code: 
  
  
  
  
  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
  
  
  SCREAMING_SNAKE_CASE__    :    Any								=					logging.get_logger(__name__)
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   ['''pixel_values''']
  
  
  
  
  
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 1 / 255			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				None:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__(**SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	crop_size if crop_size is not None else {'height': 256, 'width': 256}
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			,    param_name='crop_size'			)
  				SCREAMING_SNAKE_CASE_			=	size if size is not None else {'height': 224, 'width': 224}
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  
  				SCREAMING_SNAKE_CASE_			=	do_center_crop
  				SCREAMING_SNAKE_CASE_			=	crop_size
  				SCREAMING_SNAKE_CASE_			=	do_resize
  				SCREAMING_SNAKE_CASE_			=	size
  				SCREAMING_SNAKE_CASE_			=	resample
  				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 IMAGENET_DEFAULT_MEAN
  				SCREAMING_SNAKE_CASE_			=	image_std if image_std is not None else IMAGENET_DEFAULT_STD
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	self.size if size is None else size
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			,    param_name='crop_size'			)
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	get_image_size(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	min(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	(size['height'] / crop_size['height']) * min_dim
  				SCREAMING_SNAKE_CASE_			=	(size['width'] / crop_size['width']) * min_dim
  				return center_crop(SCREAMING_SNAKE_CASE			,    size=(cropped_height, cropped_width)			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	get_size_dict(SCREAMING_SNAKE_CASE			)
  				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(
  				    SCREAMING_SNAKE_CASE			,    size=(size['height'], size['width'])			,    resample=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				Dict:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return rescale(SCREAMING_SNAKE_CASE			,    scale=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    **SCREAMING_SNAKE_CASE			,    )     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return normalize(SCREAMING_SNAKE_CASE			,    mean=SCREAMING_SNAKE_CASE			,    std=SCREAMING_SNAKE_CASE			,    data_format=SCREAMING_SNAKE_CASE			,    **SCREAMING_SNAKE_CASE			)
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = ChannelDimension.FIRST			,    **SCREAMING_SNAKE_CASE			,    )     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				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(SCREAMING_SNAKE_CASE			,    param_name='crop_size'			)
  				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(SCREAMING_SNAKE_CASE			)
  				SCREAMING_SNAKE_CASE_			=	resample if resample is not None else self.resample
  				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_			=	make_list_of_images(SCREAMING_SNAKE_CASE			)
  
  				if not valid_images(SCREAMING_SNAKE_CASE			):
  							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.
  				SCREAMING_SNAKE_CASE_			=	[to_numpy_array(SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_center_crop:
  							SCREAMING_SNAKE_CASE_			=	[self.center_crop(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_resize:
  							SCREAMING_SNAKE_CASE_			=	[self.resize(image=SCREAMING_SNAKE_CASE			,    size=SCREAMING_SNAKE_CASE			,    resample=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_rescale:
  							SCREAMING_SNAKE_CASE_			=	[self.rescale(image=SCREAMING_SNAKE_CASE			,    scale=SCREAMING_SNAKE_CASE			) for image in images]
  
  				if do_normalize:
  							SCREAMING_SNAKE_CASE_			=	[self.normalize(image=SCREAMING_SNAKE_CASE			,    mean=SCREAMING_SNAKE_CASE			,    std=SCREAMING_SNAKE_CASE			) for image in images]
  
  				SCREAMING_SNAKE_CASE_			=	[to_channel_dimension_format(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for image in images]
  
  				SCREAMING_SNAKE_CASE_			=	{'pixel_values': images}
  				return BatchFeature(data=SCREAMING_SNAKE_CASE			,    tensor_type=SCREAMING_SNAKE_CASE			)
  
style_context: 
  
  
  
  
  from __future__ import annotations
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	m - 1, 0
  			while i >= 0 and j < n:
  						if key == mat[i][j]:
  									print(F'Key {key} found at row- {i + 1} column- {j + 1}'   )
  									return
  						if key < mat[i][j]:
  									i -= 1
  						else:
  									j += 1
  			print(F'Key {key} not found'   )
  
  def lowercase						(     ) ->       None:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  			SCREAMING_SNAKE_CASE_			=	int(input('Enter the element to be searched:'   )   )
  			print(SCREAMING_SNAKE_CASE   )
  			search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE					,						len(SCREAMING_SNAKE_CASE   )					,						len(mat[0]   )					,						SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  				main()
  