
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
					import PIL

SCREAMING_SNAKE_CASE							:List[str]										=    logging.get_logger(__name__)


def 						UpperCAmelCase      (     a_						)		->     List[List[ImageInput]]:







				"""simple docstring"""



				if isinstance(a_		,       (list, tuple)						) and isinstance(videos[0]		,       (list, tuple)						) and is_valid_image(videos[0][0]						):
								return videos

				elif isinstance(a_		,       (list, tuple)						) and is_valid_image(videos[0]						):
								return [videos]

				elif is_valid_image(a_						):
								return [[videos]]

				raise ValueError(F'''Could not make batched video from {videos}'''						)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					["pixel_values"]






	def __init__(	self :			Union[str, Any]							,A :			bool = True							,A :			Dict[str, int] = None							,A :			PILImageResampling = PILImageResampling.BILINEAR							,A :			bool = True							,A :			Dict[str, int] = None							,A :			bool = True							,A :			Union[int, float] = 1 / 2_55							,A :			bool = True							,A :			bool = True							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[Union[float, List[float]]] = None							,**A :			Optional[Any]							,):
					super().__init__(**A			)
					__A             =			size if size is not None else {"shortest_edge": 2_56}
					__A             =			get_size_dict(A							,default_to_square=A			)
					__A             =			crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
					__A             =			get_size_dict(A							,param_name="crop_size"			)

					__A             =			do_resize
					__A             =			size
					__A             =			do_center_crop
					__A             =			crop_size
					__A             =			resample
					__A             =			do_rescale
					__A             =			rescale_factor
					__A             =			offset
					__A             =			do_normalize
					__A             =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					__A             =			image_std if image_std is not None else IMAGENET_STANDARD_STD






	def     UpperCamelCase_  (	self :			List[str]							,A :			np.ndarray							,A :			Dict[str, int]							,A :			PILImageResampling = PILImageResampling.BILINEAR							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			int							,):
					__A             =			get_size_dict(A							,default_to_square=A			)
					if "shortest_edge" in size:
									__A             =			get_resize_output_image_size(A							,size["shortest_edge"]							,default_to_square=A			)
					elif "height" in size and "width" in size:
									__A             =			(size["height"], size["width"])
					else:
									raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''			)
					return resize(A							,size=A							,resample=A							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			List[Any]							,A :			np.ndarray							,A :			Dict[str, int]							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			Dict							,):
					__A             =			get_size_dict(A			)
					if "height" not in size or "width" not in size:
									raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''			)
					return center_crop(A							,size=(size["height"], size["width"])							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			np.ndarray							,A :			Union[int, float]							,A :			bool = True							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			List[Any]							,):
					__A             =			image.astype(np.floataa			)
					if offset:
									__A             =			image - (scale / 2)
					return rescale(A							,scale=A							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			Any							,A :			np.ndarray							,A :			Union[float, List[float]]							,A :			Union[float, List[float]]							,A :			Optional[Union[str, ChannelDimension]] = None							,**A :			Dict							,):
					return normalize(A							,mean=A							,std=A							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			Any							,A :			ImageInput							,A :			bool = None							,A :			Dict[str, int] = None							,A :			PILImageResampling = None							,A :			bool = None							,A :			Dict[str, int] = None							,A :			bool = None							,A :			float = None							,A :			bool = None							,A :			bool = None							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[ChannelDimension] = ChannelDimension.FIRST							,):
					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_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."			)

					if offset and not do_rescale:
									raise ValueError("For offset, do_rescale must also be set to True."			)

					# All transformations expect numpy arrays.
					__A             =			to_numpy_array(A			)

					if do_resize:
									__A             =			self.resize(image=A							,size=A							,resample=A			)

					if do_center_crop:
									__A             =			self.center_crop(A							,size=A			)

					if do_rescale:
									__A             =			self.rescale(image=A							,scale=A							,offset=A			)

					if do_normalize:
									__A             =			self.normalize(image=A							,mean=A							,std=A			)

					__A             =			to_channel_dimension_format(A							,A			)
					return image







	def     UpperCamelCase_  (	self :			str							,A :			ImageInput							,A :			bool = None							,A :			Dict[str, int] = None							,A :			PILImageResampling = None							,A :			bool = None							,A :			Dict[str, int] = None							,A :			bool = None							,A :			float = None							,A :			bool = None							,A :			bool = None							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[Union[float, List[float]]] = None							,A :			Optional[Union[str, TensorType]] = None							,A :			ChannelDimension = ChannelDimension.FIRST							,**A :			Dict							,):
					__A             =			do_resize if do_resize is not None else self.do_resize
					__A             =			resample if resample is not None else self.resample
					__A             =			do_center_crop if do_center_crop is not None else self.do_center_crop
					__A             =			do_rescale if do_rescale is not None else self.do_rescale
					__A             =			rescale_factor if rescale_factor is not None else self.rescale_factor
					__A             =			offset if offset is not None else self.offset
					__A             =			do_normalize if do_normalize is not None else self.do_normalize
					__A             =			image_mean if image_mean is not None else self.image_mean
					__A             =			image_std if image_std is not None else self.image_std

					__A             =			size if size is not None else self.size
					__A             =			get_size_dict(A							,default_to_square=A			)
					__A             =			crop_size if crop_size is not None else self.crop_size
					__A             =			get_size_dict(A							,param_name="crop_size"			)

					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."			)

					__A             =			make_batched(A			)

					__A             =			[
					    [
					        self._preprocess_image(
					            image=A							,do_resize=A							,size=A							,resample=A							,do_center_crop=A							,crop_size=A							,do_rescale=A							,rescale_factor=A							,offset=A							,do_normalize=A							,image_mean=A							,image_std=A							,data_format=A							,)
					        for img in video
					    ]
					    for video in videos
					]

					__A             =			{"pixel_values": videos}
					return BatchFeature(data=A							,tensor_type=A			)


def 						UpperCAmelCase      (     )		->     list[list[int]]:







				"""simple docstring"""



				return [list(range(1_0_0_0 - i		,       -1_0_0_0 - i		,       -1						)						) for i in range(1_0_0_0						)]


SCREAMING_SNAKE_CASE							:List[str]										=    generate_large_matrix()
SCREAMING_SNAKE_CASE							:str										=    (
    [[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      (     a_						)		->     None:







				"""simple docstring"""



				assert all(row == sorted(a_		,       reverse=a_						) for row in grid						)
				assert all(list(a_						) == sorted(a_		,       reverse=a_						) for col in zip(*a_						)						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			0
				__A             =			len(a_						) - 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:
								__A             =			(left + right) // 2
								__A             =			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:
												__A             =			mid + 1
								else:
												__A             =			mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
				return len(a_						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			0
				__A             =			len(grid[0]						)

				for i in range(len(a_						)						):
								__A             =			find_negative_index(grid[i][:bound]						)
								total += bound
				return (len(a_						) * len(grid[0]						)) - total


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				return len([number for row in grid for number in row if number < 0]						)


def 						UpperCAmelCase      (     a_						)		->     int:







				"""simple docstring"""



				__A             =			0
				for row in grid:
								for i, number in enumerate(a_						):
												if number < 0:
																total += len(a_						) - i
																break
				return total


def 						UpperCAmelCase      (     )		->     None:







				"""simple docstring"""



				from timeit import timeit

				print("Running benchmarks"						)
				__A             =			(
				    "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
				):
								__A             =			timeit(F'''{func}(grid=grid)'''		,       setup=a_		,       number=5_0_0						)
								print(F'''{func}() took {time:0.4f} seconds'''						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()
