

from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a		=logging.get_logger(__name__)






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  Any						      =      ['''pixel_values''']
							def __init__(		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = PILImageResampling.BICUBIC   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int] = None   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Union[int, float] = 1 / 2_5_5   ,SCREAMING_SNAKE_CASE__		:			bool = True   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD   ,**SCREAMING_SNAKE_CASE__		:			int   ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = size if size is not None else {'shortest_edge': 2_2_4}
								__lowerCamelCase				:				List[Any]					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[Any]					       = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__lowerCamelCase				:				str					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,param_name='crop_size')

								__lowerCamelCase				:				Any					       = do_resize
								__lowerCamelCase				:				Optional[Any]					       = size
								__lowerCamelCase				:				List[Any]					       = resample
								__lowerCamelCase				:				Optional[int]					       = do_center_crop
								__lowerCamelCase				:				Optional[int]					       = crop_size
								__lowerCamelCase				:				List[str]					       = do_rescale
								__lowerCamelCase				:				Optional[Any]					       = rescale_factor
								__lowerCamelCase				:				Dict					       = do_normalize
								__lowerCamelCase				:				int					       = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								__lowerCamelCase				:				Union[str, Any]					       = image_std if image_std is not None else IMAGENET_DEFAULT_STD
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int]   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = PILImageResampling.BICUBIC   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			List[Any]   ,):
								__lowerCamelCase				:				Any					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)
								# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
								if "shortest_edge" in size:
									__lowerCamelCase				:				List[str]					       = int((2_5_6 / 2_2_4) * size['shortest_edge'])
									__lowerCamelCase				:				Union[str, Any]					       = get_resize_output_image_size(SCREAMING_SNAKE_CASE__   ,size=SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase				:				str					       = {'height': output_size[0], 'width': output_size[1]}
								if "height" not in size_dict or "width" not in size_dict:
									raise ValueError(
									    F"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}")
								return resize(
								    SCREAMING_SNAKE_CASE__   ,size=(size_dict['height'], size_dict['width'])   ,resample=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Dict[str, int]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			List[str]   ,):
								__lowerCamelCase				:				List[Any]					       = get_size_dict(SCREAMING_SNAKE_CASE__)
								if "height" not in size or "width" not in size:
									raise ValueError(F"Size dict must have keys 'height' and 'width'. Got {size.keys()}")
								return center_crop(SCREAMING_SNAKE_CASE__   ,size=(size['height'], size['width'])   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Union[int, float]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			Dict   ,):
								return rescale(SCREAMING_SNAKE_CASE__   ,scale=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			np.ndarray   ,SCREAMING_SNAKE_CASE__		:			Union[float, List[float]]   ,SCREAMING_SNAKE_CASE__		:			Union[float, List[float]]   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[str, ChannelDimension]] = None   ,**SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,):
								return normalize(SCREAMING_SNAKE_CASE__   ,mean=SCREAMING_SNAKE_CASE__   ,std=SCREAMING_SNAKE_CASE__   ,data_format=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)


							def     lowerCAmelCase     (		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			ImageInput   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Dict[str, int]] = None   ,SCREAMING_SNAKE_CASE__		:			PILImageResampling = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Dict[str, int]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[float] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[bool] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, Iterable[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[Union[float, Iterable[float]]] = None   ,SCREAMING_SNAKE_CASE__		:			Optional[TensorType] = None   ,SCREAMING_SNAKE_CASE__		:			ChannelDimension = ChannelDimension.FIRST   ,**SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,):
								__lowerCamelCase				:				Union[str, Any]					       = do_resize if do_resize is not None else self.do_resize
								__lowerCamelCase				:				Tuple					       = resample if resample is not None else self.resample
								__lowerCamelCase				:				str					       = do_center_crop if do_center_crop is not None else self.do_center_crop
								__lowerCamelCase				:				Union[str, Any]					       = do_rescale if do_rescale is not None else self.do_rescale
								__lowerCamelCase				:				Any					       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCamelCase				:				Union[str, Any]					       = do_normalize if do_normalize is not None else self.do_normalize
								__lowerCamelCase				:				Dict					       = image_mean if image_mean is not None else self.image_mean
								__lowerCamelCase				:				Dict					       = image_std if image_std is not None else self.image_std

								__lowerCamelCase				:				List[Any]					       = size if size is not None else self.size
								__lowerCamelCase				:				Any					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,default_to_square=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Union[str, Any]					       = crop_size if crop_size is not None else self.crop_size
								__lowerCamelCase				:				Any					       = get_size_dict(SCREAMING_SNAKE_CASE__   ,param_name='crop_size')

								__lowerCamelCase				:				Optional[Any]					       = make_list_of_images(SCREAMING_SNAKE_CASE__)

								if not valid_images(SCREAMING_SNAKE_CASE__):
									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.')

								# All transformations expect numpy arrays.
								__lowerCamelCase				:				str					       = [to_numpy_array(SCREAMING_SNAKE_CASE__) for image in images]

								if do_resize:
									__lowerCamelCase				:				Any					       = [self.resize(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								if do_center_crop:
									__lowerCamelCase				:				Union[str, Any]					       = [self.center_crop(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								if do_rescale:
									__lowerCamelCase				:				Any					       = [self.rescale(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								if do_normalize:
									__lowerCamelCase				:				List[Any]					       = [self.normalize(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase				:				Optional[Any]					       = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__) for image in images]

								__lowerCamelCase				:				Union[str, Any]					       = {'pixel_values': images}
								return BatchFeature(data=SCREAMING_SNAKE_CASE__   ,tensor_type=SCREAMING_SNAKE_CASE__)







# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def        UpperCamelCase_(							_snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[False] * len(_snake_case	)
			__a        =[-1] * len(_snake_case	)

			def dfs(_snake_case   :  Dict       ,     _snake_case   :  Any	):
						__a        =True
						__a        =c
						for u in graph[v]:
									if not visited[u]:
												dfs(_snake_case       ,     1 - c	)

			for i in range(len(_snake_case	)	):
						if not visited[i]:
									dfs(_snake_case       ,     0	)

			for i in range(len(_snake_case	)	):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
_lowerCAmelCase					:			int  				=			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))






