



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, logging


_lowercase						: Union[str, Any]     						=logging.get_logger(__name__)

class   snake_case__							(__lowercase						):







						"""simple docstring"""




						__lowerCAmelCase	:Tuple         =	["pixel_values"]

						def __init__(  self   ,  __lowercase = True   ,  __lowercase = None   ,  __lowercase = PILImageResampling.BILINEAR   ,  __lowercase = True   ,  __lowercase = None   ,  __lowercase = True   ,  __lowercase = 1 / 2_5_5   ,  __lowercase = True   ,  __lowercase = None   ,  __lowercase = None   ,  **__lowercase   ,  )   -> int:
									"""simple docstring"""




									super().__init__(**_A    )
									a__  :		Optional[Any]					=    size if size is not None else {"""shortest_edge""": 2_5_6}
									a__  :		str					=    get_size_dict(_A   ,  default_to_square=_A    )
									a__  :		Any					=    crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
									a__  :		Any					=    get_size_dict(_A    )
									a__  :		Tuple					=    do_resize
									a__  :		int					=    size
									a__  :		List[Any]					=    resample
									a__  :		str					=    do_center_crop
									a__  :		Tuple					=    crop_size
									a__  :		List[Any]					=    do_rescale
									a__  :		Optional[int]					=    rescale_factor
									a__  :		Any					=    do_normalize
									a__  :		Optional[Any]					=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									a__  :		Optional[Any]					=    image_std if image_std is not None else IMAGENET_STANDARD_STD

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase = PILImageResampling.BICUBIC   ,  __lowercase = None   ,  **__lowercase   ,  )   -> Union[str, Any]:
									"""simple docstring"""




									a__  :		List[Any]					=    get_size_dict(_A   ,  default_to_square=_A    )
									if "shortest_edge" not in size:
												raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
									a__  :		List[Any]					=    get_resize_output_image_size(_A   ,  size=size["""shortest_edge"""]   ,  default_to_square=_A    )
									return resize(_A   ,  size=_A   ,  resample=_A   ,  data_format=_A   ,  **_A    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase = None   ,  **__lowercase   ,  )   -> Optional[Any]:
									"""simple docstring"""




									a__  :		str					=    get_size_dict(_A    )
									return center_crop(_A   ,  size=(size["""height"""], size["""width"""])   ,  data_format=_A   ,  **_A    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase = None   ,  **__lowercase    )   -> int:
									"""simple docstring"""




									return rescale(_A   ,  scale=_A   ,  data_format=_A   ,  **_A    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase   ,  __lowercase   ,  __lowercase = None   ,  **__lowercase   ,  )   -> Any:
									"""simple docstring"""




									return normalize(_A   ,  mean=_A   ,  std=_A   ,  data_format=_A   ,  **_A    )

						def 				SCREAMING_SNAKE_CASE__(  self   ,  __lowercase   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = None   ,  __lowercase = ChannelDimension.FIRST   ,  **__lowercase   ,  )   -> Optional[int]:
									"""simple docstring"""




									a__  :		int					=    do_resize if do_resize is not None else self.do_resize
									a__  :		Union[str, Any]					=    size if size is not None else self.size
									a__  :		Any					=    get_size_dict(_A   ,  default_to_square=_A    )
									a__  :		Optional[Any]					=    resample if resample is not None else self.resample
									a__  :		Optional[int]					=    do_center_crop if do_center_crop is not None else self.do_center_crop
									a__  :		List[Any]					=    crop_size if crop_size is not None else self.crop_size
									a__  :		List[Any]					=    get_size_dict(_A    )
									a__  :		List[str]					=    do_rescale if do_rescale is not None else self.do_rescale
									a__  :		Union[str, Any]					=    rescale_factor if rescale_factor is not None else self.rescale_factor
									a__  :		Any					=    do_normalize if do_normalize is not None else self.do_normalize
									a__  :		Any					=    image_mean if image_mean is not None else self.image_mean
									a__  :		List[Any]					=    image_std if image_std is not None else self.image_std

									a__  :		List[str]					=    make_list_of_images(_A    )

									if not valid_images(_A    ):
												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:
												raise ValueError("""Size 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.
									a__  :		str					=    [to_numpy_array(_A    ) for image in images]

									if do_resize:
												a__  :		str					=    [self.resize(image=_A   ,  size=_A   ,  resample=_A    ) for image in images]

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

									if do_rescale:
												a__  :		int					=    [self.rescale(image=_A   ,  scale=_A    ) for image in images]

									if do_normalize:
												a__  :		Dict					=    [self.normalize(image=_A   ,  mean=_A   ,  std=_A    ) for image in images]

									a__  :		Any					=    [to_channel_dimension_format(_A   ,  _A    ) for image in images]

									a__  :		Union[str, Any]					=    {"""pixel_values""": images}
									return BatchFeature(data=_A   ,  tensor_type=_A    )





from typing import List

from .keymap import KEYMAP, get_character
def 					_snake_case	(  __snake_case						):

					def decorator(__snake_case						):
										_UpperCamelCase			     =     getattr(__snake_case  ,     '''handle_key'''  ,     []						)
										handle += [key]
										setattr(__snake_case  ,     '''handle_key'''  ,     __snake_case						)
										return func

					return decorator
def 					_snake_case	(  *__snake_case						):

					def decorator(__snake_case						):
										_UpperCamelCase			     =     getattr(__snake_case  ,     '''handle_key'''  ,     []						)
										handle += keys
										setattr(__snake_case  ,     '''handle_key'''  ,     __snake_case						)
										return func

					return decorator
class     lowerCAmelCase_     (       __lowercase ):







							def __new__(   cls      :     Optional[Any] ,			_A      :     Optional[Any] ,			_A      :     Optional[int] ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     super().__new__(cls ,			_A ,			_A ,			_A		)
												if not hasattr(_A ,			'''key_handler'''		):
																	setattr(_A ,			'''key_handler''' ,			{}		)
												setattr(_A ,			'''handle_input''' ,			KeyHandler.handle_input		)

												for value in attrs.values():
																	_UpperCamelCase			     =     getattr(_A ,			'''handle_key''' ,			[]		)
																	for key in handled_keys:
																						_UpperCamelCase			     =     value
												return new_cls







							@staticmethod
							def  UpperCamelCase_						(   cls      :     str		):
												_UpperCamelCase			     =     get_character()
												if char != KEYMAP["undefined"]:
																	_UpperCamelCase			     =     ord(_A		)
												_UpperCamelCase			     =     cls.key_handler.get(_A		)
												if handler:
																	_UpperCamelCase			     =     char
																	return handler(cls		)
												else:
																	return None




def 					_snake_case	(  cls						):
					return KeyHandler(cls.__name__  ,     cls.__bases__  ,     cls.__dict__.copy()						)
