


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



def 			lowerCAmelCase (		lowerCAmelCase_ )-> Optional[Any]:
				if isinstance(lowerCAmelCase_ ,   lowerCAmelCase_ ):
								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 (		lowerCAmelCase_ )-> Callable[[str], Any]:
				lowerCAmelCase_ :      Union[str, Any]    				=				{str(lowerCAmelCase_ ): choice for choice in choices}
				return lambda lowerCAmelCase_ : str_to_choice.get(lowerCAmelCase_ ,   lowerCAmelCase_ )



def 			lowerCAmelCase (		*,
    lowerCAmelCase_ = None ,   lowerCAmelCase_ = None ,   lowerCAmelCase_ = dataclasses.MISSING ,   lowerCAmelCase_ = dataclasses.MISSING ,   lowerCAmelCase_ = None ,   **lowerCAmelCase_ ,   )-> dataclasses.Field:
				if metadata is None:
								# Important, don't use as default param in function signature because dict is mutable and shared across function calls
								lowerCAmelCase_ :      str    				=				{}
				if aliases is not None:
								lowerCAmelCase_ :      Any    				=				aliases
				if help is not None:
								lowerCAmelCase_ :      Union[str, Any]    				=				help

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







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Iterable[DataClassType]




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




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

										lowerCAmelCase_ :      List[Any]    				=				kwargs.pop('''aliases'''   ,						[]							)
										if isinstance(__lowercase   ,						__lowercase							):
														lowerCAmelCase_ :      Any    				=				[aliases]

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

														lowerCAmelCase_ :      Dict    				=				make_choice_type_function(kwargs['''choices''']							)

														if field.default is not dataclasses.MISSING:
																		lowerCAmelCase_ :      Tuple    				=				field.default
														else:
																		lowerCAmelCase_ :      List[str]    				=				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(__lowercase							)

														# Hack because type=bool in argparse does not behave as we want.
														lowerCAmelCase_ :      Optional[int]    				=				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_ :      Union[str, 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_ :      Dict    				=				True
										elif isclass(__lowercase							) and issubclass(__lowercase   ,						__lowercase							):
														lowerCAmelCase_ :      Optional[int]    				=				field.type.__args__[0]
														lowerCAmelCase_ :      int    				=				'''+'''
														if field.default_factory is not dataclasses.MISSING:
																		lowerCAmelCase_ :      Tuple    				=				field.default_factory()
														elif field.default is dataclasses.MISSING:
																		lowerCAmelCase_ :      Optional[Any]    				=				True
										else:
														lowerCAmelCase_ :      Dict    				=				field.type
														if field.default is not dataclasses.MISSING:
																		lowerCAmelCase_ :      int    				=				field.default
														elif field.default_factory is not dataclasses.MISSING:
																		lowerCAmelCase_ :      str    				=				field.default_factory()
														else:
																		lowerCAmelCase_ :      int    				=				True
										parser.add_argument(__lowercase   ,						*__lowercase   ,						**__lowercase							)

										# 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_ :      Optional[Any]    				=				False
														parser.add_argument(f"""--no_{field.name}"""   ,						action='''store_false'''   ,						dest=field.name   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase							)					->    List[Any]:
										if hasattr(__lowercase   ,						'''_argument_group_name'''							):
														lowerCAmelCase_ :      str    				=				self.add_argument_group(dtype._argument_group_name							)
										else:
														lowerCAmelCase_ :      Any    				=				self

										try:
														lowerCAmelCase_ :      Dict[str, type]    				=				get_type_hints(__lowercase							)
										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, 1_0) and "unsupported operand type(s) for |" in str(__lowercase							):
																		lowerCAmelCase_ :      Union[str, Any]    				=				'''.'''.join(map(__lowercase   ,						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(__lowercase							):
														if not field.init:
																		continue
														lowerCAmelCase_ :      List[str]    				=				type_hints[field.name]
														self._parse_dataclass_field(__lowercase   ,						__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase=None   ,						__lowercase=False   ,						__lowercase=True   ,						__lowercase=None   ,						__lowercase=None   ,						)					->    Tuple[DataClass, ...]:

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

														if args_filename:
																		args_files.append(Path(__lowercase							)							)
														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_ :      Optional[Any]    				=				ArgumentParser()
																		args_file_parser.add_argument(__lowercase   ,						type=__lowercase   ,						action='''append'''							)

																		# Use only remaining args for further parsing (remove the args_file_flag)
																		lowerCAmelCase_				,  lowerCAmelCase_ :      int    				=				args_file_parser.parse_known_args(args=__lowercase							)
																		lowerCAmelCase_ :      List[Any]    				=				vars(__lowercase							).get(args_file_flag.lstrip('''-'''							)   ,						__lowercase							)

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

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




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = False							)					->    Tuple[DataClass, ...]:
										with open(Path(__lowercase							)   ,						encoding='''utf-8'''							) as open_json_file:
														lowerCAmelCase_ :      List[str]    				=				json.loads(open_json_file.read()							)
										lowerCAmelCase_ :      List[str]    				=				self.parse_dict(__lowercase   ,						allow_extra_keys=__lowercase							)
										return tuple(__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = False							)					->    Tuple[DataClass, ...]:
										lowerCAmelCase_ :      Optional[int]    				=				self.parse_dict(yaml.safe_load(Path(__lowercase							).read_text()							)   ,						allow_extra_keys=__lowercase							)
										return tuple(__lowercase							)



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,
    get_resize_output_image_size,
    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			:  Any     =logging.get_logger(__name__)







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Dict					=							["""pixel_values"""]




						def __init__(				self   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = PILImageResampling.BILINEAR   ,						__lowercase = True   ,						__lowercase = 1 / 2_5_5   ,						__lowercase = True   ,						__lowercase = None   ,						__lowercase = None   ,						**__lowercase   ,						)					->    None:
										super().__init__(**__lowercase							)
										lowerCAmelCase_ :      Dict    				=				size if size is not None else {'''shortest_edge''': 3_8_4}
										lowerCAmelCase_ :      Optional[Any]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)

										lowerCAmelCase_ :      List[Any]    				=				do_resize
										lowerCAmelCase_ :      Optional[int]    				=				size
										# Default value set here for backwards compatibility where the value in config is None
										lowerCAmelCase_ :      str    				=				crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
										lowerCAmelCase_ :      Tuple    				=				resample
										lowerCAmelCase_ :      Optional[int]    				=				do_rescale
										lowerCAmelCase_ :      Any    				=				rescale_factor
										lowerCAmelCase_ :      List[str]    				=				do_normalize
										lowerCAmelCase_ :      str    				=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowerCAmelCase_ :      Dict    				=				image_std if image_std is not None else IMAGENET_STANDARD_STD




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = PILImageResampling.BICUBIC   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										lowerCAmelCase_ :      Optional[Any]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)
										if "shortest_edge" not in size:
														raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""							)
										lowerCAmelCase_ :      Optional[int]    				=				size['''shortest_edge''']

										if shortest_edge < 3_8_4:
														# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
														lowerCAmelCase_ :      Optional[Any]    				=				int(shortest_edge / crop_pct							)
														lowerCAmelCase_ :      List[str]    				=				get_resize_output_image_size(__lowercase   ,						size=__lowercase   ,						default_to_square=__lowercase							)
														lowerCAmelCase_ :      List[Any]    				=				resize(image=__lowercase   ,						size=__lowercase   ,						resample=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)
														# then crop to (shortest_edge, shortest_edge)
														return center_crop(image=__lowercase   ,						size=(shortest_edge, shortest_edge)   ,						data_format=__lowercase   ,						**__lowercase							)
										else:
														# warping (no cropping) when evaluated at 384 or larger
														return resize(
														    __lowercase   ,						size=(shortest_edge, shortest_edge)   ,						resample=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase   ,						)					->    Any:
										return rescale(__lowercase   ,						scale=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase = None   ,						**__lowercase   ,						)					->    np.ndarray:
										return normalize(__lowercase   ,						mean=__lowercase   ,						std=__lowercase   ,						data_format=__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = None   ,						__lowercase = ChannelDimension.FIRST   ,						**__lowercase   ,						)					->    PIL.Image.Image:
										lowerCAmelCase_ :      Optional[int]    				=				do_resize if do_resize is not None else self.do_resize
										lowerCAmelCase_ :      Any    				=				crop_pct if crop_pct is not None else self.crop_pct
										lowerCAmelCase_ :      str    				=				resample if resample is not None else self.resample
										lowerCAmelCase_ :      Union[str, Any]    				=				do_rescale if do_rescale is not None else self.do_rescale
										lowerCAmelCase_ :      str    				=				rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCAmelCase_ :      Any    				=				do_normalize if do_normalize is not None else self.do_normalize
										lowerCAmelCase_ :      str    				=				image_mean if image_mean is not None else self.image_mean
										lowerCAmelCase_ :      int    				=				image_std if image_std is not None else self.image_std

										lowerCAmelCase_ :      int    				=				size if size is not None else self.size
										lowerCAmelCase_ :      List[str]    				=				get_size_dict(__lowercase   ,						default_to_square=__lowercase							)

										lowerCAmelCase_ :      Tuple    				=				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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
														raise ValueError('''crop_pct must be specified if size < 384.'''							)

										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_ :      Optional[Any]    				=				[to_numpy_array(__lowercase							) for image in images]

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

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

										if do_normalize:
														lowerCAmelCase_ :      List[Any]    				=				[self.normalize(image=__lowercase   ,						mean=__lowercase   ,						std=__lowercase							) for image in images]

										lowerCAmelCase_ :      Optional[Any]    				=				[to_channel_dimension_format(__lowercase   ,						__lowercase							) for image in images]

										lowerCAmelCase_ :      Dict    				=				{'''pixel_values''': images}
										return BatchFeature(data=__lowercase   ,						tensor_type=__lowercase							)