




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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


SCREAMING_SNAKE_CASE:     Any									= logging.get_logger(__name__)


if is_vision_available():
						import PIL



class    lowercase_		(__snake_case ):
						lowerCAmelCase__			      =["pixel_values"]



						def __init__(			self	:   List[str]					,							snake_case__	:   str = True					,							snake_case__	:   Union[str, Any] = None					,							snake_case__	:   Dict = PILImageResampling.BICUBIC					,							snake_case__	:   Any = True					,							snake_case__	:   Any = None					,							snake_case__	:   str = True					,							snake_case__	:   Dict = 1 / 2_55					,							snake_case__	:   int = True					,							snake_case__	:   int = None					,							snake_case__	:   Tuple = None					,							snake_case__	:   Any = True					,							**snake_case__	:   List[Any]					,							):

								"""simple docstring"""

								super().__init__(**a__  )
								SCREAMING_SNAKE_CASE_					      =							size if size is not None else {'shortest_edge': 2_24}
								SCREAMING_SNAKE_CASE_					      =							get_size_dict(a__					,							default_to_square=a__  )
								SCREAMING_SNAKE_CASE_					      =							crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
								SCREAMING_SNAKE_CASE_					      =							get_size_dict(a__					,							default_to_square=a__					,							param_name='crop_size'  )

								SCREAMING_SNAKE_CASE_					      =							do_resize
								SCREAMING_SNAKE_CASE_					      =							size
								SCREAMING_SNAKE_CASE_					      =							resample
								SCREAMING_SNAKE_CASE_					      =							do_center_crop
								SCREAMING_SNAKE_CASE_					      =							crop_size
								SCREAMING_SNAKE_CASE_					      =							do_rescale
								SCREAMING_SNAKE_CASE_					      =							rescale_factor
								SCREAMING_SNAKE_CASE_					      =							do_normalize
								SCREAMING_SNAKE_CASE_					      =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								SCREAMING_SNAKE_CASE_					      =							image_std if image_std is not None else OPENAI_CLIP_STD
								SCREAMING_SNAKE_CASE_					      =							do_convert_rgb



						def 	__a					(			self	:   List[str]					,							snake_case__	:   List[str]					,							snake_case__	:   Any					,							snake_case__	:   List[Any] = PILImageResampling.BICUBIC					,							snake_case__	:   Any = None					,							**snake_case__	:   Optional[int]					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							get_size_dict(a__					,							default_to_square=a__  )
								if "shortest_edge" not in size:
										raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
								SCREAMING_SNAKE_CASE_					      =							get_resize_output_image_size(a__					,							size=size['shortest_edge']					,							default_to_square=a__  )
								return resize(a__					,							size=a__					,							resample=a__					,							data_format=a__					,							**a__  )



						def 	__a					(			self	:   str					,							snake_case__	:   List[str]					,							snake_case__	:   Dict					,							snake_case__	:   Dict = None					,							**snake_case__	:   int					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							get_size_dict(a__  )
								if "height" not in size or "width" not in size:
										raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''  )
								return center_crop(a__					,							size=(size['height'], size['width'])					,							data_format=a__					,							**a__  )



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   Optional[int]					,							snake_case__	:   List[Any]					,							snake_case__	:   int = None					,							**snake_case__	:   str					,							):

								"""simple docstring"""

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



						def 	__a					(			self	:   Union[str, Any]					,							snake_case__	:   int					,							snake_case__	:   Dict					,							snake_case__	:   int					,							snake_case__	:   Any = None					,							**snake_case__	:   Tuple					,							):

								"""simple docstring"""

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






						def 	__a					(			self	:   int					,							snake_case__	:   Optional[int]					,							snake_case__	:   Optional[Any] = None					,							snake_case__	:   str = None					,							snake_case__	:   List[Any] = None					,							snake_case__	:   List[str] = None					,							snake_case__	:   int = None					,							snake_case__	:   Any = None					,							snake_case__	:   Tuple = None					,							snake_case__	:   str = None					,							snake_case__	:   Optional[Any] = None					,							snake_case__	:   Optional[Any] = None					,							snake_case__	:   List[str] = None					,							snake_case__	:   List[str] = None					,							snake_case__	:   Any = ChannelDimension.FIRST					,							**snake_case__	:   Dict					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							do_resize if do_resize is not None else self.do_resize
								SCREAMING_SNAKE_CASE_					      =							size if size is not None else self.size
								SCREAMING_SNAKE_CASE_					      =							get_size_dict(a__					,							param_name='size'					,							default_to_square=a__  )
								SCREAMING_SNAKE_CASE_					      =							resample if resample is not None else self.resample
								SCREAMING_SNAKE_CASE_					      =							do_center_crop if do_center_crop is not None else self.do_center_crop
								SCREAMING_SNAKE_CASE_					      =							crop_size if crop_size is not None else self.crop_size
								SCREAMING_SNAKE_CASE_					      =							get_size_dict(a__					,							param_name='crop_size'					,							default_to_square=a__  )
								SCREAMING_SNAKE_CASE_					      =							do_rescale if do_rescale is not None else self.do_rescale
								SCREAMING_SNAKE_CASE_					      =							rescale_factor if rescale_factor is not None else self.rescale_factor
								SCREAMING_SNAKE_CASE_					      =							do_normalize if do_normalize is not None else self.do_normalize
								SCREAMING_SNAKE_CASE_					      =							image_mean if image_mean is not None else self.image_mean
								SCREAMING_SNAKE_CASE_					      =							image_std if image_std is not None else self.image_std
								SCREAMING_SNAKE_CASE_					      =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								SCREAMING_SNAKE_CASE_					      =							make_list_of_images(a__  )

								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.'  )

								if do_resize and size is None:
										raise ValueError('Size 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.'  )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
										SCREAMING_SNAKE_CASE_					      =							[convert_to_rgb(a__  ) for image in images]

								# All transformations expect numpy arrays.
								SCREAMING_SNAKE_CASE_					      =							[to_numpy_array(a__  ) for image in images]

								if do_resize:
										SCREAMING_SNAKE_CASE_					      =							[self.resize(image=a__					,							size=a__					,							resample=a__  ) for image in images]

								if do_center_crop:
										SCREAMING_SNAKE_CASE_					      =							[self.center_crop(image=a__					,							size=a__  ) for image in images]

								if do_rescale:
										SCREAMING_SNAKE_CASE_					      =							[self.rescale(image=a__					,							scale=a__  ) for image in images]

								if do_normalize:
										SCREAMING_SNAKE_CASE_					      =							[self.normalize(image=a__					,							mean=a__					,							std=a__  ) for image in images]

								SCREAMING_SNAKE_CASE_					      =							[to_channel_dimension_format(a__					,							a__  ) for image in images]

								SCREAMING_SNAKE_CASE_					      =							{'pixel_values': images}
								return BatchFeature(data=a__					,							tensor_type=a__  )



def 	_lowerCAmelCase					( UpperCamelCase__:  Union[str, Any]    )			-> Dict:





	"""simple docstring"""







	return [
	    {
	        0: [1, 2],
	        1: [0, 2],
	        2: [0, 1, 3, 5],
	        3: [2, 4],
	        4: [3],
	        5: [2, 6, 8],
	        6: [5, 7],
	        7: [6, 8],
	        8: [5, 7],
	    },
	    {
	        0: [6],
	        1: [9],
	        2: [4, 5],
	        3: [4],
	        4: [2, 3],
	        5: [2],
	        6: [0, 7],
	        7: [6],
	        8: [],
	        9: [1],
	    },
	    {
	        0: [4],
	        1: [6],
	        2: [],
	        3: [5, 6, 7],
	        4: [0, 6],
	        5: [3, 8, 9],
	        6: [1, 3, 4, 7],
	        7: [3, 6, 8, 9],
	        8: [5, 7],
	        9: [5, 7],
	    },
	    {
	        0: [1, 3],
	        1: [0, 2, 4],
	        2: [1, 3, 4],
	        3: [0, 2, 4],
	        4: [1, 2, 3],
	    },
	][index]





def 	_lowerCAmelCase					( UpperCamelCase__:  dict[int, list[int]]    )			-> list[tuple[int, int]]:





	"""simple docstring"""







	A             =					0
	A             =					len(UpperCamelCase__    )  # No of vertices in graph
	A             =					[0] * n
	A             =					[False] * n

	def dfs(UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  str				,       UpperCamelCase__:  Union[str, Any]				,       UpperCamelCase__:  Any    ):
		A             =					True
		A             =					id_
		id_ += 1
		for to in graph[at]:
			if to == parent:
				pass
			elif not visited[to]:
				dfs(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       id_    )
				A             =					min(low[at]				,       low[to]    )
				if id_ <= low[to]:
					bridges.append((at, to) if at < to else (to, at)    )
			else:
				# This edge is a back edge and cannot be a bridge
				A             =					min(low[at]				,       low[to]    )

	A             =					[]
	for i in range(UpperCamelCase__    ):
		if not visited[i]:
			dfs(UpperCamelCase__				,       -1				,       UpperCamelCase__				,       id_    )
	return bridges


if __name__ == "__main__":
				import doctest

				doctest.testmod()



