


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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


_lowercase							:   Optional[Any]						=						logging.get_logger(__name__)






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			['pixel_values']

				def __init__(    self					, a__ = True					, a__ = None					, a__ = PIL.Image.BICUBIC					, a__ = True					, a__ = None					, a__ = 1 / 255					, a__ = True					, a__ = True					, a__ = None					, a__ = None					, **a__					, )						->					None:
					super().__init__(**a__ )
					A             =					size if size is not None else {"""height""": 256, """width""": 256}
					A             =					get_size_dict(a__ )
					A             =					crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
					A             =					get_size_dict(a__					, param_name="""crop_size""" )

					A             =					do_resize
					A             =					size
					A             =					resample
					A             =					do_center_crop
					A             =					crop_size
					A             =					do_rescale
					A             =					rescale_factor
					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					, a__					, a__					, a__ = PIL.Image.BICUBIC					, a__ = None					, **a__					, )						->					np.ndarray:
					A             =					get_size_dict(a__ )
					if "height" not in size or "width" not in size:
						raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}' )
					return resize(
					    a__					, size=(size["""height"""], size["""width"""])					, resample=a__					, data_format=a__					, **a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ = None					, **a__					, )						->					np.ndarray:
					A             =					get_size_dict(a__ )
					if "height" not in size or "width" not in size:
						raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}' )
					return center_crop(a__					, size=(size["""height"""], size["""width"""])					, data_format=a__					, **a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__ = None					, **a__					, )						->					Any:
					return rescale(a__					, scale=a__					, data_format=a__					, **a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__					, a__					, a__ = None					, **a__					, )						->					np.ndarray:
					return normalize(a__					, mean=a__					, std=a__					, data_format=a__					, **a__ )

				def   _UpperCAmelCase       (    self					, a__					, a__ = None					, a__ = None					, a__=None					, a__ = None					, a__ = None					, a__ = None					, a__ = None					, a__ = None					, a__ = None					, a__ = None					, a__ = None					, a__ = ChannelDimension.FIRST					, **a__					, )						->					PIL.Image.Image:
					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             =					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__ )
					A             =					crop_size if crop_size is not None else self.crop_size
					A             =					get_size_dict(a__					, param_name="""crop_size""" )

					A             =					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 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.
					A             =					[to_numpy_array(a__ ) for image in images]

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

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

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

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

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

					A             =					{"""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()



