Add files using upload-large-folder tool

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_decoders/jsonl.py

@@ -0,0 +1,123 @@
+from __future__ import annotations
+
+import json
+from typing_extensions import Generic, TypeVar, Iterator, AsyncIterator
+
+import httpx
+
+from .._models import construct_type_unchecked
+
+_T = TypeVar("_T")
+
+
+class JSONLDecoder(Generic[_T]):
+    """A decoder for [JSON Lines](https://jsonlines.org) format.
+
+    This class provides an iterator over a byte-iterator that parses each JSON Line
+    into a given type.
+    """
+
+    http_response: httpx.Response
+    """The HTTP response this decoder was constructed from"""
+
+    def __init__(
+        self,
+        *,
+        raw_iterator: Iterator[bytes],
+        line_type: type[_T],
+        http_response: httpx.Response,
+    ) -> None:
+        super().__init__()
+        self.http_response = http_response
+        self._raw_iterator = raw_iterator
+        self._line_type = line_type
+        self._iterator = self.__decode__()
+
+    def close(self) -> None:
+        """Close the response body stream.
+
+        This is called automatically if you consume the entire stream.
+        """
+        self.http_response.close()
+
+    def __decode__(self) -> Iterator[_T]:
+        buf = b""
+        for chunk in self._raw_iterator:
+            for line in chunk.splitlines(keepends=True):
+                buf += line
+                if buf.endswith((b"\r", b"\n", b"\r\n")):
+                    yield construct_type_unchecked(
+                        value=json.loads(buf),
+                        type_=self._line_type,
+                    )
+                    buf = b""
+
+        # flush
+        if buf:
+            yield construct_type_unchecked(
+                value=json.loads(buf),
+                type_=self._line_type,
+            )
+
+    def __next__(self) -> _T:
+        return self._iterator.__next__()
+
+    def __iter__(self) -> Iterator[_T]:
+        for item in self._iterator:
+            yield item
+
+
+class AsyncJSONLDecoder(Generic[_T]):
+    """A decoder for [JSON Lines](https://jsonlines.org) format.
+
+    This class provides an async iterator over a byte-iterator that parses each JSON Line
+    into a given type.
+    """
+
+    http_response: httpx.Response
+
+    def __init__(
+        self,
+        *,
+        raw_iterator: AsyncIterator[bytes],
+        line_type: type[_T],
+        http_response: httpx.Response,
+    ) -> None:
+        super().__init__()
+        self.http_response = http_response
+        self._raw_iterator = raw_iterator
+        self._line_type = line_type
+        self._iterator = self.__decode__()
+
+    async def close(self) -> None:
+        """Close the response body stream.
+
+        This is called automatically if you consume the entire stream.
+        """
+        await self.http_response.aclose()
+
+    async def __decode__(self) -> AsyncIterator[_T]:
+        buf = b""
+        async for chunk in self._raw_iterator:
+            for line in chunk.splitlines(keepends=True):
+                buf += line
+                if buf.endswith((b"\r", b"\n", b"\r\n")):
+                    yield construct_type_unchecked(
+                        value=json.loads(buf),
+                        type_=self._line_type,
+                    )
+                    buf = b""
+
+        # flush
+        if buf:
+            yield construct_type_unchecked(
+                value=json.loads(buf),
+                type_=self._line_type,
+            )
+
+    async def __anext__(self) -> _T:
+        return await self._iterator.__anext__()
+
+    async def __aiter__(self) -> AsyncIterator[_T]:
+        async for item in self._iterator:
+            yield item

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_utils/__init__.py

@@ -0,0 +1,64 @@
+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,
+    strip_not_given as strip_not_given,
+    deepcopy_minimal as deepcopy_minimal,
+    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,
+)
+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

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_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)

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_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

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_utils/_httpx.py

@@ -0,0 +1,64 @@
+"""
+This file includes code adapted from HTTPX's utility module
+(https://github.com/encode/httpx/blob/336204f0121a9aefdebac5cacd81f912bafe8057/httpx/_utils.py).
+We implement custom proxy handling to support configurations like `socket_options`,
+which are not currently configurable through the HTTPX client.
+For more context, see: https://github.com/encode/httpx/discussions/3514
+"""
+
+from __future__ import annotations
+
+import ipaddress
+from typing import Mapping
+from urllib.request import getproxies
+
+
+def is_ipv4_hostname(hostname: str) -> bool:
+    try:
+        ipaddress.IPv4Address(hostname.split("/")[0])
+    except Exception:
+        return False
+    return True
+
+
+def is_ipv6_hostname(hostname: str) -> bool:
+    try:
+        ipaddress.IPv6Address(hostname.split("/")[0])
+    except Exception:
+        return False
+    return True
+
+
+def get_environment_proxies() -> Mapping[str, str | None]:
+    """
+    Gets the proxy mappings based on environment variables.
+    We use our own logic to parse these variables, as HTTPX
+    doesn’t allow full configuration of the underlying
+    transport when proxies are set via environment variables.
+    """
+
+    proxy_info = getproxies()
+    mounts: dict[str, str | None] = {}
+
+    for scheme in ("http", "https", "all"):
+        if proxy_info.get(scheme):
+            hostname = proxy_info[scheme]
+            mounts[f"{scheme}://"] = hostname if "://" in hostname else f"http://{hostname}"
+
+    no_proxy_hosts = [host.strip() for host in proxy_info.get("no", "").split(",")]
+    for hostname in no_proxy_hosts:
+        if hostname == "*":
+            return {}
+        elif hostname:
+            if "://" in hostname:
+                mounts[hostname] = None
+            elif is_ipv4_hostname(hostname):
+                mounts[f"all://{hostname}"] = None
+            elif is_ipv6_hostname(hostname):
+                mounts[f"all://[{hostname}]"] = None
+            elif hostname.lower() == "localhost":
+                mounts[f"all://{hostname}"] = None
+            else:
+                mounts[f"all://*{hostname}"] = None
+
+    return mounts

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_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)

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_utils/_logs.py

@@ -0,0 +1,25 @@
+import os
+import logging
+
+logger: logging.Logger = logging.getLogger("anthropic")
+httpx_logger: logging.Logger = logging.getLogger("httpx")
+
+
+def _basic_config() -> None:
+    # e.g. [2023-10-05 14:12:26 - anthropic._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("ANTHROPIC_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)

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_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: ...

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_utils/_reflection.py

@@ -0,0 +1,42 @@
+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(),
+) -> 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:\n\n" + "\n\n".join(errors))

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_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 `anthropic.resources` module.
+
+    This is used so that we can lazily import `anthropic.resources` only when
+    needed *and* so that users can just import `anthropic` and reference `anthropic.resources`
+    """
+
+    @override
+    def __load__(self) -> Any:
+        import importlib
+
+        mod = importlib.import_module("anthropic.resources")
+        return mod
+
+
+resources = ResourcesProxy().__as_proxied__()

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_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:
+        ...

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_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

data/conda_envs/evalclaw/lib/python3.11/site-packages/anthropic/_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)