
'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__lowercase	=    logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 							_snake_case     (  __a ):
	"""simple docstring"""





	_UpperCamelCase							:  int								=		['''pixel_values''']




	def __init__(						self				: List[str]					,						UpperCamelCase_				: List[str] = True					,						UpperCamelCase_				: str = None					,						UpperCamelCase_				: Optional[int] = PILImageResampling.BILINEAR					,						UpperCamelCase_				: str = True					,						UpperCamelCase_				: List[Any] = False					,						UpperCamelCase_				: List[Any] = True					,						UpperCamelCase_				: List[str] = True					,						UpperCamelCase_				: List[str] = 1 / 255					,						UpperCamelCase_				: Dict = True					,						UpperCamelCase_				: Dict = None					,						UpperCamelCase_				: Any = None					,						**UpperCamelCase_				: Optional[int]					,						):
			super().__init__(**SCREAMING_SNAKE_CASE__      )

			lowerCAmelCase_   :       Dict    							=size if size is not None else {'''height''': 2560, '''width''': 1920}
			if isinstance(SCREAMING_SNAKE_CASE__					,						(tuple, list)      ):
					# The previous feature extractor size parameter was in (width, height) format
					lowerCAmelCase_   :       Optional[int]    							=size[::-1]
			lowerCAmelCase_   :       Optional[int]    							=get_size_dict(SCREAMING_SNAKE_CASE__      )

			lowerCAmelCase_   :       Dict    							=do_resize
			lowerCAmelCase_   :       Dict    							=size
			lowerCAmelCase_   :       int    							=resample
			lowerCAmelCase_   :       Dict    							=do_thumbnail
			lowerCAmelCase_   :       Tuple    							=do_align_long_axis
			lowerCAmelCase_   :       Optional[Any]    							=do_pad
			lowerCAmelCase_   :       List[Any]    							=do_rescale
			lowerCAmelCase_   :       Optional[Any]    							=rescale_factor
			lowerCAmelCase_   :       Tuple    							=do_normalize
			lowerCAmelCase_   :       int    							=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			lowerCAmelCase_   :       Union[str, Any]    							=image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 	__A      (						self				: Optional[int]					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: Any = None      ):
			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=get_image_size(SCREAMING_SNAKE_CASE__      )
			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=size['''height'''], size['''width''']

			if (output_width < output_height and input_width > input_height) or (
			    output_width > output_height and input_width < input_height
			):
					lowerCAmelCase_   :       Union[str, Any]    							=np.rotaa(SCREAMING_SNAKE_CASE__					,						3      )

			if data_format is not None:
					lowerCAmelCase_   :       Tuple    							=to_channel_dimension_format(SCREAMING_SNAKE_CASE__					,						SCREAMING_SNAKE_CASE__      )

			return image




	def 	__A      (						self				: Optional[Any]					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: Optional[int]					,						UpperCamelCase_				: int = False					,						UpperCamelCase_				: int = None					,						):
			lowerCAmelCase_							,		lowerCAmelCase_   :       List[str]    							=size['''height'''], size['''width''']
			lowerCAmelCase_							,		lowerCAmelCase_   :       Optional[Any]    							=get_image_size(SCREAMING_SNAKE_CASE__      )

			lowerCAmelCase_   :       str    							=output_width - input_width
			lowerCAmelCase_   :       Optional[int]    							=output_height - input_height

			if random_padding:
					lowerCAmelCase_   :       Optional[int]    							=np.random.randint(low=0					,						high=delta_height + 1      )
					lowerCAmelCase_   :       Optional[Any]    							=np.random.randint(low=0					,						high=delta_width + 1      )
			else:
					lowerCAmelCase_   :       str    							=delta_height // 2
					lowerCAmelCase_   :       Optional[int]    							=delta_width // 2

			lowerCAmelCase_   :       Dict    							=delta_height - pad_top
			lowerCAmelCase_   :       List[Any]    							=delta_width - pad_left

			lowerCAmelCase_   :       List[Any]    							=((pad_top, pad_bottom), (pad_left, pad_right))
			return pad(SCREAMING_SNAKE_CASE__					,						SCREAMING_SNAKE_CASE__					,						data_format=SCREAMING_SNAKE_CASE__      )




	def 	__A      (						self				: Tuple					,						*UpperCamelCase_				: int					,						**UpperCamelCase_				: str      ):
			logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''      )
			return self.pad_image(*SCREAMING_SNAKE_CASE__					,						**SCREAMING_SNAKE_CASE__      )




	def 	__A      (						self				: Optional[Any]					,						UpperCamelCase_				: str					,						UpperCamelCase_				: int					,						UpperCamelCase_				: int = PILImageResampling.BICUBIC					,						UpperCamelCase_				: Union[str, Any] = None					,						**UpperCamelCase_				: Dict					,						):
			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=get_image_size(SCREAMING_SNAKE_CASE__      )
			lowerCAmelCase_							,		lowerCAmelCase_   :       Dict    							=size['''height'''], size['''width''']

			# We always resize to the smallest of either the input or output size.
			lowerCAmelCase_   :       List[str]    							=min(SCREAMING_SNAKE_CASE__					,						SCREAMING_SNAKE_CASE__      )
			lowerCAmelCase_   :       Optional[Any]    							=min(SCREAMING_SNAKE_CASE__					,						SCREAMING_SNAKE_CASE__      )

			if height == input_height and width == input_width:
					return image

			if input_height > input_width:
					lowerCAmelCase_   :       str    							=int(input_width * height / input_height      )
			elif input_width > input_height:
					lowerCAmelCase_   :       Any    							=int(input_height * width / input_width      )

			return resize(
			    SCREAMING_SNAKE_CASE__					,						size=(height, width)					,						resample=SCREAMING_SNAKE_CASE__					,						reducing_gap=2.0					,						data_format=SCREAMING_SNAKE_CASE__					,						**SCREAMING_SNAKE_CASE__      )




	def 	__A      (						self				: Tuple					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: int					,						UpperCamelCase_				: Tuple = PILImageResampling.BICUBIC					,						UpperCamelCase_				: Dict = None					,						**UpperCamelCase_				: Dict					,						):
			lowerCAmelCase_   :       Optional[int]    							=get_size_dict(SCREAMING_SNAKE_CASE__      )
			lowerCAmelCase_   :       Tuple    							=min(size['''height''']					,						size['''width''']      )
			lowerCAmelCase_   :       Dict    							=get_resize_output_image_size(SCREAMING_SNAKE_CASE__					,						size=SCREAMING_SNAKE_CASE__					,						default_to_square=SCREAMING_SNAKE_CASE__      )
			lowerCAmelCase_   :       str    							=resize(SCREAMING_SNAKE_CASE__					,						size=SCREAMING_SNAKE_CASE__					,						resample=SCREAMING_SNAKE_CASE__					,						data_format=SCREAMING_SNAKE_CASE__					,						**SCREAMING_SNAKE_CASE__      )
			return resized_image




	def 	__A      (						self				: str					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: Dict					,						UpperCamelCase_				: Any = None					,						**UpperCamelCase_				: int					,						):
			return rescale(SCREAMING_SNAKE_CASE__					,						scale=SCREAMING_SNAKE_CASE__					,						data_format=SCREAMING_SNAKE_CASE__					,						**SCREAMING_SNAKE_CASE__      )




	def 	__A      (						self				: Tuple					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: str					,						UpperCamelCase_				: Any = None					,						**UpperCamelCase_				: Tuple					,						):
			return normalize(SCREAMING_SNAKE_CASE__					,						mean=SCREAMING_SNAKE_CASE__					,						std=SCREAMING_SNAKE_CASE__					,						data_format=SCREAMING_SNAKE_CASE__					,						**SCREAMING_SNAKE_CASE__      )





	def 	__A      (						self				: str					,						UpperCamelCase_				: Tuple					,						UpperCamelCase_				: Any = None					,						UpperCamelCase_				: str = None					,						UpperCamelCase_				: Dict = None					,						UpperCamelCase_				: Optional[int] = None					,						UpperCamelCase_				: Tuple = None					,						UpperCamelCase_				: List[str] = None					,						UpperCamelCase_				: int = False					,						UpperCamelCase_				: List[str] = None					,						UpperCamelCase_				: List[str] = None					,						UpperCamelCase_				: Optional[Any] = None					,						UpperCamelCase_				: Optional[Any] = None					,						UpperCamelCase_				: int = None					,						UpperCamelCase_				: Any = None					,						UpperCamelCase_				: str = ChannelDimension.FIRST					,						**UpperCamelCase_				: int					,						):
			lowerCAmelCase_   :       Union[str, Any]    							=do_resize if do_resize is not None else self.do_resize
			lowerCAmelCase_   :       Optional[Any]    							=size if size is not None else self.size
			if isinstance(SCREAMING_SNAKE_CASE__					,						(tuple, list)      ):
					# Previous feature extractor had size in (width, height) format
					lowerCAmelCase_   :       Optional[Any]    							=size[::-1]
			lowerCAmelCase_   :       Optional[Any]    							=get_size_dict(SCREAMING_SNAKE_CASE__      )
			lowerCAmelCase_   :       Tuple    							=resample if resample is not None else self.resample
			lowerCAmelCase_   :       Tuple    							=do_thumbnail if do_thumbnail is not None else self.do_thumbnail
			lowerCAmelCase_   :       int    							=do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
			lowerCAmelCase_   :       Optional[Any]    							=do_pad if do_pad is not None else self.do_pad
			lowerCAmelCase_   :       Any    							=do_rescale if do_rescale is not None else self.do_rescale
			lowerCAmelCase_   :       Union[str, Any]    							=rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCAmelCase_   :       str    							=do_normalize if do_normalize is not None else self.do_normalize
			lowerCAmelCase_   :       Dict    							=image_mean if image_mean is not None else self.image_mean
			lowerCAmelCase_   :       Optional[Any]    							=image_std if image_std is not None else self.image_std

			lowerCAmelCase_   :       int    							=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_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_pad and size is None:
					raise ValueError('''Size must be specified if do_pad 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_   :       Dict    							=[to_numpy_array(SCREAMING_SNAKE_CASE__      ) for image in images]

			if do_align_long_axis:
					lowerCAmelCase_   :       Optional[int]    							=[self.align_long_axis(SCREAMING_SNAKE_CASE__					,						size=SCREAMING_SNAKE_CASE__      ) for image in images]

			if do_resize:
					lowerCAmelCase_   :       int    							=[self.resize(image=SCREAMING_SNAKE_CASE__					,						size=SCREAMING_SNAKE_CASE__					,						resample=SCREAMING_SNAKE_CASE__      ) for image in images]

			if do_thumbnail:
					lowerCAmelCase_   :       int    							=[self.thumbnail(image=SCREAMING_SNAKE_CASE__					,						size=SCREAMING_SNAKE_CASE__      ) for image in images]

			if do_pad:
					lowerCAmelCase_   :       Optional[Any]    							=[self.pad_image(image=SCREAMING_SNAKE_CASE__					,						size=SCREAMING_SNAKE_CASE__					,						random_padding=SCREAMING_SNAKE_CASE__      ) for image in images]

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

			if do_normalize:
					lowerCAmelCase_   :       List[str]    							=[self.normalize(image=SCREAMING_SNAKE_CASE__					,						mean=SCREAMING_SNAKE_CASE__					,						std=SCREAMING_SNAKE_CASE__      ) for image in images]

			lowerCAmelCase_   :       List[Any]    							=[to_channel_dimension_format(SCREAMING_SNAKE_CASE__					,						SCREAMING_SNAKE_CASE__      ) for image in images]

			lowerCAmelCase_   :       str    							={'''pixel_values''': images}
			return BatchFeature(data=SCREAMING_SNAKE_CASE__					,						tensor_type=SCREAMING_SNAKE_CASE__      )









"""simple docstring"""




from __future__ import annotations



def 						UpperCAmelCase__ (					A__						,     A__						,     A__						,     A__ )   ->   None:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      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 						UpperCAmelCase__ (					)   ->   None:




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


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




