


"""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


__UpperCamelCase						=      logging.get_logger(__name__)


if is_vision_available():
	import PIL
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     List[Any]								=     ["""pixel_values"""]







		def __init__(	self						,       lowerCAmelCase__ = True						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = PILImageResampling.BICUBIC						,       lowerCAmelCase__ = True						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = True						,       lowerCAmelCase__ = 1 / 255						,       lowerCAmelCase__ = True						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = True						,       **lowerCAmelCase__						,       )     ->							None:
								super().__init__(**lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					size if size is not None else {'shortest_edge': 224}
								SCREAMING_SNAKE_CASE													=					get_size_dict(lowerCAmelCase__						,       default_to_square=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					crop_size if crop_size is not None else {'height': 224, 'width': 224}
								SCREAMING_SNAKE_CASE													=					get_size_dict(lowerCAmelCase__						,       default_to_square=lowerCAmelCase__						,       param_name='crop_size'				)

								SCREAMING_SNAKE_CASE													=					do_resize
								SCREAMING_SNAKE_CASE													=					size
								SCREAMING_SNAKE_CASE													=					resample
								SCREAMING_SNAKE_CASE													=					do_center_crop
								SCREAMING_SNAKE_CASE													=					crop_size
								SCREAMING_SNAKE_CASE													=					do_rescale
								SCREAMING_SNAKE_CASE													=					rescale_factor
								SCREAMING_SNAKE_CASE													=					do_normalize
								SCREAMING_SNAKE_CASE													=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								SCREAMING_SNAKE_CASE													=					image_std if image_std is not None else OPENAI_CLIP_STD
								SCREAMING_SNAKE_CASE													=					do_convert_rgb







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__ = PILImageResampling.BICUBIC						,       lowerCAmelCase__ = None						,       **lowerCAmelCase__						,       )     ->							np.ndarray:
								SCREAMING_SNAKE_CASE													=					get_size_dict(lowerCAmelCase__						,       default_to_square=lowerCAmelCase__				)
								if "shortest_edge" not in size:
														raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'				)
								SCREAMING_SNAKE_CASE													=					get_resize_output_image_size(lowerCAmelCase__						,       size=size['shortest_edge']						,       default_to_square=lowerCAmelCase__				)
								return resize(lowerCAmelCase__						,       size=lowerCAmelCase__						,       resample=lowerCAmelCase__						,       data_format=lowerCAmelCase__						,       **lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       **lowerCAmelCase__						,       )     ->							np.ndarray:
								SCREAMING_SNAKE_CASE													=					get_size_dict(lowerCAmelCase__				)
								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(lowerCAmelCase__						,       size=(size['height'], size['width'])						,       data_format=lowerCAmelCase__						,       **lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       **lowerCAmelCase__						,       )     ->							Optional[Any]:
								return rescale(lowerCAmelCase__						,       scale=lowerCAmelCase__						,       data_format=lowerCAmelCase__						,       **lowerCAmelCase__				)







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







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = None						,       lowerCAmelCase__ = ChannelDimension.FIRST						,       **lowerCAmelCase__						,       )     ->							PIL.Image.Image:
								SCREAMING_SNAKE_CASE													=					do_resize if do_resize is not None else self.do_resize
								SCREAMING_SNAKE_CASE													=					size if size is not None else self.size
								SCREAMING_SNAKE_CASE													=					get_size_dict(lowerCAmelCase__						,       param_name='size'						,       default_to_square=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					resample if resample is not None else self.resample
								SCREAMING_SNAKE_CASE													=					do_center_crop if do_center_crop is not None else self.do_center_crop
								SCREAMING_SNAKE_CASE													=					crop_size if crop_size is not None else self.crop_size
								SCREAMING_SNAKE_CASE													=					get_size_dict(lowerCAmelCase__						,       param_name='crop_size'						,       default_to_square=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					do_rescale if do_rescale is not None else self.do_rescale
								SCREAMING_SNAKE_CASE													=					rescale_factor if rescale_factor is not None else self.rescale_factor
								SCREAMING_SNAKE_CASE													=					do_normalize if do_normalize is not None else self.do_normalize
								SCREAMING_SNAKE_CASE													=					image_mean if image_mean is not None else self.image_mean
								SCREAMING_SNAKE_CASE													=					image_std if image_std is not None else self.image_std
								SCREAMING_SNAKE_CASE													=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								SCREAMING_SNAKE_CASE													=					make_list_of_images(lowerCAmelCase__				)

								if not valid_images(lowerCAmelCase__				):
														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:
														SCREAMING_SNAKE_CASE													=					[convert_to_rgb(lowerCAmelCase__				) for image in images]

								# All transformations expect numpy arrays.
								SCREAMING_SNAKE_CASE													=					[to_numpy_array(lowerCAmelCase__				) for image in images]

								if do_resize:
														SCREAMING_SNAKE_CASE													=					[self.resize(image=lowerCAmelCase__						,       size=lowerCAmelCase__						,       resample=lowerCAmelCase__				) for image in images]

								if do_center_crop:
														SCREAMING_SNAKE_CASE													=					[self.center_crop(image=lowerCAmelCase__						,       size=lowerCAmelCase__				) for image in images]

								if do_rescale:
														SCREAMING_SNAKE_CASE													=					[self.rescale(image=lowerCAmelCase__						,       scale=lowerCAmelCase__				) for image in images]

								if do_normalize:
														SCREAMING_SNAKE_CASE													=					[self.normalize(image=lowerCAmelCase__						,       mean=lowerCAmelCase__						,       std=lowerCAmelCase__				) for image in images]

								SCREAMING_SNAKE_CASE													=					[to_channel_dimension_format(lowerCAmelCase__						,       lowerCAmelCase__				) for image in images]

								SCREAMING_SNAKE_CASE													=					{'pixel_values': images}
								return BatchFeature(data=lowerCAmelCase__						,       tensor_type=lowerCAmelCase__				)






"""simple docstring"""




def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						str:
						SCREAMING_SNAKE_CASE													=					int(SCREAMING_SNAKE_CASE_  )
						if decimal in (0, 1):  # Exit cases for the recursion
												return str(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					divmod(SCREAMING_SNAKE_CASE_ ,			2  )
						return binary_recursive(SCREAMING_SNAKE_CASE_  ) + str(SCREAMING_SNAKE_CASE_  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						str:
						SCREAMING_SNAKE_CASE													=					str(SCREAMING_SNAKE_CASE_  ).strip()
						if not number:
												raise ValueError('No input value was provided'  )
						SCREAMING_SNAKE_CASE													=					'-' if number.startswith('-'  ) else ''
						SCREAMING_SNAKE_CASE													=					number.lstrip('-'  )
						if not number.isnumeric():
												raise ValueError('Input value is not an integer'  )
						return F'{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE_  )  )}'


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

	testmod()


