

"""simple docstring"""





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


_UpperCamelCase							=      NewType('DataClass', Any)
_UpperCamelCase							=      NewType('DataClassType', Any)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							if isinstance(_lowercase    ,						_lowercase   ):
														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 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							list   ):


							'''simple docstring'''







							__lowerCamelCase      :      Dict         ={str(_lowercase   ): choice for choice in choices}
							return lambda SCREAMING_SNAKE_CASE   : str_to_choice.get(_lowercase    ,						_lowercase   )



def 				lowerCAmelCase_			(      *,
    SCREAMING_SNAKE_CASE       :							Union[str, List[str]] = None    ,						SCREAMING_SNAKE_CASE       :							str = None    ,						SCREAMING_SNAKE_CASE       :							Any = dataclasses.MISSING    ,						SCREAMING_SNAKE_CASE       :							Callable[[], Any] = dataclasses.MISSING    ,						SCREAMING_SNAKE_CASE       :							dict = None    ,						**SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						):


							'''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
														__lowerCamelCase      :      Tuple         ={}
							if aliases is not None:
														__lowerCamelCase      :      Union[str, Any]         =aliases
							if help is not None:
														__lowerCamelCase      :      Optional[Any]         =help

							return dataclasses.field(metadata=_lowercase    ,						default=_lowercase    ,						default_factory=_lowercase    ,						**_lowercase   )
class 		SCREAMING_SNAKE_CASE_							(    a__   ):
				"""simple docstring"""
				__snake_case   :       Iterable[DataClassType]




				def __init__(     self  :List[Any]						,    __lowercase  :Any						,    **__lowercase  :int			):
											# To make the default appear when using --help
											if "formatter_class" not in kwargs:
																		__lowerCamelCase      :      Any         =ArgumentDefaultsHelpFormatter
											super().__init__(**lowerCAmelCase__			)
											if dataclasses.is_dataclass(lowerCAmelCase__			):
																		__lowerCamelCase      :      Dict         =[dataclass_types]
											__lowerCamelCase      :      List[str]         =list(lowerCAmelCase__			)
											for dtype in self.dataclass_types:
																		self._add_dataclass_arguments(lowerCAmelCase__			)




				@staticmethod
				def 					__lowercase  (     __lowercase  :Tuple						,    __lowercase  :Dict			):
											__lowerCamelCase      :      int         =f'--{field.name}'
											__lowerCamelCase      :      List[Any]         =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						,    lowerCAmelCase__			):
																		raise RuntimeError(
																		    '''Unresolved type detected, which should have been done with the help of '''
																		    '''`typing.get_type_hints` method by default'''			)

											__lowerCamelCase      :      Dict         =kwargs.pop('''aliases'''						,    []			)
											if isinstance(lowerCAmelCase__						,    lowerCAmelCase__			):
																		__lowerCamelCase      :      Optional[Any]         =[aliases]

											__lowerCamelCase      :      Union[str, Any]         =getattr(field.type						,    '''__origin__'''						,    field.type			)
											if origin_type is Union or (hasattr(lowerCAmelCase__						,    '''UnionType'''			) and isinstance(lowerCAmelCase__						,    types.UnionType			)):
																		if str not in field.type.__args__ and (
																		    len(field.type.__args__			) != 2 or type(lowerCAmelCase__			) 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(lowerCAmelCase__			) not in field.type.__args__:
																									# filter `str` in Union
																									__lowerCamelCase      :      List[Any]         =field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																									__lowerCamelCase      :      str         =getattr(field.type						,    '''__origin__'''						,    field.type			)
																		elif bool not in field.type.__args__:
																									# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																									__lowerCamelCase      :      str         =(
																									    field.type.__args__[0] if isinstance(lowerCAmelCase__						,    field.type.__args__[1]			) else field.type.__args__[1]
																									)
																									__lowerCamelCase      :      Union[str, Any]         =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)
											__lowerCamelCase      :      List[Any]         ={}
											if origin_type is Literal or (isinstance(field.type						,    lowerCAmelCase__			) and issubclass(field.type						,    lowerCAmelCase__			)):
																		if origin_type is Literal:
																									__lowerCamelCase      :      Union[str, Any]         =field.type.__args__
																		else:
																									__lowerCamelCase      :      Optional[int]         =[x.value for x in field.type]

																		__lowerCamelCase      :      List[Any]         =make_choice_type_function(kwargs['''choices''']			)

																		if field.default is not dataclasses.MISSING:
																									__lowerCamelCase      :      Any         =field.default
																		else:
																									__lowerCamelCase      :      Optional[int]         =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
																		__lowerCamelCase      :      List[Any]         =copy(lowerCAmelCase__			)

																		# Hack because type=bool in argparse does not behave as we want.
																		__lowerCamelCase      :      Any         =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.
																									__lowerCamelCase      :      int         =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
																									__lowerCamelCase      :      List[Any]         =default
																									# This tells argparse we accept 0 or 1 value after --field_name
																									__lowerCamelCase      :      str         ="?"
																									# This is the value that will get picked if we do --field_name (without value)
																									__lowerCamelCase      :      Optional[Any]         =True
											elif isclass(lowerCAmelCase__			) and issubclass(lowerCAmelCase__						,    lowerCAmelCase__			):
																		__lowerCamelCase      :      int         =field.type.__args__[0]
																		__lowerCamelCase      :      str         ="+"
																		if field.default_factory is not dataclasses.MISSING:
																									__lowerCamelCase      :      Tuple         =field.default_factory()
																		elif field.default is dataclasses.MISSING:
																									__lowerCamelCase      :      List[str]         =True
											else:
																		__lowerCamelCase      :      str         =field.type
																		if field.default is not dataclasses.MISSING:
																									__lowerCamelCase      :      Union[str, Any]         =field.default
																		elif field.default_factory is not dataclasses.MISSING:
																									__lowerCamelCase      :      Optional[int]         =field.default_factory()
																		else:
																									__lowerCamelCase      :      Tuple         =True
											parser.add_argument(lowerCAmelCase__						,    *lowerCAmelCase__						,    **lowerCAmelCase__			)

											# 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]):
																		__lowerCamelCase      :      Dict         =False
																		parser.add_argument(f'--no_{field.name}'						,    action='''store_false'''						,    dest=field.name						,    **lowerCAmelCase__			)




				def 					__lowercase  (     self  :str						,    __lowercase  :str			):
											if hasattr(lowerCAmelCase__						,    '''_argument_group_name'''			):
																		__lowerCamelCase      :      Any         =self.add_argument_group(dtype._argument_group_name			)
											else:
																		__lowerCamelCase      :      Optional[Any]         =self

											try:
																		__lowerCamelCase      :      Dict[str, type]         =get_type_hints(lowerCAmelCase__			)
											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(lowerCAmelCase__			):
																									__lowerCamelCase      :      List[Any]         =".".join(map(lowerCAmelCase__						,    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(lowerCAmelCase__			):
																		if not field.init:
																									continue
																		__lowerCamelCase      :      Any         =type_hints[field.name]
																		self._parse_dataclass_field(lowerCAmelCase__						,    lowerCAmelCase__			)




				def 					__lowercase  (     self  :List[str]						,    __lowercase  :Optional[Any]=None						,    __lowercase  :Optional[Any]=False						,    __lowercase  :List[str]=True						,    __lowercase  :Union[str, Any]=None						,    __lowercase  :Union[str, Any]=None						,    ):

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

																		if args_filename:
																									args_files.append(Path(lowerCAmelCase__			)			)
																		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
																									__lowerCamelCase      :      int         =ArgumentParser()
																									args_file_parser.add_argument(lowerCAmelCase__						,    type=lowerCAmelCase__						,    action='''append'''			)

																									# Use only remaining args for further parsing (remove the args_file_flag)
																									__lowerCamelCase      :      Dict         =args_file_parser.parse_known_args(args=lowerCAmelCase__			)
																									__lowerCamelCase      :      Optional[int]         =vars(lowerCAmelCase__			).get(args_file_flag.lstrip('''-'''			)						,    lowerCAmelCase__			)

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

																		__lowerCamelCase      :      Union[str, Any]         =[]
																		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
																		__lowerCamelCase      :      Any         =file_args + args if args is not None else file_args + sys.argv[1:]
											__lowerCamelCase      :      List[str]         =self.parse_known_args(args=lowerCAmelCase__			)
											__lowerCamelCase      :      Dict         =[]
											for dtype in self.dataclass_types:
																		__lowerCamelCase      :      Any         ={f.name for f in dataclasses.fields(lowerCAmelCase__			) if f.init}
																		__lowerCamelCase      :      Any         ={k: v for k, v in vars(lowerCAmelCase__			).items() if k in keys}
																		for k in keys:
																									delattr(lowerCAmelCase__						,    lowerCAmelCase__			)
																		__lowerCamelCase      :      Any         =dtype(**lowerCAmelCase__			)
																		outputs.append(lowerCAmelCase__			)
											if len(namespace.__dict__			) > 0:
																		# additional namespace.
																		outputs.append(lowerCAmelCase__			)
											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 					__lowercase  (     self  :List[str]						,    __lowercase  :Union[str, Any]						,    __lowercase  :Any = False			):
											__lowerCamelCase      :      Union[str, Any]         =set(args.keys()			)
											__lowerCamelCase      :      List[str]         =[]
											for dtype in self.dataclass_types:
																		__lowerCamelCase      :      int         ={f.name for f in dataclasses.fields(lowerCAmelCase__			) if f.init}
																		__lowerCamelCase      :      Tuple         ={k: v for k, v in args.items() if k in keys}
																		unused_keys.difference_update(inputs.keys()			)
																		__lowerCamelCase      :      List[str]         =dtype(**lowerCAmelCase__			)
																		outputs.append(lowerCAmelCase__			)
											if not allow_extra_keys and unused_keys:
																		raise ValueError(f'Some keys are not used by the HfArgumentParser: {sorted(lowerCAmelCase__			)}'			)
											return tuple(lowerCAmelCase__			)




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :List[Any]						,    __lowercase  :Dict = False			):
											with open(Path(lowerCAmelCase__			)						,    encoding='''utf-8'''			) as open_json_file:
																		__lowerCamelCase      :      List[Any]         =json.loads(open_json_file.read()			)
											__lowerCamelCase      :      Optional[Any]         =self.parse_dict(lowerCAmelCase__						,    allow_extra_keys=lowerCAmelCase__			)
											return tuple(lowerCAmelCase__			)




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :Union[str, Any]						,    __lowercase  :Tuple = False			):
											__lowerCamelCase      :      int         =self.parse_dict(yaml.safe_load(Path(lowerCAmelCase__			).read_text()			)						,    allow_extra_keys=lowerCAmelCase__			)
											return tuple(lowerCAmelCase__			)











"""simple docstring"""





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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


_UpperCamelCase							=      logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       List[str]	 =		["""pixel_values"""]




				def __init__(     self  :List[str]						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :bool = True						,    __lowercase  :Dict[str, int] = None						,    __lowercase  :Union[int, float] = 1 / 255						,    __lowercase  :bool = True						,    __lowercase  :bool = True						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    __lowercase  :Optional[Union[float, List[float]]] = None						,    **__lowercase  :Union[str, Any]						,    ):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Dict         =size if size is not None else {'''height''': 256, '''width''': 256}
											__lowerCamelCase      :      Union[str, Any]         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      Tuple         =crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											__lowerCamelCase      :      int         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      Tuple         =do_resize
											__lowerCamelCase      :      int         =size
											__lowerCamelCase      :      Union[str, Any]         =resample
											__lowerCamelCase      :      Dict         =do_center_crop
											__lowerCamelCase      :      Any         =crop_size
											__lowerCamelCase      :      Dict         =do_rescale
											__lowerCamelCase      :      List[Any]         =rescale_factor
											__lowerCamelCase      :      Any         =do_normalize
											__lowerCamelCase      :      int         =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase      :      List[str]         =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 					__lowercase  (     self  :Union[str, Any]						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :PILImageResampling = PIL.Image.BICUBIC						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[int]						,    ):
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return resize(
											    __lowercase						,    size=(size['''height'''], size['''width'''])						,    resample=__lowercase						,    data_format=__lowercase						,    **__lowercase			)




				def 					__lowercase  (     self  :Any						,    __lowercase  :np.ndarray						,    __lowercase  :Dict[str, int]						,    __lowercase  :Optional[Union[str, ChannelDimension]] = None						,    **__lowercase  :Optional[Any]						,    ):
											__lowerCamelCase      :      Dict         =get_size_dict(__lowercase			)
											if "height" not in size or "width" not in size:
																		raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'			)
											return center_crop(__lowercase						,    size=(size['''height'''], size['''width'''])						,    data_format=__lowercase						,    **__lowercase			)




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




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




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

											__lowerCamelCase      :      Dict         =size if size is not None else self.size
											__lowerCamelCase      :      int         =get_size_dict(__lowercase			)
											__lowerCamelCase      :      List[Any]         =crop_size if crop_size is not None else self.crop_size
											__lowerCamelCase      :      Optional[int]         =get_size_dict(__lowercase						,    param_name='''crop_size'''			)

											__lowerCamelCase      :      int         =make_list_of_images(__lowercase			)

											if not valid_images(__lowercase			):
																		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_center_crop and crop_size is None:
																		raise ValueError('''Crop size must be specified if do_center_crop 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.'''			)

											# All transformations expect numpy arrays.
											__lowerCamelCase      :      str         =[to_numpy_array(__lowercase			) for image in images]

											if do_resize:
																		__lowerCamelCase      :      Union[str, Any]         =[self.resize(image=__lowercase						,    size=__lowercase						,    resample=__lowercase			) for image in images]

											if do_center_crop:
																		__lowerCamelCase      :      Optional[Any]         =[self.center_crop(image=__lowercase						,    size=__lowercase			) for image in images]

											if do_rescale:
																		__lowerCamelCase      :      str         =[self.rescale(image=__lowercase						,    scale=__lowercase			) for image in images]

											if do_normalize:
																		__lowerCamelCase      :      int         =[self.normalize(image=__lowercase						,    mean=__lowercase						,    std=__lowercase			) for image in images]

											__lowerCamelCase      :      Tuple         =[to_channel_dimension_format(__lowercase						,    __lowercase			) for image in images]

											__lowerCamelCase      :      Any         ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase						,    tensor_type=__lowercase			)




