

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 convert_to_rgb, 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


if is_vision_available():
       import PIL


A_		:      List[Any]					    = logging.get_logger(__name__)
class        A_ (   __snake_case    ):


    '''simple docstring'''






    a__  				=							["pixel_values"]


    def __init__(self  ,   lowercase__ = True  ,   lowercase__ = None  ,   lowercase__ = PILImageResampling.BICUBIC  ,   lowercase__ = True  ,   lowercase__ = 1 / 255  ,   lowercase__ = True  ,   lowercase__ = None  ,   lowercase__ = None  ,   lowercase__ = True  ,   **lowercase__  ,   )  ->   None:
        super().__init__(**__lowerCamelCase	)
        __UpperCAmelCase					       =      size if size is not None else {'''height''': 384, '''width''': 384}
        __UpperCAmelCase					       =      get_size_dict(__lowerCamelCase  ,   default_to_square=__lowerCamelCase	)

        __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 OPENAI_CLIP_MEAN
        __UpperCAmelCase					       =      image_std if image_std is not None else OPENAI_CLIP_STD
        __UpperCAmelCase					       =      do_convert_rgb


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__ = PILImageResampling.BICUBIC  ,   lowercase__ = None  ,   **lowercase__  ,   )  ->   np.ndarray:
        __UpperCAmelCase					       =      get_size_dict(__lowerCamelCase  ,   default_to_square=__lowerCamelCase	)
        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()}'''	)
        __UpperCAmelCase					       =      (size['''height'''], size['''width'''])
        return resize(__lowerCamelCase  ,   size=__lowerCamelCase  ,   resample=__lowerCamelCase  ,   data_format=__lowerCamelCase  ,   **__lowerCamelCase	)


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__ = None  ,   **lowercase__  ,   )  ->   Optional[int]:
        return rescale(__lowerCamelCase  ,   scale=__lowerCamelCase  ,   data_format=__lowerCamelCase  ,   **__lowerCamelCase	)


    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__  ,   lowercase__  ,   lowercase__ = None  ,   **lowercase__  ,   )  ->   np.ndarray:
        return normalize(__lowerCamelCase  ,   mean=__lowerCamelCase  ,   std=__lowerCamelCase  ,   data_format=__lowerCamelCase  ,   **__lowerCamelCase	)






    def   lowerCAmelCase_    (self  ,   lowercase__  ,   lowercase__ = None  ,   lowercase__ = None  ,   lowercase__ = None  ,   lowercase__ = None  ,   lowercase__ = None  ,   lowercase__ = None  ,   lowercase__ = None  ,   lowercase__ = None  ,   lowercase__ = None  ,   lowercase__ = None  ,   lowercase__ = ChannelDimension.FIRST  ,   **lowercase__  ,   )  ->   PIL.Image.Image:
        __UpperCAmelCase					       =      do_resize if do_resize is not None else self.do_resize
        __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					       =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        __UpperCAmelCase					       =      size if size is not None else self.size
        __UpperCAmelCase					       =      get_size_dict(__lowerCamelCase  ,   default_to_square=__lowerCamelCase	)

        __UpperCAmelCase					       =      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 or resample is None:
            raise ValueError('''Size and resample 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 std must be specified if do_normalize is True.'''	)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
            __UpperCAmelCase					       =      [convert_to_rgb(__lowerCamelCase	) for image in images]

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

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

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

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

        __UpperCAmelCase					       =      [to_channel_dimension_format(__lowerCamelCase  ,   __lowerCamelCase	) for image in images]

        __UpperCAmelCase					       =      BatchFeature(data={'''pixel_values''': images}  ,   tensor_type=__lowerCamelCase	)

        return encoded_outputs








def 	_UpperCamelCase					(    )    ->list[list[int]]:
   return [list(range(1_0_0_0 - i	,    -1_0_0_0 - i	,    -1 ) ) for i in range(1_0_0_0 )]


__a	 =      generate_large_matrix()
__a	 =      (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->None:
   assert all(row == sorted(lowerCAmelCase_	,    reverse=lowerCAmelCase_ ) for row in grid )
   assert all(list(lowerCAmelCase_ ) == sorted(lowerCAmelCase_	,    reverse=lowerCAmelCase_ ) for col in zip(*lowerCAmelCase_ ) )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(lowerCAmelCase_ ) - 1

   # Edge cases such as no values or all numbers are negative.
   if not array or array[0] < 0:
      return 0

   while right + 1 > left:
      UpperCAmelCase      					=  (left + right) // 2
      UpperCAmelCase      					=  array[mid]

      # Num must be negative and the index must be greater than or equal to 0.
      if num < 0 and array[mid - 1] >= 0:
         return mid

      if num >= 0:
         UpperCAmelCase      					=  mid + 1
      else:
         UpperCAmelCase      					=  mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
   return len(lowerCAmelCase_ )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   UpperCAmelCase      					=  len(grid[0] )

   for i in range(len(lowerCAmelCase_ ) ):
      UpperCAmelCase      					=  find_negative_index(grid[i][:bound] )
      total += bound
   return (len(lowerCAmelCase_ ) * len(grid[0] )) - total
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   return len([number for row in grid for number in row if number < 0] )
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   UpperCAmelCase      					=  0
   for row in grid:
      for i, number in enumerate(lowerCAmelCase_ ):
         if number < 0:
            total += len(lowerCAmelCase_ ) - i
            break
   return total
def 	_UpperCamelCase					(    )    ->None:
   from timeit import timeit

   print("""Running benchmarks""" )
   UpperCAmelCase      					=  (
       """from __main__ import count_negatives_binary_search, """
       """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
   )
   for func in (
       "count_negatives_binary_search",  # took 0.7727 seconds
       "count_negatives_brute_force_with_break",  # took 4.6505 seconds
       "count_negatives_brute_force",  # took 12.8160 seconds
   ):
      UpperCAmelCase      					=  timeit(F"""{func}(grid=grid)"""	,    setup=lowerCAmelCase_	,    number=5_0_0 )
      print(F"""{func}() took {time:0.4f} seconds""" )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()



