"""simple docstring"""







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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)



class  _lowerCamelCase			(__lowerCamelCase     ):
				_snake_case     =  ["pixel_values"]







				def __init__( self    :       Optional[int]			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Optional[Dict[str, int]] = None			,					lowerCamelCase_    :       PILImageResampling = PILImageResampling.BILINEAR			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Dict[str, int] = None			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Union[int, float] = 1 / 2_5_5			,					lowerCamelCase_    :       bool = True			,					lowerCamelCase_    :       Optional[Union[float, List[float]]] = None			,					lowerCamelCase_    :       Optional[Union[float, List[float]]] = None			,					**lowerCamelCase_    :       int			,					):

					"""simple docstring"""






					super().__init__(**lowerCamelCase_						)
					_lowercase :    Optional[Any]							=							size if size is not None else {'shortest_edge': 2_5_6}
					_lowercase :    Optional[int]							=							get_size_dict(lowerCamelCase_			,					default_to_square=lowerCamelCase_						)
					_lowercase :    List[str]							=							crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
					_lowercase :    Any							=							get_size_dict(lowerCamelCase_						)
					_lowercase :    Optional[int]							=							do_resize
					_lowercase :    Optional[int]							=							size
					_lowercase :    Union[str, Any]							=							resample
					_lowercase :    Optional[Any]							=							do_center_crop
					_lowercase :    Union[str, Any]							=							crop_size
					_lowercase :    Any							=							do_rescale
					_lowercase :    Tuple							=							rescale_factor
					_lowercase :    Optional[int]							=							do_normalize
					_lowercase :    int							=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					_lowercase :    Any							=							image_std if image_std is not None else IMAGENET_STANDARD_STD







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       np.ndarray			,					lowerCamelCase_    :       Dict[str, int]			,					lowerCamelCase_    :       PILImageResampling = PILImageResampling.BICUBIC			,					lowerCamelCase_    :       Optional[Union[str, ChannelDimension]] = None			,					**lowerCamelCase_    :       Tuple			,					):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Dict			,					lowerCamelCase_    :       np.ndarray			,					lowerCamelCase_    :       Dict[str, int]			,					lowerCamelCase_    :       Optional[Union[str, ChannelDimension]] = None			,					**lowerCamelCase_    :       str			,					):

					"""simple docstring"""






					_lowercase :    Tuple							=							get_size_dict(lowerCamelCase_						)
					return center_crop(lowerCamelCase_			,					size=(size['height'], size['width'])			,					data_format=lowerCamelCase_			,					**lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       np.ndarray			,					lowerCamelCase_    :       float			,					lowerCamelCase_    :       Optional[Union[str, ChannelDimension]] = None			,					**lowerCamelCase_    :       Optional[Any]						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       np.ndarray			,					lowerCamelCase_    :       Union[float, List[float]]			,					lowerCamelCase_    :       Union[float, List[float]]			,					lowerCamelCase_    :       Optional[Union[str, ChannelDimension]] = None			,					**lowerCamelCase_    :       List[Any]			,					):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       List[Any]			,					lowerCamelCase_    :       ImageInput			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Dict[str, int] = None			,					lowerCamelCase_    :       PILImageResampling = None			,					lowerCamelCase_    :       bool = None			,					lowerCamelCase_    :       Dict[str, int] = None			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Optional[float] = None			,					lowerCamelCase_    :       Optional[bool] = None			,					lowerCamelCase_    :       Optional[Union[float, List[float]]] = None			,					lowerCamelCase_    :       Optional[Union[float, List[float]]] = None			,					lowerCamelCase_    :       Optional[Union[str, TensorType]] = None			,					lowerCamelCase_    :       Union[str, ChannelDimension] = ChannelDimension.FIRST			,					**lowerCamelCase_    :       int			,					):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							do_resize if do_resize is not None else self.do_resize
					_lowercase :    List[Any]							=							size if size is not None else self.size
					_lowercase :    Union[str, Any]							=							get_size_dict(lowerCamelCase_			,					default_to_square=lowerCamelCase_						)
					_lowercase :    Any							=							resample if resample is not None else self.resample
					_lowercase :    Tuple							=							do_center_crop if do_center_crop is not None else self.do_center_crop
					_lowercase :    List[Any]							=							crop_size if crop_size is not None else self.crop_size
					_lowercase :    Tuple							=							get_size_dict(lowerCamelCase_						)
					_lowercase :    List[Any]							=							do_rescale if do_rescale is not None else self.do_rescale
					_lowercase :    int							=							rescale_factor if rescale_factor is not None else self.rescale_factor
					_lowercase :    int							=							do_normalize if do_normalize is not None else self.do_normalize
					_lowercase :    str							=							image_mean if image_mean is not None else self.image_mean
					_lowercase :    List[Any]							=							image_std if image_std is not None else self.image_std

					_lowercase :    Union[str, Any]							=							make_list_of_images(lowerCamelCase_						)

					if not valid_images(lowerCamelCase_						):
						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.
					_lowercase :    Optional[Any]							=							[to_numpy_array(lowerCamelCase_						) for image in images]

					if do_resize:
						_lowercase :    Optional[Any]							=							[self.resize(image=lowerCamelCase_			,					size=lowerCamelCase_			,					resample=lowerCamelCase_						) for image in images]

					if do_center_crop:
						_lowercase :    List[Any]							=							[self.center_crop(image=lowerCamelCase_			,					size=lowerCamelCase_						) for image in images]

					if do_rescale:
						_lowercase :    str							=							[self.rescale(image=lowerCamelCase_			,					scale=lowerCamelCase_						) for image in images]

					if do_normalize:
						_lowercase :    Any							=							[self.normalize(image=lowerCamelCase_			,					mean=lowerCamelCase_			,					std=lowerCamelCase_						) for image in images]

					_lowercase :    int							=							[to_channel_dimension_format(lowerCamelCase_			,					lowerCamelCase_						) for image in images]

					_lowercase :    Tuple							=							{'pixel_values': images}
					return BatchFeature(data=lowerCamelCase_			,					tensor_type=lowerCamelCase_						)




"""simple docstring"""







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(__UpperCAmelCase			)			)


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	if index == len(__UpperCAmelCase			):
		return True

	# Recursive Step
	for i in range(__UpperCAmelCase			):
		if valid_coloring(graph[index]     ,__UpperCAmelCase     ,__UpperCAmelCase			):
			# Color current vertex
			_lowercase :    str							=							i
			# Validate coloring
			if util_color(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,index + 1			):
				return True
			# Backtrack
			_lowercase :    List[str]							=							-1
	return False







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[-1] * len(__UpperCAmelCase			)

	if util_color(__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,0			):
		return colored_vertices

	return []



