


"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


SCREAMING_SNAKE_CASE :    List[str]									=				logging.get_logger(__name__)


if is_vision_available():
			import PIL



class 			_UpperCAmelCase (							lowercase_				):






		'''simple docstring'''

		lowerCamelCase__				=["""pixel_values"""]



		def __init__(self				,       a_ = True				,       a_ = None				,       a_ = PILImageResampling.BICUBIC				,       a_ = True				,       a_ = None				,       a_ = True				,       a_ = 1 / 2_55				,       a_ = True				,       a_ = None				,       a_ = None				,       a_ = True				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(**a_					)
			__snake_case	:						str						=				size if size is not None else {'''shortest_edge''': 2_24}
			__snake_case	:						int						=				get_size_dict(a_				,       default_to_square=a_					)
			__snake_case	:						Optional[Any]						=				crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
			__snake_case	:						Union[str, Any]						=				get_size_dict(a_				,       default_to_square=a_				,       param_name='''crop_size'''					)

			__snake_case	:						Optional[int]						=				do_resize
			__snake_case	:						List[str]						=				size
			__snake_case	:						Optional[int]						=				resample
			__snake_case	:						Dict						=				do_center_crop
			__snake_case	:						Union[str, Any]						=				crop_size
			__snake_case	:						str						=				do_rescale
			__snake_case	:						Union[str, Any]						=				rescale_factor
			__snake_case	:						List[Any]						=				do_normalize
			__snake_case	:						Any						=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
			__snake_case	:						Optional[int]						=				image_std if image_std is not None else OPENAI_CLIP_STD
			__snake_case	:						Optional[int]						=				do_convert_rgb



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = PILImageResampling.BICUBIC				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						Dict						=				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()}"""					)
			__snake_case	:						Dict						=				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				,       a_				,       a_				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			__snake_case	:						str						=				get_size_dict(a_					)
			if "height" not in size or "width" not in size:
				raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""					)
			return center_crop(a_				,       size=(size['''height'''], size['''width'''])				,       data_format=a_				,       **a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = None				,       a_ = ChannelDimension.FIRST				,       **a_				,       ):





			'''simple docstring'''

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

			__snake_case	:						Dict						=				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.'''					)

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

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

			if do_resize:
				__snake_case	:						Dict						=				[self.resize(image=a_				,       size=a_				,       resample=a_					) for image in images]

			if do_center_crop:
				__snake_case	:						Tuple						=				[self.center_crop(image=a_				,       size=a_					) for image in images]

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

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

			__snake_case	:						List[Any]						=				[to_channel_dimension_format(a_				,       a_					) for image in images]

			__snake_case	:						str						=				{'''pixel_values''': images}
			return BatchFeature(data=a_				,       tensor_type=a_					)









'''simple docstring'''

def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







       if not isinstance(A  , A       ):
              raise TypeError('''only integers accepted as input'''       )
       else:
              lowercase__      			= str(abs(A       )       )
              lowercase__      			= [list(A       ) for char in range(len(A       )       )]
              for index in range(len(A       )       ):
                     num_transpositions[index].pop(A       )
              return max(
                  int(''''''.join(list(A       )       )       ) for transposition in num_transpositions       )


if __name__ == "__main__":
 __import__('doctest').testmod()




