


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	 =      logging.get_logger(__name__)






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							['''pixel_values''']





      def __init__(		self    :       Optional[Any]       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Dict[str, int] = None       ,							__lowerCamelCase    :       PILImageResampling = PILImageResampling.BICUBIC       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Union[int, float] = 1 / 2_5_5       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Optional[Union[float, List[float]]] = None       ,							__lowerCamelCase    :       Optional[Union[float, List[float]]] = None       ,							__lowerCamelCase    :       bool = True       ,							**__lowerCamelCase    :       Optional[Any]       ,							)     -> None:






         """simple docstring"""







         super().__init__(**__lowerCamelCase							)
         UpperCAmelCase      					=  size if size is not None else {"""height""": 3_8_4, """width""": 3_8_4}
         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 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       np.ndarray       ,							__lowerCamelCase    :       Dict[str, int]       ,							__lowerCamelCase    :       PILImageResampling = PILImageResampling.BICUBIC       ,							__lowerCamelCase    :       Optional[Union[str, ChannelDimension]] = None       ,							**__lowerCamelCase    :       str       ,							)     -> np.ndarray:






         """simple docstring"""







         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 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       np.ndarray       ,							__lowerCamelCase    :       Union[int, float]       ,							__lowerCamelCase    :       Optional[Union[str, ChannelDimension]] = None       ,							**__lowerCamelCase    :       Dict       ,							)     -> Optional[int]:






         """simple docstring"""







         return rescale(__lowerCamelCase       ,							scale=__lowerCamelCase       ,							data_format=__lowerCamelCase       ,							**__lowerCamelCase							)





      def 							_lowercase					(		self    :       Optional[Any]       ,							__lowerCamelCase    :       np.ndarray       ,							__lowerCamelCase    :       Union[float, List[float]]       ,							__lowerCamelCase    :       Union[float, List[float]]       ,							__lowerCamelCase    :       Optional[Union[str, ChannelDimension]] = None       ,							**__lowerCamelCase    :       Union[str, Any]       ,							)     -> np.ndarray:






         """simple docstring"""







         return normalize(__lowerCamelCase       ,							mean=__lowerCamelCase       ,							std=__lowerCamelCase       ,							data_format=__lowerCamelCase       ,							**__lowerCamelCase							)





      def 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       ImageInput       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[Dict[str, int]] = None       ,							__lowerCamelCase    :       PILImageResampling = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[float] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[Union[float, List[float]]] = None       ,							__lowerCamelCase    :       Optional[Union[float, List[float]]] = None       ,							__lowerCamelCase    :       Optional[Union[str, TensorType]] = None       ,							__lowerCamelCase    :       bool = None       ,							__lowerCamelCase    :       ChannelDimension = ChannelDimension.FIRST       ,							**__lowerCamelCase    :       Tuple       ,							)     -> PIL.Image.Image:






         """simple docstring"""







         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()



