





'''simple docstring'''



from collections import deque




def    _lowerCAmelCase						(				lowerCamelCase_			:  Dict				):
			__lowercase										=							len(A_				)
			__lowercase										=							deque()
			__lowercase										=							[False for _ in range(A_				)]
			__lowercase										=							[-1 for _ in range(A_				)]
			__lowercase										=							index_of[:]

			def strong_connect(lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  str		,       lowerCamelCase_			:  int				):
						__lowercase										=							index  # the number when this node is seen
						__lowercase										=							index  # lowest rank node reachable from here
						index += 1
						stack.append(A_				)
						__lowercase										=							True

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

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

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

			return components




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Any				):
			__lowercase										=							[[] for _ in range(A_				)]
			for u, v in edges:
						g[u].append(A_				)
			return g


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

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










'''simple docstring'''



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				int											=       ["pixel_values"]


	def __init__(self     ,_lowerCamelCase = True     ,_lowerCamelCase = 32     ,_lowerCamelCase=PILImageResampling.BILINEAR     ,_lowerCamelCase = True     ,**_lowerCamelCase     ,)							->							None:







				'''simple docstring'''





				__lowercase										=							do_resize
				__lowercase										=							do_rescale
				__lowercase										=							size_divisor
				__lowercase										=							resample
				super().__init__(**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							get_image_size(_lowerCamelCase )
				# Rounds the height and width down to the closest multiple of size_divisor
				__lowercase										=							height // size_divisor * size_divisor
				__lowercase										=							width // size_divisor * size_divisor
				__lowercase										=							resize(_lowerCamelCase     ,(new_h, new_w)     ,resample=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )
				return image


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							np.ndarray:







				'''simple docstring'''





				return rescale(image=_lowerCamelCase     ,scale=_lowerCamelCase     ,data_format=_lowerCamelCase     ,**_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase=None     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase = ChannelDimension.FIRST     ,**_lowerCamelCase     ,)							->							BatchFeature:







				'''simple docstring'''





				__lowercase										=							do_resize if do_resize is not None else self.do_resize
				__lowercase										=							do_rescale if do_rescale is not None else self.do_rescale
				__lowercase										=							size_divisor if size_divisor is not None else self.size_divisor
				__lowercase										=							resample if resample is not None else self.resample

				if do_resize and size_divisor is None:
							raise ValueError('''size_divisor is required for resizing''' )

				__lowercase										=							make_list_of_images(_lowerCamelCase )

				if not valid_images(_lowerCamelCase ):
							raise ValueError('''Invalid image(s)''' )

				# All transformations expect numpy arrays.
				__lowercase										=							[to_numpy_array(_lowerCamelCase ) for img in images]

				if do_resize:
							__lowercase										=							[self.resize(_lowerCamelCase     ,size_divisor=_lowerCamelCase     ,resample=_lowerCamelCase ) for image in images]

				if do_rescale:
							__lowercase										=							[self.rescale(_lowerCamelCase     ,scale=1 / 255 ) for image in images]

				__lowercase										=							[to_channel_dimension_format(_lowerCamelCase     ,_lowerCamelCase ) for image in images]

				__lowercase										=							{'''pixel_values''': images}
				return BatchFeature(data=_lowerCamelCase     ,tensor_type=_lowerCamelCase )





