
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


_UpperCAmelCase   :					str    =			logging.get_logger(__name__)


class 							lowercase   (						_SCREAMING_SNAKE_CASE  ):
		__lowercase			:			List[Any]      	=							["pixel_values"]



		def __init__(					self							, A_ = True							, A_ = None							, A_ = True							, A_ = None							, A_ = PILImageResampling.BICUBIC							, A_ = True							, A_ = 1 / 255							, A_ = True							, A_ = None							, A_ = None							, **A_							, )    ->   None:
				"""simple docstring"""
				super().__init__(**A_			)
				UpperCamelCase            =	crop_size if crop_size is not None else {'height': 256, 'width': 256}
				UpperCamelCase            =	get_size_dict(A_							, param_name='crop_size'			)
				UpperCamelCase            =	size if size is not None else {'height': 224, 'width': 224}
				UpperCamelCase            =	get_size_dict(A_			)

				UpperCamelCase            =	do_center_crop
				UpperCamelCase            =	crop_size
				UpperCamelCase            =	do_resize
				UpperCamelCase            =	size
				UpperCamelCase            =	resample
				UpperCamelCase            =	do_rescale
				UpperCamelCase            =	rescale_factor
				UpperCamelCase            =	do_normalize
				UpperCamelCase            =	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				UpperCamelCase            =	image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def __UpperCamelCase					(					self							, A_							, A_							, A_ = None							, A_ = None							, **A_							, )    ->   np.ndarray:
				"""simple docstring"""
				UpperCamelCase            =	self.size if size is None else size
				UpperCamelCase            =	get_size_dict(A_			)
				UpperCamelCase            =	get_size_dict(A_							, param_name='crop_size'			)

				UpperCamelCase      ,    UpperCamelCase            =	get_image_size(A_			)
				UpperCamelCase            =	min(A_							, A_			)
				UpperCamelCase            =	(size['height'] / crop_size['height']) * min_dim
				UpperCamelCase            =	(size['width'] / crop_size['width']) * min_dim
				return center_crop(A_							, size=(cropped_height, cropped_width)							, data_format=A_							, **A_			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_ = PIL.Image.BICUBIC							, A_ = None							, **A_							, )    ->   np.ndarray:
				"""simple docstring"""
				UpperCamelCase            =	get_size_dict(A_			)
				if "height" not in size or "width" not in size:
						raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''			)
				return resize(
				    A_							, size=(size['height'], size['width'])							, resample=A_							, data_format=A_							, **A_			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_ = None							, **A_							, )    ->   Any:
				"""simple docstring"""
				return rescale(A_							, scale=A_							, data_format=A_							, **A_			)



		def __UpperCamelCase					(					self							, A_							, A_							, A_							, A_ = None							, **A_							, )    ->   np.ndarray:
				"""simple docstring"""
				return normalize(A_							, mean=A_							, std=A_							, data_format=A_							, **A_			)



		def __UpperCamelCase					(					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_ = ChannelDimension.FIRST							, **A_							, )    ->   PIL.Image.Image:
				"""simple docstring"""
				UpperCamelCase            =	do_center_crop if do_center_crop is not None else self.do_center_crop
				UpperCamelCase            =	crop_size if crop_size is not None else self.crop_size
				UpperCamelCase            =	get_size_dict(A_							, param_name='crop_size'			)
				UpperCamelCase            =	do_resize if do_resize is not None else self.do_resize
				UpperCamelCase            =	size if size is not None else self.size
				UpperCamelCase            =	get_size_dict(A_			)
				UpperCamelCase            =	resample if resample is not None else self.resample
				UpperCamelCase            =	do_rescale if do_rescale is not None else self.do_rescale
				UpperCamelCase            =	rescale_factor if rescale_factor is not None else self.rescale_factor
				UpperCamelCase            =	do_normalize if do_normalize is not None else self.do_normalize
				UpperCamelCase            =	image_mean if image_mean is not None else self.image_mean
				UpperCamelCase            =	image_std if image_std is not None else self.image_std

				UpperCamelCase            =	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_center_crop and crop_size is None:
						raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'			)

				if do_resize and size is None:
						raise ValueError('Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'			)

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

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

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

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

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

				UpperCamelCase            =	[to_channel_dimension_format(A_							, A_			) for image in images]

				UpperCamelCase            =	{'pixel_values': images}
				return BatchFeature(data=A_							, tensor_type=A_			)


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if not isinstance(lowercase       ,			lowercase					):
				UpperCamelCase            =	(
				    'operation can not be conducted on a object of type '
				    f'''{type(lowercase					).__name__}'''
				)
				raise TypeError(lowercase					)
		UpperCamelCase            =	''
		for _ in range(0       ,			32					):
				bit_string += str(number % 2					)
				UpperCamelCase            =	number >> 1
		return bit_string


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if number < 0:
				raise ValueError('the value of input must be positive'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('Input value must be a \'int\' type'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''					)
		UpperCamelCase            =	0
		# iterator over [1 to 32],since we are dealing with 32 bit integer
		for _ in range(1       ,			33					):
				# left shift the bits by unity
				UpperCamelCase            =	result << 1
				# get the end bit
				UpperCamelCase            =	number % 2
				# right shift the bits by unity
				UpperCamelCase            =	number >> 1
				# add that bit to our ans
				UpperCamelCase            =	result | end_bit
		return get_reverse_bit_string(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
