





'''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


snake_case_   :  str								=      logging.get_logger(__name__)


if is_vision_available():
			import PIL

class     A_ (  lowerCAmelCase_  ):

			'''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_			,     ):
								super().__init__(**A_   )
								_UpperCamelCase											=   size if size is not None else {"shortest_edge": 2_24}
								_UpperCamelCase											=   get_size_dict(A_			,     default_to_square=A_   )
								_UpperCamelCase											=   crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
								_UpperCamelCase											=   get_size_dict(A_   )

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




			def       a	(					self			,     A_			,     A_			,     A_ = PILImageResampling.BICUBIC			,     A_ = None			,     **A_			,     ):
								_UpperCamelCase											=   get_size_dict(A_			,     default_to_square=A_   )
								_UpperCamelCase											=   get_resize_output_image_size(
								    A_			,     size=(size["height"], size["width"])			,     default_to_square=A_   )
								return resize(A_			,     size=A_			,     resample=A_			,     data_format=A_			,     **A_   )




			def       a	(					self			,     A_			,     A_			,     A_ = None			,     **A_			,     ):
								_UpperCamelCase											=   get_size_dict(A_   )
								return center_crop(A_			,     size=(size["height"], size["width"])			,     data_format=A_			,     **A_   )




			def       a	(					self			,     A_			,     A_			,     A_ = None			,     **A_			,     ):
								return rescale(A_			,     scale=A_			,     data_format=A_			,     **A_   )




			def       a	(					self			,     A_			,     A_			,     A_			,     A_ = None			,     **A_			,     ):
								return normalize(A_			,     mean=A_			,     std=A_			,     data_format=A_			,     **A_   )




			def       a	(					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_			,     ):
								_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_			,     default_to_square=A_   )
								_UpperCamelCase											=   resample if resample is not None else self.resample
								_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_   )
								_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											=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								_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_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:
													_UpperCamelCase											=   [convert_to_rgb(A_   ) for image in images]

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

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

								if do_center_crop:
													_UpperCamelCase											=   [self.center_crop(image=A_			,     size=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_   )










'''simple docstring'''



from __future__ import annotations



def  lowercase__(		_UpperCamelCase  : list[list[int]] ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int ,   _UpperCamelCase  : int | float				)->   None:






					"""simple docstring"""

					_UpperCamelCase				,	_UpperCamelCase											=   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"""

					_UpperCamelCase											=   [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
					_UpperCamelCase											=   int(input("Enter the element to be searched:"				)				)
					print(_UpperCamelCase				)
					search_in_a_sorted_matrix(_UpperCamelCase ,   len(_UpperCamelCase				) ,   len(mat[0]				) ,   _UpperCamelCase				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



