




"""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,
    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__  		=		logging.get_logger(__name__)






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    ["""pixel_values"""]
			def __init__(						self						,   lowercase = True						,   lowercase = None						,   lowercase = PILImageResampling.BILINEAR						,   lowercase = True						,   lowercase = None						,   lowercase = True						,   lowercase = 1 / 255						,   lowercase = True						,   lowercase = None						,   lowercase = None						,   **lowercase						,   ):
							super().__init__(**lowercase						)
							_lowerCamelCase  :    List[Any]					=					size if size is not None else {'shortest_edge': 256}
							_lowerCamelCase  :    Optional[int]					=					get_size_dict(lowercase						,   default_to_square=lowercase						)
							_lowerCamelCase  :    Dict					=					crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_lowerCamelCase  :    Dict					=					get_size_dict(lowercase						)
							_lowerCamelCase  :    Any					=					do_resize
							_lowerCamelCase  :    int					=					size
							_lowerCamelCase  :    Dict					=					resample
							_lowerCamelCase  :    Union[str, Any]					=					do_center_crop
							_lowerCamelCase  :    Optional[int]					=					crop_size
							_lowerCamelCase  :    str					=					do_rescale
							_lowerCamelCase  :    int					=					rescale_factor
							_lowerCamelCase  :    str					=					do_normalize
							_lowerCamelCase  :    List[Any]					=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							_lowerCamelCase  :    Tuple					=					image_std if image_std is not None else IMAGENET_STANDARD_STD
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = PILImageResampling.BICUBIC						,   lowercase = None						,   **lowercase						,   ):
							_lowerCamelCase  :    List[Any]					=					get_size_dict(lowercase						,   default_to_square=lowercase						)
							if "shortest_edge" not in size:
											raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
							_lowerCamelCase  :    Any					=					get_resize_output_image_size(lowercase						,   size=size['shortest_edge']						,   default_to_square=lowercase						)
							return resize(lowercase						,   size=lowercase						,   resample=lowercase						,   data_format=lowercase						,   **lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = None						,   **lowercase						,   ):
							_lowerCamelCase  :    List[Any]					=					get_size_dict(lowercase						)
							return center_crop(lowercase						,   size=(size['height'], size['width'])						,   data_format=lowercase						,   **lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = None						,   **lowercase						):
							return rescale(lowercase						,   scale=lowercase						,   data_format=lowercase						,   **lowercase						)
			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase = None						,   **lowercase						,   ):
							return normalize(lowercase						,   mean=lowercase						,   std=lowercase						,   data_format=lowercase						,   **lowercase						)






			def       A_					(						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						,   ):
							_lowerCamelCase  :    str					=					do_resize if do_resize is not None else self.do_resize
							_lowerCamelCase  :    Dict					=					size if size is not None else self.size
							_lowerCamelCase  :    List[Any]					=					get_size_dict(lowercase						,   default_to_square=lowercase						)
							_lowerCamelCase  :    int					=					resample if resample is not None else self.resample
							_lowerCamelCase  :    int					=					do_center_crop if do_center_crop is not None else self.do_center_crop
							_lowerCamelCase  :    Union[str, Any]					=					crop_size if crop_size is not None else self.crop_size
							_lowerCamelCase  :    List[Any]					=					get_size_dict(lowercase						)
							_lowerCamelCase  :    List[str]					=					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  :    Optional[Any]					=					image_mean if image_mean is not None else self.image_mean
							_lowerCamelCase  :    Tuple					=					image_std if image_std is not None else self.image_std

							_lowerCamelCase  :    Any					=					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:
											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.
							_lowerCamelCase  :    Optional[Any]					=					[to_numpy_array(lowercase						) for image in images]

							if do_resize:
											_lowerCamelCase  :    Any					=					[self.resize(image=lowercase						,   size=lowercase						,   resample=lowercase						) for image in images]

							if do_center_crop:
											_lowerCamelCase  :    Any					=					[self.center_crop(image=lowercase						,   size=lowercase						) for image in images]

							if do_rescale:
											_lowerCamelCase  :    List[Any]					=					[self.rescale(image=lowercase						,   scale=lowercase						) for image in images]

							if do_normalize:
											_lowerCamelCase  :    Union[str, Any]					=					[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  :    List[str]					=					{'pixel_values': images}
							return BatchFeature(data=lowercase						,   tensor_type=lowercase						)





"""simple docstring"""






def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    Optional[int]					=					int(lowercase__	)
				if decimal in (0, 1):  # Exit cases for the recursion
								return str(lowercase__	)
				_lowerCamelCase,				_lowerCamelCase  :    Dict					=					divmod(lowercase__							,					2	)
				return binary_recursive(lowercase__	) + str(lowercase__	)







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[Any]					=					str(lowercase__	).strip()
				if not number:
								raise ValueError('No input value was provided'	)
				_lowerCamelCase  :    str					=					'-' if number.startswith('-'	) else ''
				_lowerCamelCase  :    Union[str, Any]					=					number.lstrip('-'	)
				if not number.isnumeric():
								raise ValueError('Input value is not an integer'	)
				return f'''{negative}0b{binary_recursive(int(lowercase__	)	)}'''


if __name__ == "__main__":
			from doctest import testmod

			testmod()