for variant in variants]
+                    )
+                    msg = f"Missing required arguments; Expected either {variations} arguments to be given"
+                else:
+                    assert len(variants) > 0
+
+                    # TODO: this error message is not deterministic
+                    missing = list(set(variants[0]) - given_params)
+                    if len(missing) > 1:
+                        msg = f"Missing required arguments: {human_join([quote(arg) for arg in missing])}"
+                    else:
+                        msg = f"Missing required argument: {quote(missing[0])}"
+                raise TypeError(msg)
+            return func(*args, **kwargs)
+
+        return wrapper  # type: ignore
+
+    return inner
+
+
+_K = TypeVar("_K")
+_V = TypeVar("_V")
+
+
+@overload
+def strip_not_given(obj: None) -> None: ...
+
+
+@overload
+def strip_not_given(obj: Mapping[_K, _V | NotGiven]) -> dict[_K, _V]: ...
+
+
+@overload
+def strip_not_given(obj: object) -> object: ...
+
+
+def strip_not_given(obj: object | None) -> object:
+    """Remove all top-level keys where their values are instances of `NotGiven`"""
+    if obj is None:
+        return None
+
+    if not is_mapping(obj):
+        return obj
+
+    return {key: value for key, value in obj.items() if not isinstance(value, NotGiven)}
+
+
+def coerce_integer(val: str) -> int:
+    return int(val, base=10)
+
+
+def coerce_float(val: str) -> float:
+    return float(val)
+
+
+def coerce_boolean(val: str) -> bool:
+    return val == "true" or val == "1" or val == "on"
+
+
+def maybe_coerce_integer(val: str | None) -> int | None:
+    if val is None:
+        return None
+    return coerce_integer(val)
+
+
+def maybe_coerce_float(val: str | None) -> float | None:
+    if val is None:
+        return None
+    return coerce_float(val)
+
+
+def maybe_coerce_boolean(val: str | None) -> bool | None:
+    if val is None:
+        return None
+    return coerce_boolean(val)
+
+
+def removeprefix(string: str, prefix: str) -> str:
+    """Remove a prefix from a string.
+
+    Backport of `str.removeprefix` for Python < 3.9
+    """
+    if string.startswith(prefix):
+        return string[len(prefix) :]
+    return string
+
+
+def removesuffix(string: str, suffix: str) -> str:
+    """Remove a suffix from a string.
+
+    Backport of `str.removesuffix` for Python < 3.9
+    """
+    if string.endswith(suffix):
+        return string[: -len(suffix)]
+    return string
+
+
+def file_from_path(path: str) -> FileTypes:
+    contents = Path(path).read_bytes()
+    file_name = os.path.basename(path)
+    return (file_name, contents)
+
+
+def get_required_header(headers: HeadersLike, header: str) -> str:
+    lower_header = header.lower()
+    if is_mapping_t(headers):
+        # mypy doesn't understand the type narrowing here
+        for k, v in headers.items():  # type: ignore
+            if k.lower() == lower_header and isinstance(v, str):
+                return v
+
+    # to deal with the case where the header looks like Stainless-Event-Id
+    intercaps_header = re.sub(r"([^\w])(\w)", lambda pat: pat.group(1) + pat.group(2).upper(), header.capitalize())
+
+    for normalized_header in [header, lower_header, header.upper(), intercaps_header]:
+        value = headers.get(normalized_header)
+        if value:
+            return value
+
+    raise ValueError(f"Could not find {header} header")
+
+
+def get_async_library() -> str:
+    try:
+        return sniffio.current_async_library()
+    except Exception:
+        return "false"
+
+
+def lru_cache(*, maxsize: int | None = 128) -> Callable[[CallableT], CallableT]:
+    """A version of functools.lru_cache that retains the type signature
+    for the wrapped function arguments.
+    """
+    wrapper = functools.lru_cache(  # noqa: TID251
+        maxsize=maxsize,
+    )
+    return cast(Any, wrapper)  # type: ignore[no-any-return]
+
+
+def json_safe(data: object) -> object:
+    """Translates a mapping / sequence recursively in the same fashion
+    as `pydantic` v2's `model_dump(mode="json")`.
+    """
+    if is_mapping(data):
+        return {json_safe(key): json_safe(value) for key, value in data.items()}
+
+    if is_iterable(data) and not isinstance(data, (str, bytes, bytearray)):
+        return [json_safe(item) for item in data]
+
+    if isinstance(data, (datetime, date)):
+        return data.isoformat()
+
+    return data
+
+
+def is_azure_client(client: object) -> TypeGuard[AzureOpenAI]:
+    from ..lib.azure import AzureOpenAI
+
+    return isinstance(client, AzureOpenAI)
+
+
+def is_async_azure_client(client: object) -> TypeGuard[AsyncAzureOpenAI]:
+    from ..lib.azure import AsyncAzureOpenAI
+
+    return isinstance(client, AsyncAzureOpenAI)

venv/lib/python3.12/site-packages/openai/_version.py

@@ -0,0 +1,4 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+__title__ = "openai"
+__version__ = "2.45.0"  # x-release-please-version

venv/lib/python3.12/site-packages/openai/auth/__init__.py

@@ -0,0 +1,19 @@
+from __future__ import annotations
+
+from ._workload import (
+    WorkloadIdentity as WorkloadIdentity,
+    SubjectTokenProvider as SubjectTokenProvider,
+    WorkloadIdentityAuth as WorkloadIdentityAuth,
+    gcp_id_token_provider as gcp_id_token_provider,
+    k8s_service_account_token_provider as k8s_service_account_token_provider,
+    azure_managed_identity_token_provider as azure_managed_identity_token_provider,
+)
+
+__all__ = [
+    "SubjectTokenProvider",
+    "WorkloadIdentity",
+    "WorkloadIdentityAuth",
+    "k8s_service_account_token_provider",
+    "azure_managed_identity_token_provider",
+    "gcp_id_token_provider",
+]

venv/lib/python3.12/site-packages/openai/auth/_workload.py

@@ -0,0 +1,309 @@
+from __future__ import annotations
+
+import time
+import threading
+from typing import Any, Callable, TypedDict, cast
+from pathlib import Path
+from typing_extensions import Literal, NotRequired
+
+import httpx
+
+from .._exceptions import OAuthError, OpenAIError, SubjectTokenProviderError
+from .._utils._sync import to_thread
+
+TOKEN_EXCHANGE_GRANT_TYPE = "urn:ietf:params:oauth:grant-type:token-exchange"
+DEFAULT_TOKEN_EXCHANGE_URL = "https://auth.openai.com/oauth/token"
+DEFAULT_REFRESH_BUFFER_SECONDS = 1200
+
+SUBJECT_TOKEN_TYPES = {
+    "jwt": "urn:ietf:params:oauth:token-type:jwt",
+    "id": "urn:ietf:params:oauth:token-type:id_token",
+}
+
+
+class SubjectTokenProvider(TypedDict):
+    token_type: Literal["jwt", "id"]
+    get_token: Callable[[], str]
+
+
+class WorkloadIdentity(TypedDict):
+    """Identity provider resource id in WIFAPI."""
+
+    identity_provider_id: str
+
+    """Service account id to bind the verified external identity to."""
+    service_account_id: str
+
+    """The provider configuration for obtaining the subject token."""
+    provider: SubjectTokenProvider
+
+    """Optional buffer time in seconds to refresh the OpenAI token before it expires. Defaults to 1200 seconds (20 minutes)."""
+    refresh_buffer_seconds: NotRequired[float]
+
+
+def k8s_service_account_token_provider(
+    token_file_path: str | Path = "/var/run/secrets/kubernetes.io/serviceaccount/token",
+) -> SubjectTokenProvider:
+    """
+    Get a subject token provider for Kubernetes clusters with Workload Identity configured.
+
+    Cloud providers typically mount the subject token as a file in the container.
+
+    Args:
+        token_file_path: path to the mounted service account token file. Defaults to `/var/run/secrets/kubernetes.io/serviceaccount/token`.
+    """
+
+    def get_token() -> str:
+        try:
+            with open(token_file_path, "r") as f:
+                token = f.read().strip()
+                if not token:
+                    raise SubjectTokenProviderError(f"The token file at {token_file_path} is empty.")
+                return token
+        except Exception as e:
+            raise SubjectTokenProviderError(f"Failed to read the token file at {token_file_path}: {e}") from e
+
+    return {"token_type": "jwt", "get_token": get_token}
+
+
+def azure_managed_identity_token_provider(
+    resource: str = "https://management.azure.com/",
+    *,
+    object_id: str | None = None,
+    client_id: str | None = None,
+    msi_res_id: str | None = None,
+    api_version: str = "2018-02-01",
+    timeout: float = 10.0,
+    http_client: httpx.Client | None = None,
+) -> SubjectTokenProvider:
+    """
+    Get a subject token provider for Azure Managed Identities.
+
+    See: https://learn.microsoft.com/en-us/entra/identity/managed-identities-azure-resources/how-to-use-vm-token#get-a-token-using-http
+
+    Args:
+        resource: the resource URI to request a token for. Defaults to `https://management.azure.com/` (Azure Resource Manager).
+        object_id: the object ID of the managed identity to use, when multiple are assigned.
+        client_id: the client ID of the managed identity to use, when multiple are assigned.
+        msi_res_id: the ARM resource ID of the managed identity to use, when multiple are assigned.
+        api_version: the Azure IMDS API version. Defaults to `2018-02-01`.
+        timeout: the request timeout in seconds. Defaults to 10.0.
+        http_client: optional httpx.Client instance to use for requests. If not provided, a new client will be created for each request.
+    """
+
+    def get_token() -> str:
+        try:
+            url = "http://169.254.169.254/metadata/identity/oauth2/token"
+            params: dict[str, str] = {"api-version": api_version, "resource": resource}
+            if object_id is not None:
+                params["object_id"] = object_id
+            if client_id is not None:
+                params["client_id"] = client_id
+            if msi_res_id is not None:
+                params["msi_res_id"] = msi_res_id
+
+            if http_client is not None:
+                response = http_client.get(url, params=params, headers={"Metadata": "true"}, timeout=timeout)
+            else:
+                with httpx.Client() as client:
+                    response = client.get(url, params=params, headers={"Metadata": "true"}, timeout=timeout)
+
+            if response.is_error:
+                raise SubjectTokenProviderError(
+                    f"Failed to fetch Azure subject token from IMDS: HTTP {response.status_code}",
+                    response=response,
+                )
+            data = response.json()
+            token = data.get("access_token")
+            if not token:
+                raise SubjectTokenProviderError(
+                    "Azure IMDS response did not include an access_token", response=response
+                )
+            return cast(str, token)
+        except Exception as e:
+            raise SubjectTokenProviderError(f"Failed to fetch Azure subject token from IMDS: {e}") from e
+
+    return {"token_type": "jwt", "get_token": get_token}
+
+
+def gcp_id_token_provider(
+    audience: str = "https://api.openai.com/v1",
+    *,
+    timeout: float = 10.0,
+    http_client: httpx.Client | None = None,
+) -> SubjectTokenProvider:
+    """
+    Get a subject token provider for GCP VM instances using the instance metadata server.
+
+    See: https://cloud.google.com/compute/docs/instances/verifying-instance-identity
+
+    Args:
+        audience: the unique URI agreed upon by both the instance and the system verifying
+            the instance's identity. Defaults to `https://api.openai.com/v1`.
+        timeout: the request timeout in seconds. Defaults to 10.0.
+        http_client: optional httpx.Client instance to use for requests. If not provided, a new client will be created for each request.
+    """
+
+    def get_token() -> str:
+        try:
+            url = "http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/default/identity"
+            params = {"audience": audience}
+
+            if http_client is not None:
+                response = http_client.get(url, params=params, headers={"Metadata-Flavor": "Google"}, timeout=timeout)
+            else:
+                with httpx.Client() as client:
+                    response = client.get(url, params=params, headers={"Metadata-Flavor": "Google"}, timeout=timeout)
+
+            if response.is_error:
+                raise SubjectTokenProviderError(
+                    f"Failed to fetch GCP subject token from metadata server: HTTP {response.status_code}",
+                    response=response,
+                )
+            token = response.text.strip()
+            if not token:
+                raise SubjectTokenProviderError("GCP metadata server returned an empty token", response=response)
+            return token
+        except Exception as e:
+            raise SubjectTokenProviderError(f"Failed to fetch GCP subject token from metadata server: {e}") from e
+
+    return {"token_type": "id", "get_token": get_token}
+
+
+class WorkloadIdentityAuth:
+    def __init__(
+        self,
+        *,
+        workload_identity: WorkloadIdentity,
+        token_exchange_url: str = DEFAULT_TOKEN_EXCHANGE_URL,
+    ):
+        self.workload_identity = workload_identity
+        self.token_exchange_url = token_exchange_url
+
+        self._cached_token: str | None = None
+        self._cached_token_expires_at_monotonic: float | None = None
+        self._cached_token_refresh_at_monotonic: float | None = None
+        self._refreshing: bool = False
+        self._lock = threading.Lock()
+        self._condition = threading.Condition(self._lock)
+
+    def get_token(self) -> str:
+        with self._lock:
+            while self._refreshing and self._token_unusable():
+                self._condition.wait()
+
+            if not self._token_unusable() and not self._needs_refresh():
+                return cast(str, self._cached_token)
+
+            if self._refreshing:
+                while self._refreshing:
+                    self._condition.wait()
+                token = self._cached_token  # type: ignore[unreachable]
+                if self._token_unusable():
+                    raise RuntimeError("Token is unusable after refresh completed")
+                return cast(str, token)
+
+            self._refreshing = True
+
+        try:
+            self._perform_refresh()
+            with self._lock:
+                if self._token_unusable():
+                    raise RuntimeError("Token is unusable after refresh completed")
+                return cast(str, self._cached_token)
+        finally:
+            with self._lock:
+                self._refreshing = False
+                self._condition.notify_all()
+
+    async def get_token_async(self) -> str:
+        return await to_thread(self.get_token)
+
+    def invalidate_token(self) -> None:
+        with self._lock:
+            self._cached_token = None
+            self._cached_token_expires_at_monotonic = None
+            self._cached_token_refresh_at_monotonic = None
+
+    def _perform_refresh(self) -> None:
+        token_data = self._fetch_token_from_exchange()
+        now = time.monotonic()
+        expires_in = token_data["expires_in"]
+
+        with self._lock:
+            self._cached_token = token_data["access_token"]
+            self._cached_token_expires_at_monotonic = now + expires_in
+            self._cached_token_refresh_at_monotonic = now + self._refresh_delay_seconds(expires_in)
+
+    def _fetch_token_from_exchange(self) -> dict[str, Any]:
+        subject_token = self._get_subject_token()
+
+        token_type = self.workload_identity["provider"]["token_type"]
+        subject_token_type = SUBJECT_TOKEN_TYPES.get(token_type)
+        if subject_token_type is None:
+            raise OpenAIError(
+                f"Unsupported token type: {token_type!r}. Supported types: {', '.join(SUBJECT_TOKEN_TYPES.keys())}"
+            )
+
+        with httpx.Client() as client:
+            response = client.post(
+                self.token_exchange_url,
+                json={
+                    "grant_type": TOKEN_EXCHANGE_GRANT_TYPE,
+                    "subject_token": subject_token,
+                    "subject_token_type": subject_token_type,
+                    "identity_provider_id": self.workload_identity["identity_provider_id"],
+                    "service_account_id": self.workload_identity["service_account_id"],
+                },
+                timeout=10.0,
+            )
+            return self._handle_token_response(response)
+
+    def _handle_token_response(self, response: httpx.Response) -> dict[str, Any]:
+        try:
+            body = response.json() if response.content else None
+        except ValueError:
+            body = None
+
+        if response.status_code in (400, 401, 403):
+            raise OAuthError(response=response, body=body)
+
+        if response.is_success:
+            if body is None:
+                raise OpenAIError("Token exchange succeeded but response body was empty")
+            access_token = body.get("access_token")
+            expires_in = body.get("expires_in")
+            if not isinstance(access_token, str) or not access_token:
+                raise OpenAIError("Token exchange response did not include a valid access_token")
+            if not isinstance(expires_in, (int, float)):
+                raise OpenAIError("Token exchange response did not include a valid expires_in")
+            return {"access_token": access_token, "expires_in": float(expires_in)}
+
+        raise OpenAIError(
+            f"Token exchange failed with status {response.status_code}",
+        )
+
+    def _get_subject_token(self) -> str:
+        provider = self.workload_identity["provider"]
+        subject_token = provider["get_token"]()
+        if not subject_token:
+            raise OpenAIError("The workload identity provider returned an empty subject token")
+        return subject_token
+
+    def _token_unusable(self) -> bool:
+        return self._cached_token is None or self._token_expired()
+
+    def _token_expired(self) -> bool:
+        if self._cached_token_expires_at_monotonic is None:
+            return True
+        return time.monotonic() >= self._cached_token_expires_at_monotonic
+
+    def _needs_refresh(self) -> bool:
+        if self._cached_token_refresh_at_monotonic is None:
+            return False
+        return time.monotonic() >= self._cached_token_refresh_at_monotonic
+
+    def _refresh_delay_seconds(self, expires_in: float) -> float:
+        configured_buffer = self.workload_identity.get("refresh_buffer_seconds", DEFAULT_REFRESH_BUFFER_SECONDS)
+        effective_buffer = min(configured_buffer, expires_in / 2)
+        return max(expires_in - effective_buffer, 0.0)

venv/lib/python3.12/site-packages/openai/helpers/__init__.py

@@ -0,0 +1,4 @@
+from .microphone import Microphone
+from .local_audio_player import LocalAudioPlayer
+
+__all__ = ["Microphone", "LocalAudioPlayer"]

venv/lib/python3.12/site-packages/openai/helpers/local_audio_player.py

@@ -0,0 +1,165 @@
+# mypy: ignore-errors
+from __future__ import annotations
+
+import queue
+import asyncio
+from typing import Any, Union, Callable, AsyncGenerator, cast
+from typing_extensions import TYPE_CHECKING
+
+from .. import _legacy_response
+from .._extras import numpy as np, sounddevice as sd
+from .._response import StreamedBinaryAPIResponse, AsyncStreamedBinaryAPIResponse
+
+if TYPE_CHECKING:
+    import numpy.typing as npt
+
+SAMPLE_RATE = 24000
+
+
+class LocalAudioPlayer:
+    def __init__(
+        self,
+        should_stop: Union[Callable[[], bool], None] = None,
+    ):
+        self.channels = 1
+        self.dtype = np.float32
+        self.should_stop = should_stop
+
+    async def _tts_response_to_buffer(
+        self,
+        response: Union[
+            _legacy_response.HttpxBinaryResponseContent,
+            AsyncStreamedBinaryAPIResponse,
+            StreamedBinaryAPIResponse,
+        ],
+    ) -> npt.NDArray[np.float32]:
+        chunks: list[bytes] = []
+        if isinstance(response, _legacy_response.HttpxBinaryResponseContent) or isinstance(
+            response, StreamedBinaryAPIResponse
+        ):
+            for chunk in response.iter_bytes(chunk_size=1024):
+                if chunk:
+                    chunks.append(chunk)
+        else:
+            async for chunk in response.iter_bytes(chunk_size=1024):
+                if chunk:
+                    chunks.append(chunk)
+
+        audio_bytes = b"".join(chunks)
+        audio_np = np.frombuffer(audio_bytes, dtype=np.int16).astype(np.float32) / 32767.0
+        audio_np = audio_np.reshape(-1, 1)
+        return audio_np
+
+    async def play(
+        self,
+        input: Union[
+            npt.NDArray[np.int16],
+            npt.NDArray[np.float32],
+            _legacy_response.HttpxBinaryResponseContent,
+            AsyncStreamedBinaryAPIResponse,
+            StreamedBinaryAPIResponse,
+        ],
+    ) -> None:
+        audio_content: npt.NDArray[np.float32]
+        if isinstance(input, np.ndarray):
+            if input.dtype == np.int16 and self.dtype == np.float32:
+                audio_content = (input.astype(np.float32) / 32767.0).reshape(-1, self.channels)
+            elif input.dtype == np.float32:
+                audio_content = cast("npt.NDArray[np.float32]", input)
+            else:
+                raise ValueError(f"Unsupported dtype: {input.dtype}")
+        else:
+            audio_content = await self._tts_response_to_buffer(input)
+
+        loop = asyncio.get_event_loop()
+        event = asyncio.Event()
+        idx = 0
+
+        def callback(
+            outdata: npt.NDArray[np.float32],
+            frame_count: int,
+            _time_info: Any,
+            _status: Any,
+        ):
+            nonlocal idx
+
+            remainder = len(audio_content) - idx
+            if remainder == 0 or (callable(self.should_stop) and self.should_stop()):
+                loop.call_soon_threadsafe(event.set)
+                raise sd.CallbackStop
+            valid_frames = frame_count if remainder >= frame_count else remainder
+            outdata[:valid_frames] = audio_content[idx : idx + valid_frames]
+            outdata[valid_frames:] = 0
+            idx += valid_frames
+
+        stream = sd.OutputStream(
+            samplerate=SAMPLE_RATE,
+            callback=callback,
+            dtype=audio_content.dtype,
+            channels=audio_content.shape[1],
+        )
+        with stream:
+            await event.wait()
+
+    async def play_stream(
+        self,
+        buffer_stream: AsyncGenerator[Union[npt.NDArray[np.float32], npt.NDArray[np.int16], None], None],
+    ) -> None:
+        loop = asyncio.get_event_loop()
+        event = asyncio.Event()
+        buffer_queue: queue.Queue[Union[npt.NDArray[np.float32], npt.NDArray[np.int16], None]] = queue.Queue(maxsize=50)
+
+        async def buffer_producer():
+            async for buffer in buffer_stream:
+                if buffer is None:
+                    break
+                await loop.run_in_executor(None, buffer_queue.put, buffer)
+            await loop.run_in_executor(None, buffer_queue.put, None)  # Signal completion
+
+        def callback(
+            outdata: npt.NDArray[np.float32],
+            frame_count: int,
+            _time_info: Any,
+            _status: Any,
+        ):
+            nonlocal current_buffer, buffer_pos
+
+            frames_written = 0
+            while frames_written < frame_count:
+                if current_buffer is None or buffer_pos >= len(current_buffer):
+                    try:
+                        current_buffer = buffer_queue.get(timeout=0.1)
+                        if current_buffer is None:
+                            loop.call_soon_threadsafe(event.set)
+                            raise sd.CallbackStop
+                        buffer_pos = 0
+
+                        if current_buffer.dtype == np.int16 and self.dtype == np.float32:
+                            current_buffer = (current_buffer.astype(np.float32) / 32767.0).reshape(-1, self.channels)
+
+                    except queue.Empty:
+                        outdata[frames_written:] = 0
+                        return
+
+                remaining_frames = len(current_buffer) - buffer_pos
+                frames_to_write = min(frame_count - frames_written, remaining_frames)
+                outdata[frames_written : frames_written + frames_to_write] = current_buffer[
+                    buffer_pos : buffer_pos + frames_to_write
+                ]
+                buffer_pos += frames_to_write
+                frames_written += frames_to_write
+
+        current_buffer = None
+        buffer_pos = 0
+
+        producer_task = asyncio.create_task(buffer_producer())
+
+        with sd.OutputStream(
+            samplerate=SAMPLE_RATE,
+            channels=self.channels,
+            dtype=self.dtype,
+            callback=callback,
+        ):
+            await event.wait()
+
+        await producer_task

venv/lib/python3.12/site-packages/openai/helpers/microphone.py

@@ -0,0 +1,100 @@
+# mypy: ignore-errors
+from __future__ import annotations
+
+import io
+import time
+import wave
+import asyncio
+from typing import Any, Type, Union, Generic, TypeVar, Callable, overload
+from typing_extensions import TYPE_CHECKING, Literal
+
+from .._types import FileTypes, FileContent
+from .._extras import numpy as np, sounddevice as sd
+
+if TYPE_CHECKING:
+    import numpy.typing as npt
+
+SAMPLE_RATE = 24000
+
+DType = TypeVar("DType", bound=np.generic)
+
+
+class Microphone(Generic[DType]):
+    def __init__(
+        self,
+        channels: int = 1,
+        dtype: Type[DType] = np.int16,
+        should_record: Union[Callable[[], bool], None] = None,
+        timeout: Union[float, None] = None,
+    ):
+        self.channels = channels
+        self.dtype = dtype
+        self.should_record = should_record
+        self.buffer_chunks = []
+        self.timeout = timeout
+        self.has_record_function = callable(should_record)
+
+    def _ndarray_to_wav(self, audio_data: npt.NDArray[DType]) -> FileTypes:
+        buffer: FileContent = io.BytesIO()
+        with wave.open(buffer, "w") as wav_file:
+            wav_file.setnchannels(self.channels)
+            wav_file.setsampwidth(np.dtype(self.dtype).itemsize)
+            wav_file.setframerate(SAMPLE_RATE)
+            wav_file.writeframes(audio_data.tobytes())
+        buffer.seek(0)
+        return ("audio.wav", buffer, "audio/wav")
+
+    @overload
+    async def record(self, return_ndarray: Literal[True]) -> npt.NDArray[DType]: ...
+
+    @overload
+    async def record(self, return_ndarray: Literal[False]) -> FileTypes: ...
+
+    @overload
+    async def record(self, return_ndarray: None = ...) -> FileTypes: ...
+
+    async def record(self, return_ndarray: Union[bool, None] = False) -> Union[npt.NDArray[DType], FileTypes]:
+        loop = asyncio.get_event_loop()
+        event = asyncio.Event()
+        self.buffer_chunks: list[npt.NDArray[DType]] = []
+        start_time = time.perf_counter()
+
+        def callback(
+            indata: npt.NDArray[DType],
+            _frame_count: int,
+            _time_info: Any,
+            _status: Any,
+        ):
+            execution_time = time.perf_counter() - start_time
+            reached_recording_timeout = execution_time > self.timeout if self.timeout is not None else False
+            if reached_recording_timeout:
+                loop.call_soon_threadsafe(event.set)
+                raise sd.CallbackStop
+
+            should_be_recording = self.should_record() if callable(self.should_record) else True
+            if not should_be_recording:
+                loop.call_soon_threadsafe(event.set)
+                raise sd.CallbackStop
+
+            self.buffer_chunks.append(indata.copy())
+
+        stream = sd.InputStream(
+            callback=callback,
+            dtype=self.dtype,
+            samplerate=SAMPLE_RATE,
+            channels=self.channels,
+        )
+        with stream:
+            await event.wait()
+
+        # Concatenate all chunks into a single buffer, handle empty case
+        concatenated_chunks: npt.NDArray[DType] = (
+            np.concatenate(self.buffer_chunks, axis=0)
+            if len(self.buffer_chunks) > 0
+            else np.array([], dtype=self.dtype)
+        )
+
+        if return_ndarray:
+            return concatenated_chunks
+        else:
+            return self._ndarray_to_wav(concatenated_chunks)

venv/lib/python3.12/site-packages/openai/lib/.keep

@@ -0,0 +1,4 @@
+File generated from our OpenAPI spec by Stainless.
+
+This directory can be used to store custom files to expand the SDK.
+It is ignored by Stainless code generation and its content (other than this keep file) won't be touched.

venv/lib/python3.12/site-packages/openai/lib/__init__.py

@@ -0,0 +1,2 @@
+from ._tools import pydantic_function_tool as pydantic_function_tool
+from ._parsing import ResponseFormatT as ResponseFormatT

venv/lib/python3.12/site-packages/openai/lib/_bedrock_auth.py

@@ -0,0 +1,186 @@
+# pyright: reportMissingTypeStubs=false
+
+from __future__ import annotations
+
+import os
+import hashlib
+from typing import Any, Literal, Mapping, Callable, Protocol, cast
+from dataclasses import field, dataclass
+
+from .._exceptions import OpenAIError
+
+AwsCredentialsProvider = Callable[[], object]
+
+
+class _BotocoreSession(Protocol):
+    def get_credentials(self) -> object | None: ...
+
+
+_AUTHORIZATION = "authorization"
+_AWS_SIGNING_HEADERS = (
+    _AUTHORIZATION,
+    "x-amz-content-sha256",
+    "x-amz-date",
+    "x-amz-security-token",
+)
+
+
+def _load_botocore() -> tuple[Any, Any, Any, Any]:
+    try:
+        from botocore.auth import SigV4Auth  # type: ignore[import-untyped]
+        from botocore.session import Session  # type: ignore[import-untyped]
+        from botocore.awsrequest import AWSRequest  # type: ignore[import-untyped]
+        from botocore.credentials import Credentials  # type: ignore[import-untyped]
+    except ImportError as exc:
+        raise OpenAIError(
+            "Bedrock AWS authentication requires optional AWS dependencies. "
+            "Install them with `pip install openai[bedrock]` and try again."
+        ) from exc
+
+    return SigV4Auth, AWSRequest, Credentials, Session
+
+
+@dataclass(frozen=True)
+class BedrockAwsAuthConfig:
+    region: str
+    source: Literal["static", "profile", "provider", "default"]
+    region_source: Literal["explicit", "environment", "profile"] = "explicit"
+    profile: str | None = None
+    access_key_id: str | None = field(default=None, repr=False)
+    secret_access_key: str | None = field(default=None, repr=False)
+    session_token: str | None = field(default=None, repr=False)
+    credentials_provider: AwsCredentialsProvider | None = field(default=None, repr=False, compare=False)
+
+
+class BedrockAwsAuth:
+    def __init__(self, config: BedrockAwsAuthConfig, *, session: _BotocoreSession | None = None) -> None:
+        sigv4_auth_cls, aws_request_cls, credentials_cls, session_cls = _load_botocore()
+
+        if session is None:
+            try:
+                session = session_cls(profile=config.profile)
+            except Exception as exc:
+                raise OpenAIError(
+                    "Failed to resolve AWS credentials for Bedrock. Verify your AWS profile, environment variables, "
+                    "or runtime identity configuration and try again."
+                ) from exc
+
+        assert session is not None
+        self.config = config
+        self._session = session
+        self._credentials_provider = config.credentials_provider
+        self._explicit_credentials = (
+            credentials_cls(config.access_key_id, config.secret_access_key, config.session_token)
+            if config.access_key_id is not None and config.secret_access_key is not None
+            else None
+        )
+        self._aws_request_cls = aws_request_cls
+        self._sigv4_auth_cls = sigv4_auth_cls
+
+    @classmethod
+    def resolve(
+        cls,
+        *,
+        region: str | None,
+        profile: str | None,
+        access_key_id: str | None,
+        secret_access_key: str | None,
+        session_token: str | None,
+        credentials_provider: AwsCredentialsProvider | None,
+    ) -> BedrockAwsAuth:
+        _, _, _, session_cls = _load_botocore()
+
+        try:
+            session = session_cls(profile=profile)
+            resolved_region, region_source = resolve_aws_region_with_source(region, session=session)
+        except OpenAIError:
+            raise
+        except Exception as exc:
+            raise OpenAIError(
+                "Failed to resolve AWS credentials for Bedrock. Verify your AWS profile, environment variables, "
+                "or runtime identity configuration and try again."
+            ) from exc
+
+        source: Literal["static", "profile", "provider", "default"]
+        if access_key_id is not None:
+            source = "static"
+        elif profile is not None:
+            source = "profile"
+        elif credentials_provider is not None:
+            source = "provider"
+        else:
+            source = "default"
+
+        config = BedrockAwsAuthConfig(
+            region=resolved_region,
+            source=source,
+            region_source=region_source,
+            profile=profile,
+            access_key_id=access_key_id,
+            secret_access_key=secret_access_key,
+            session_token=session_token,
+            credentials_provider=credentials_provider,
+        )
+        return cls(config, session=session)
+
+    def sign(self, *, method: str, url: str, headers: Mapping[str, str], body: bytes | None) -> dict[str, str]:
+        try:
+            credentials = (
+                self._credentials_provider()
+                if self._credentials_provider is not None
+                else self._explicit_credentials or self._session.get_credentials()
+            )
+            if credentials is None:
+                raise OpenAIError(
+                    "Could not find credentials for Bedrock. Pass a bearer credential or AWS credentials to "
+                    "`bedrock(...)`, "
+                    "set `AWS_BEARER_TOKEN_BEDROCK`, or configure the default AWS credential chain."
+                )
+
+            get_frozen_credentials = getattr(credentials, "get_frozen_credentials", None)
+            if callable(get_frozen_credentials):
+                credentials = get_frozen_credentials()
+
+            signed_headers = {
+                name: value for name, value in headers.items() if name.lower() not in _AWS_SIGNING_HEADERS
+            }
+            signed_headers["X-Amz-Content-SHA256"] = hashlib.sha256(body or b"").hexdigest()
+            aws_request = self._aws_request_cls(
+                method=method,
+                url=url,
+                data=body,
+                headers=signed_headers,
+            )
+            self._sigv4_auth_cls(credentials, "bedrock-mantle", self.config.region).add_auth(aws_request)
+        except OpenAIError:
+            raise
+        except Exception as exc:
+            raise OpenAIError(
+                "Failed to resolve AWS credentials for Bedrock. Verify your AWS profile, environment variables, "
+                "or runtime identity configuration and try again."
+            ) from exc
+
+        return dict(aws_request.headers.items())
+
+
+def resolve_aws_region_with_source(
+    aws_region: str | None, *, session: object | None = None
+) -> tuple[str, Literal["explicit", "environment", "profile"]]:
+    region = aws_region
+    source: Literal["explicit", "environment", "profile"] = "explicit"
+    if region is None or not region.strip():
+        region = os.environ.get("AWS_REGION") or os.environ.get("AWS_DEFAULT_REGION")
+        source = "environment"
+    if (region is None or not region.strip()) and session is not None:
+        get_config_variable = getattr(session, "get_config_variable", None)
+        if callable(get_config_variable):
+            region = cast("str | None", get_config_variable("region"))
+            source = "profile"
+
+    if region is None or not region.strip():
+        raise OpenAIError(
+            "Bedrock requires an AWS region. Pass `region` to `bedrock(...)`, or set `AWS_REGION` or "
+            "`AWS_DEFAULT_REGION`."
+        )
+
+    return region.strip(), source