import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


SCREAMING_SNAKE_CASE			:						Tuple				  =		NewType("""DataClass""", Any)
SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		NewType("""DataClassType""", Any)



def 	__A		(      _A       ):



 """simple docstring"""


 if isinstance(_A				,					_A       ):
  return v
 if v.lower() in ("yes", "true", "t", "y", "1"):
  return True
 elif v.lower() in ("no", "false", "f", "n", "0"):
  return False
 else:
  raise ArgumentTypeError(
      f"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)."""       )



def 	__A		(      _A       ):



 """simple docstring"""


 __a							=	{str(_A       ): choice for choice in choices}
 return lambda _A       : str_to_choice.get(_A				,					_A       )



def 	__A		(      *,
    _A = None				,					_A = None				,					_A = dataclasses.MISSING				,					_A = dataclasses.MISSING				,					_A = None				,					**_A				,					):



 """simple docstring"""


 if metadata is None:
  # Important, don't use as default param in function signature because dict is mutable and shared across function calls
  __a							=	{}
 if aliases is not None:
  __a							=	aliases
 if help is not None:
  __a							=	help

 return dataclasses.field(metadata=_A				,					default=_A				,					default_factory=_A				,					**_A       )






class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      42

  def __init__(		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[DataClassType, Iterable[DataClassType]]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   # To make the default appear when using --help
   if "formatter_class" not in kwargs:
    __a							=	ArgumentDefaultsHelpFormatter
   super().__init__(**__SCREAMING_SNAKE_CASE							)
   if dataclasses.is_dataclass(__SCREAMING_SNAKE_CASE							):
    __a							=	[dataclass_types]
   __a							=	list(__SCREAMING_SNAKE_CASE							)
   for dtype in self.dataclass_types:
    self._add_dataclass_arguments(__SCREAMING_SNAKE_CASE							)

  @staticmethod
  def 		_UpperCAmelCase (		__SCREAMING_SNAKE_CASE			:   ArgumentParser  ,				__SCREAMING_SNAKE_CASE			:   dataclasses.Field							):
   __a							=	f"""--{field.name}"""
   __a							=	field.metadata.copy()
   # field.metadata is not used at all by Data Classes,
   # it is provided as a third-party extension mechanism.
   if isinstance(field.type  ,				__SCREAMING_SNAKE_CASE							):
    raise RuntimeError(
        "Unresolved type detected, which should have been done with the help of "
        "`typing.get_type_hints` method by default"							)

   __a							=	kwargs.pop("aliases"  ,				[]							)
   if isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
    __a							=	[aliases]

   __a							=	getattr(field.type  ,				"__origin__"  ,				field.type							)
   if origin_type is Union or (hasattr(__SCREAMING_SNAKE_CASE  ,				"UnionType"							) and isinstance(__SCREAMING_SNAKE_CASE  ,				types.UnionType							)):
    if str not in field.type.__args__ and (
        len(field.type.__args__							) != 2 or type(__SCREAMING_SNAKE_CASE							) not in field.type.__args__
    ):
     raise ValueError(
         "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
         " the argument parser only supports one type per argument."
         f""" Problem encountered in field '{field.name}'."""							)
    if type(__SCREAMING_SNAKE_CASE							) not in field.type.__args__:
     # filter `str` in Union
     __a							=	field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
     __a							=	getattr(field.type  ,				"__origin__"  ,				field.type							)
    elif bool not in field.type.__args__:
     # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
     __a							=	(
         field.type.__args__[0] if isinstance(__SCREAMING_SNAKE_CASE  ,				field.type.__args__[1]							) else field.type.__args__[1]
     )
     __a							=	getattr(field.type  ,				"__origin__"  ,				field.type							)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
   __a							=	{}
   if origin_type is Literal or (isinstance(field.type  ,				__SCREAMING_SNAKE_CASE							) and issubclass(field.type  ,				__SCREAMING_SNAKE_CASE							)):
    if origin_type is Literal:
     __a							=	field.type.__args__
    else:
     __a							=	[x.value for x in field.type]

    __a							=	make_choice_type_function(kwargs["choices"]							)

    if field.default is not dataclasses.MISSING:
     __a							=	field.default
    else:
     __a							=	True
   elif field.type is bool or field.type == Optional[bool]:
    # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
    # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
    __a							=	copy(__SCREAMING_SNAKE_CASE							)

    # Hack because type=bool in argparse does not behave as we want.
    __a							=	string_to_bool
    if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
     # Default value is False if we have no default when of type bool.
     __a							=	False if field.default is dataclasses.MISSING else field.default
     # This is the value that will get picked if we don't include --field_name in any way
     __a							=	default
     # This tells argparse we accept 0 or 1 value after --field_name
     __a							=	"?"
     # This is the value that will get picked if we do --field_name (without value)
     __a							=	True
   elif isclass(__SCREAMING_SNAKE_CASE							) and issubclass(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
    __a							=	field.type.__args__[0]
    __a							=	"+"
    if field.default_factory is not dataclasses.MISSING:
     __a							=	field.default_factory()
    elif field.default is dataclasses.MISSING:
     __a							=	True
   else:
    __a							=	field.type
    if field.default is not dataclasses.MISSING:
     __a							=	field.default
    elif field.default_factory is not dataclasses.MISSING:
     __a							=	field.default_factory()
    else:
     __a							=	True
   parser.add_argument(__SCREAMING_SNAKE_CASE  ,				*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
   # Order is important for arguments with the same destination!
   # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
   # here and we do not need those changes/additional keys.
   if field.default is True and (field.type is bool or field.type == Optional[bool]):
    __a							=	False
    parser.add_argument(f"""--no_{field.name}"""  ,				action="store_false"  ,				dest=field.name  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   DataClassType							):
   if hasattr(__SCREAMING_SNAKE_CASE  ,				"_argument_group_name"							):
    __a							=	self.add_argument_group(dtype._argument_group_name							)
   else:
    __a							=	self

   try:
    __a							=	get_type_hints(__SCREAMING_SNAKE_CASE							)
   except NameError:
    raise RuntimeError(
        f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
        "removing line of `from __future__ import annotations` which opts in Postponed "
        "Evaluation of Annotations (PEP 563)"							)
   except TypeError as ex:
    # Remove this block when we drop Python 3.9 support
    if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__SCREAMING_SNAKE_CASE							):
     __a							=	".".join(map(__SCREAMING_SNAKE_CASE  ,				sys.version_info[:3]							)							)
     raise RuntimeError(
         f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
         "line of `from __future__ import annotations` which opts in union types as "
         "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
         "support Python versions that lower than 3.10, you need to use "
         "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
         "`X | None`."							) from ex
    raise

   for field in dataclasses.fields(__SCREAMING_SNAKE_CASE							):
    if not field.init:
     continue
    __a							=	type_hints[field.name]
    self._parse_dataclass_field(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Dict  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=None  ,				__SCREAMING_SNAKE_CASE			:   List[str]=False  ,				__SCREAMING_SNAKE_CASE			:   int=True  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				):

   if args_file_flag or args_filename or (look_for_args_file and len(sys.argv							)):
    __a							=	[]

    if args_filename:
     args_files.append(Path(__SCREAMING_SNAKE_CASE							)							)
    elif look_for_args_file and len(sys.argv							):
     args_files.append(Path(sys.argv[0]							).with_suffix(".args"							)							)

    # args files specified via command line flag should overwrite default args files so we add them last
    if args_file_flag:
     # Create special parser just to extract the args_file_flag values
     __a							=	ArgumentParser()
     args_file_parser.add_argument(__SCREAMING_SNAKE_CASE  ,				type=__SCREAMING_SNAKE_CASE  ,				action="append"							)

     # Use only remaining args for further parsing (remove the args_file_flag)
     __a							,  __a							=	args_file_parser.parse_known_args(args=__SCREAMING_SNAKE_CASE							)
     __a							=	vars(__SCREAMING_SNAKE_CASE							).get(args_file_flag.lstrip("-"							)  ,				__SCREAMING_SNAKE_CASE							)

     if cmd_args_file_paths:
      args_files.extend([Path(__SCREAMING_SNAKE_CASE							) for p in cmd_args_file_paths]							)

    __a							=	[]
    for args_file in args_files:
     if args_file.exists():
      file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
    __a							=	file_args + args if args is not None else file_args + sys.argv[1:]
   __a							,  __a							=	self.parse_known_args(args=__SCREAMING_SNAKE_CASE							)
   __a							=	[]
   for dtype in self.dataclass_types:
    __a							=	{f.name for f in dataclasses.fields(__SCREAMING_SNAKE_CASE							) if f.init}
    __a							=	{k: v for k, v in vars(__SCREAMING_SNAKE_CASE							).items() if k in keys}
    for k in keys:
     delattr(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    __a							=	dtype(**__SCREAMING_SNAKE_CASE							)
    outputs.append(__SCREAMING_SNAKE_CASE							)
   if len(namespace.__dict__							) > 0:
    # additional namespace.
    outputs.append(__SCREAMING_SNAKE_CASE							)
   if return_remaining_strings:
    return (*outputs, remaining_args)
   else:
    if remaining_args:
     raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}"""							)

    return (*outputs,)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   bool = False							):
   __a							=	set(args.keys()							)
   __a							=	[]
   for dtype in self.dataclass_types:
    __a							=	{f.name for f in dataclasses.fields(__SCREAMING_SNAKE_CASE							) if f.init}
    __a							=	{k: v for k, v in args.items() if k in keys}
    unused_keys.difference_update(inputs.keys()							)
    __a							=	dtype(**__SCREAMING_SNAKE_CASE							)
    outputs.append(__SCREAMING_SNAKE_CASE							)
   if not allow_extra_keys and unused_keys:
    raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__SCREAMING_SNAKE_CASE							)}"""							)
   return tuple(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   bool = False							):
   with open(Path(__SCREAMING_SNAKE_CASE							)  ,				encoding="utf-8"							) as open_json_file:
    __a							=	json.loads(open_json_file.read()							)
   __a							=	self.parse_dict(__SCREAMING_SNAKE_CASE  ,				allow_extra_keys=__SCREAMING_SNAKE_CASE							)
   return tuple(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   bool = False							):
   __a							=	self.parse_dict(yaml.safe_load(Path(__SCREAMING_SNAKE_CASE							).read_text()							)  ,				allow_extra_keys=__SCREAMING_SNAKE_CASE							)
   return tuple(__SCREAMING_SNAKE_CASE							)



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.get_logger(__name__)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""pixel_values"""]

  def __init__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float] = 1 / 2_55  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)
   __a							=	size if size is not None else {"height": 3_84, "width": 3_84}
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

   __a							=	do_resize
   __a							=	size
   __a							=	resample
   __a							=	do_rescale
   __a							=	rescale_factor
   __a							=	do_normalize
   __a							=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   __a							=	image_std if image_std is not None else OPENAI_CLIP_STD
   __a							=	do_convert_rgb

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int]  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)
   if "height" not in size or "width" not in size:
    raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""							)
   __a							=	(size["height"], size["width"])
   return resize(__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   return rescale(__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[float, List[float]]  ,				__SCREAMING_SNAKE_CASE			:   Union[float, List[float]]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   int  ,				):
   return normalize(__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   ImageInput  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict[str, int]] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[float] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, TensorType]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = None  ,				__SCREAMING_SNAKE_CASE			:   ChannelDimension = ChannelDimension.FIRST  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __a							=	do_resize if do_resize is not None else self.do_resize
   __a							=	resample if resample is not None else self.resample
   __a							=	do_rescale if do_rescale is not None else self.do_rescale
   __a							=	rescale_factor if rescale_factor is not None else self.rescale_factor
   __a							=	do_normalize if do_normalize is not None else self.do_normalize
   __a							=	image_mean if image_mean is not None else self.image_mean
   __a							=	image_std if image_std is not None else self.image_std
   __a							=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   __a							=	size if size is not None else self.size
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

   __a							=	make_list_of_images(__SCREAMING_SNAKE_CASE							)

   if not valid_images(__SCREAMING_SNAKE_CASE							):
    raise ValueError(
        "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
        "torch.Tensor, tf.Tensor or jax.ndarray."							)

   if do_resize and size is None or resample is None:
    raise ValueError("Size and resample must be specified if do_resize is True."							)

   if do_rescale and rescale_factor is None:
    raise ValueError("Rescale factor must be specified if do_rescale is True."							)

   if do_normalize and (image_mean is None or image_std is None):
    raise ValueError("Image mean and std must be specified if do_normalize is True."							)

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
    __a							=	[convert_to_rgb(__SCREAMING_SNAKE_CASE							) for image in images]

   # All transformations expect numpy arrays.
   __a							=	[to_numpy_array(__SCREAMING_SNAKE_CASE							) for image in images]

   if do_resize:
    __a							=	[self.resize(image=__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE							) for image in images]

   if do_rescale:
    __a							=	[self.rescale(image=__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE							) for image in images]

   if do_normalize:
    __a							=	[self.normalize(image=__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	BatchFeature(data={"pixel_values": images}  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

   return encoded_outputs


