


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


__UpperCamelCase    		=       logging.get_logger(__name__)
def        UpperCamelCase_(				_A   :List[Any]  )->		List[List[ImageInput]]:
			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        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






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




			def __init__(							self      ,						snake_case = True      ,						snake_case = None      ,						snake_case = PILImageResampling.BILINEAR      ,						snake_case = True      ,						snake_case = None      ,						snake_case = True      ,						snake_case = 1 / 255      ,						snake_case = True      ,						snake_case = None      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


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

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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = PILImageResampling.BILINEAR      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       get_size_dict(snake_case     )
						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(snake_case      ,						size=(size["height"], size["width"])      ,						data_format=snake_case      ,						**snake_case     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = 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.
						UpperCamelCase__					=       to_numpy_array(snake_case     )

						if do_resize:
									UpperCamelCase__					=       self.resize(image=snake_case      ,						size=snake_case      ,						resample=snake_case     )

						if do_center_crop:
									UpperCamelCase__					=       self.center_crop(snake_case      ,						size=snake_case     )

						if do_rescale:
									UpperCamelCase__					=       self.rescale(image=snake_case      ,						scale=snake_case     )

						if do_normalize:
									UpperCamelCase__					=       self.normalize(image=snake_case      ,						mean=snake_case      ,						std=snake_case     )

						UpperCamelCase__					=       to_channel_dimension_format(snake_case      ,						snake_case     )
						return image







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = ChannelDimension.FIRST      ,						**snake_case      ,						):

						'''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_center_crop if do_center_crop is not None else self.do_center_crop
						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__					=       size if size is not None else self.size
						UpperCamelCase__					=       get_size_dict(snake_case      ,						default_to_square=snake_case     )
						UpperCamelCase__					=       crop_size if crop_size is not None else self.crop_size
						UpperCamelCase__					=       get_size_dict(snake_case      ,						param_name="crop_size"     )

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

						UpperCamelCase__					=       make_batched(snake_case     )

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

						UpperCamelCase__					=       {"pixel_values": videos}
						return BatchFeature(data=snake_case      ,						tensor_type=snake_case     )






from collections import deque
def        UpperCamelCase_(				_A   :Union[str, Any]  )->		List[Any]:
			UpperCamelCase__					=       len(_A  )
			UpperCamelCase__					=       deque()
			UpperCamelCase__					=       [False for _ in range(_A  )]
			UpperCamelCase__					=       [-1 for _ in range(_A  )]
			UpperCamelCase__					=       index_of[:]

			def strong_connect(_A   :Optional[int]				,     _A   :Optional[int]				,     _A   :List[Any]  ):
						UpperCamelCase__					=       index  # the number when this node is seen
						UpperCamelCase__					=       index  # lowest rank node reachable from here
						index += 1
						stack.append(_A  )
						UpperCamelCase__					=       True

						for w in g[v]:
									if index_of[w] == -1:
												UpperCamelCase__					=       strong_connect(_A				,     _A				,     _A  )
												UpperCamelCase__					=       (
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)
									elif on_stack[w]:
												UpperCamelCase__					=       (
												    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
												)

						if lowlink_of[v] == index_of[v]:
									UpperCamelCase__					=       []
									UpperCamelCase__					=       stack.pop()
									UpperCamelCase__					=       False
									component.append(_A  )
									while w != v:
												UpperCamelCase__					=       stack.pop()
												UpperCamelCase__					=       False
												component.append(_A  )
									components.append(_A  )
						return index

			UpperCamelCase__					=       []
			for v in range(_A  ):
						if index_of[v] == -1:
									strong_connect(_A				,     0				,     _A  )

			return components
def        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :Any  )->		Union[str, Any]:
			UpperCamelCase__					=       [[] for _ in range(_A  )]
			for u, v in edges:
						g[u].append(_A  )
			return g


if __name__ == "__main__":
						# Test
						__UpperCamelCase    		=       7
						__UpperCamelCase    		=       [0, 0, 1, 2, 3, 3, 4, 4, 6]
						__UpperCamelCase    		=       [1, 3, 2, 0, 1, 4, 5, 6, 5]
						__UpperCamelCase    		=       [(u, v) for u, v in zip(source, target)]
						__UpperCamelCase    		=       create_graph(n_vertices, edges)

						assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


