
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,
    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 TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


_lowerCamelCase	:    Union[str, Any]          =      logging.get_logger(__name__)






def 	a_		(	__lowercase       :     int			)			->  List[List[ImageInput]]:
			if isinstance(__lowercase	, (list, tuple)			) and isinstance(videos[0]	, (list, tuple)			) and is_valid_image(videos[0][0]			):
						return videos

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

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

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




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		List[Any]          = ["pixel_values"]



	def __init__(  self     :   Optional[Any]      ,  lowercase     :   bool = True      ,  lowercase     :   Dict[str, int] = None      ,  lowercase     :   PILImageResampling = PILImageResampling.BILINEAR      ,  lowercase     :   bool = True      ,  lowercase     :   Dict[str, int] = None      ,  lowercase     :   bool = True      ,  lowercase     :   Union[int, float] = 1 / 255      ,  lowercase     :   bool = True      ,  lowercase     :   Optional[Union[float, List[float]]] = None      ,  lowercase     :   Optional[Union[float, List[float]]] = None      ,  **lowercase     :   Any      ,  ):


				'''simple docstring'''


				super().__init__(**lowercase     )
				_snake_case          =      size if size is not None else {'shortest_edge': 224}
				_snake_case          =      get_size_dict(lowercase      ,  default_to_square=lowercase     )
				_snake_case          =      crop_size if crop_size is not None else {'height': 224, 'width': 224}
				_snake_case          =      get_size_dict(lowercase      ,  param_name='crop_size'     )

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



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   np.ndarray      ,  lowercase     :   Dict[str, int]      ,  lowercase     :   PILImageResampling = PILImageResampling.BILINEAR      ,  lowercase     :   Optional[Union[str, ChannelDimension]] = None      ,  **lowercase     :   Tuple      ,  ):


				'''simple docstring'''


				_snake_case          =      get_size_dict(lowercase      ,  default_to_square=lowercase     )
				if "shortest_edge" in size:
							_snake_case          =      get_resize_output_image_size(lowercase      ,  size['shortest_edge']      ,  default_to_square=lowercase     )
				elif "height" in size and "width" in size:
							_snake_case          =      (size['height'], size['width'])
				else:
							raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''     )
				return resize(lowercase      ,  size=lowercase      ,  resample=lowercase      ,  data_format=lowercase      ,  **lowercase     )



	def 			A							(  self     :   Dict      ,  lowercase     :   np.ndarray      ,  lowercase     :   Dict[str, int]      ,  lowercase     :   Optional[Union[str, ChannelDimension]] = None      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


				_snake_case          =      get_size_dict(lowercase     )
				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(lowercase      ,  size=(size['height'], size['width'])      ,  data_format=lowercase      ,  **lowercase     )



	def 			A							(  self     :   Any      ,  lowercase     :   np.ndarray      ,  lowercase     :   Union[int, float]      ,  lowercase     :   Optional[Union[str, ChannelDimension]] = None      ,  **lowercase     :   Tuple      ,  ):


				'''simple docstring'''


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



	def 			A							(  self     :   str      ,  lowercase     :   np.ndarray      ,  lowercase     :   Union[float, List[float]]      ,  lowercase     :   Union[float, List[float]]      ,  lowercase     :   Optional[Union[str, ChannelDimension]] = None      ,  **lowercase     :   Any      ,  ):


				'''simple docstring'''


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



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


				'''simple docstring'''


				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.'     )

				# All transformations expect numpy arrays.
				_snake_case          =      to_numpy_array(lowercase     )

				if do_resize:
							_snake_case          =      self.resize(image=lowercase      ,  size=lowercase      ,  resample=lowercase     )

				if do_center_crop:
							_snake_case          =      self.center_crop(lowercase      ,  size=lowercase     )

				if do_rescale:
							_snake_case          =      self.rescale(image=lowercase      ,  scale=lowercase     )

				if do_normalize:
							_snake_case          =      self.normalize(image=lowercase      ,  mean=lowercase      ,  std=lowercase     )

				_snake_case          =      to_channel_dimension_format(lowercase      ,  lowercase     )
				return image





	def 			A							(  self     :   Tuple      ,  lowercase     :   ImageInput      ,  lowercase     :   bool = None      ,  lowercase     :   Dict[str, int] = None      ,  lowercase     :   PILImageResampling = None      ,  lowercase     :   bool = None      ,  lowercase     :   Dict[str, int] = None      ,  lowercase     :   bool = None      ,  lowercase     :   float = None      ,  lowercase     :   bool = None      ,  lowercase     :   Optional[Union[float, List[float]]] = None      ,  lowercase     :   Optional[Union[float, List[float]]] = None      ,  lowercase     :   Optional[Union[str, TensorType]] = None      ,  lowercase     :   ChannelDimension = ChannelDimension.FIRST      ,  **lowercase     :   Optional[Any]      ,  ):


				'''simple docstring'''


				_snake_case          =      do_resize if do_resize is not None else self.do_resize
				_snake_case          =      resample if resample is not None else self.resample
				_snake_case          =      do_center_crop if do_center_crop is not None else self.do_center_crop
				_snake_case          =      do_rescale if do_rescale is not None else self.do_rescale
				_snake_case          =      rescale_factor if rescale_factor is not None else self.rescale_factor
				_snake_case          =      do_normalize if do_normalize is not None else self.do_normalize
				_snake_case          =      image_mean if image_mean is not None else self.image_mean
				_snake_case          =      image_std if image_std is not None else self.image_std

				_snake_case          =      size if size is not None else self.size
				_snake_case          =      get_size_dict(lowercase      ,  default_to_square=lowercase     )
				_snake_case          =      crop_size if crop_size is not None else self.crop_size
				_snake_case          =      get_size_dict(lowercase      ,  param_name='crop_size'     )

				if not valid_images(lowercase     ):
							raise ValueError(
							    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
							    'torch.Tensor, tf.Tensor or jax.ndarray.'     )

				_snake_case          =      make_batched(lowercase     )

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

				_snake_case          =      {'pixel_values': videos}
				return BatchFeature(data=lowercase      ,  tensor_type=lowercase     )

from __future__ import annotations

_lowerCamelCase	:    Optional[Any]          =      [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
_lowerCamelCase	:    Optional[int]          =      [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			_snake_case          =      len(__lowercase			)

			for i in range(__lowercase			):
						_snake_case          =      -1
						for j in range(i + 1	, __lowercase			):
									if arr[i] < arr[j]:
												_snake_case          =      arr[j]
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      []
			for i, outer in enumerate(__lowercase			):
						_snake_case          =      -1
						for inner in arr[i + 1 :]:
									if outer < inner:
												_snake_case          =      inner
												break
						result.append(__lowercase			)
			return result






def 	a_		(	__lowercase       :     list[float]			)			->  list[float]:
			_snake_case          =      len(__lowercase			)
			_snake_case          =      []
			_snake_case          =      [-1] * arr_size

			for index in reversed(range(__lowercase			)			):
						if stack:
									while stack[-1] <= arr[index]:
												stack.pop()
												if not stack:
															break
						if stack:
									_snake_case          =      stack[-1]
						stack.append(arr[index]			)
			return result


if __name__ == "__main__":
			from doctest import testmod
			from timeit import timeit

			testmod()
			print(next_greatest_element_slow(arr))
			print(next_greatest_element_fast(arr))
			print(next_greatest_element(arr))

			_lowerCamelCase	:    Union[str, Any]          =      (
			    '''from __main__ import arr, next_greatest_element_slow, '''
			    '''next_greatest_element_fast, next_greatest_element'''
			)
			print(
			    '''next_greatest_element_slow():''',
			    timeit('''next_greatest_element_slow(arr)''', setup=setup),
			)
			print(
			    '''next_greatest_element_fast():''',
			    timeit('''next_greatest_element_fast(arr)''', setup=setup),
			)
			print(
			    '''     next_greatest_element():''',
			    timeit('''next_greatest_element(arr)''', setup=setup),
			)