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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


_lowerCamelCase       : Tuple      					=		logging.get_logger(__name__)





def 						__lowerCamelCase				(UpperCAmelCase__  :				Union[str, Any]	):





					if isinstance(__snake_case   ,			(list, tuple)	) and isinstance(videos[0]   ,			(list, tuple)	) and is_valid_image(videos[0][0]	):
										return videos

					elif isinstance(__snake_case   ,			(list, tuple)	) and is_valid_image(videos[0]	):
										return [videos]

					elif is_valid_image(__snake_case	):
										return [[videos]]

					raise ValueError(F"Could not make batched video from {videos}"	)


class     lowercase     (  a  ):
							lowercase__    :							List[str]										=				["""pixel_values"""]




							def __init__(    self :       Optional[Any]						,				_UpperCamelCase :       Dict = True						,				_UpperCamelCase :       Optional[int] = None						,				_UpperCamelCase :       List[Any] = PILImageResampling.BILINEAR						,				_UpperCamelCase :       Optional[Any] = True						,				_UpperCamelCase :       Tuple = None						,				_UpperCamelCase :       str = True						,				_UpperCamelCase :       int = 1 / 255						,				_UpperCamelCase :       Any = True						,				_UpperCamelCase :       str = None						,				_UpperCamelCase :       List[Any] = None						,				**_UpperCamelCase :       Dict						,				)       ->					None:



												'''simple docstring'''

												super().__init__(**__a   )
												SCREAMING_SNAKE_CASE           =							size if size is not None else {"shortest_edge": 224}
												SCREAMING_SNAKE_CASE           =							get_size_dict(__a						,				default_to_square=__a   )
												SCREAMING_SNAKE_CASE           =							crop_size if crop_size is not None else {"height": 224, "width": 224}
												SCREAMING_SNAKE_CASE           =							get_size_dict(__a						,				param_name="crop_size"   )

												SCREAMING_SNAKE_CASE           =							do_resize
												SCREAMING_SNAKE_CASE           =							size
												SCREAMING_SNAKE_CASE           =							do_center_crop
												SCREAMING_SNAKE_CASE           =							crop_size
												SCREAMING_SNAKE_CASE           =							resample
												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 IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =							image_std if image_std is not None else IMAGENET_STANDARD_STD




							def 						__snake_case(    self :       Union[str, Any]						,				_UpperCamelCase :       Optional[int]						,				_UpperCamelCase :       int						,				_UpperCamelCase :       Tuple = PILImageResampling.BILINEAR						,				_UpperCamelCase :       Optional[int] = None						,				**_UpperCamelCase :       str						,				)       ->					np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							get_size_dict(__a						,				default_to_square=__a   )
												if "shortest_edge" in size:
																	SCREAMING_SNAKE_CASE           =							get_resize_output_image_size(__a						,				size["shortest_edge"]						,				default_to_square=__a   )
												elif "height" in size and "width" in size:
																	SCREAMING_SNAKE_CASE           =							(size["height"], size["width"])
												else:
																	raise ValueError(F"Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"   )
												return resize(__a						,				size=__a						,				resample=__a						,				data_format=__a						,				**__a   )




							def 						__snake_case(    self :       List[str]						,				_UpperCamelCase :       Any						,				_UpperCamelCase :       Union[str, Any]						,				_UpperCamelCase :       Tuple = None						,				**_UpperCamelCase :       Optional[int]						,				)       ->					np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							get_size_dict(__a   )
												if "height" not in size or "width" not in size:
																	raise ValueError(F"Size must have \'height\' and \'width\' as keys. Got {size.keys()}"   )
												return center_crop(__a						,				size=(size["height"], size["width"])						,				data_format=__a						,				**__a   )




							def 						__snake_case(    self :       Union[str, Any]						,				_UpperCamelCase :       Dict						,				_UpperCamelCase :       Dict						,				_UpperCamelCase :       Tuple = None						,				**_UpperCamelCase :       Any						,				)       ->					Dict:



												'''simple docstring'''

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




							def 						__snake_case(    self :       Dict						,				_UpperCamelCase :       int						,				_UpperCamelCase :       Tuple						,				_UpperCamelCase :       Dict						,				_UpperCamelCase :       Any = None						,				**_UpperCamelCase :       Any						,				)       ->					np.ndarray:



												'''simple docstring'''

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




							def 						__snake_case(    self :       str						,				_UpperCamelCase :       int						,				_UpperCamelCase :       Tuple = None						,				_UpperCamelCase :       Optional[int] = None						,				_UpperCamelCase :       Dict = None						,				_UpperCamelCase :       Dict = None						,				_UpperCamelCase :       List[str] = None						,				_UpperCamelCase :       Tuple = None						,				_UpperCamelCase :       Dict = None						,				_UpperCamelCase :       Optional[int] = None						,				_UpperCamelCase :       int = None						,				_UpperCamelCase :       List[str] = None						,				_UpperCamelCase :       Optional[Any] = ChannelDimension.FIRST						,				)       ->					np.ndarray:



												'''simple docstring'''

												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.
												SCREAMING_SNAKE_CASE           =							to_numpy_array(__a   )

												if do_resize:
																	SCREAMING_SNAKE_CASE           =							self.resize(image=__a						,				size=__a						,				resample=__a   )

												if do_center_crop:
																	SCREAMING_SNAKE_CASE           =							self.center_crop(__a						,				size=__a   )

												if do_rescale:
																	SCREAMING_SNAKE_CASE           =							self.rescale(image=__a						,				scale=__a   )

												if do_normalize:
																	SCREAMING_SNAKE_CASE           =							self.normalize(image=__a						,				mean=__a						,				std=__a   )

												SCREAMING_SNAKE_CASE           =							to_channel_dimension_format(__a						,				__a   )
												return image





							def 						__snake_case(    self :       List[str]						,				_UpperCamelCase :       List[Any]						,				_UpperCamelCase :       Tuple = None						,				_UpperCamelCase :       Tuple = None						,				_UpperCamelCase :       Any = None						,				_UpperCamelCase :       List[str] = None						,				_UpperCamelCase :       Any = None						,				_UpperCamelCase :       Any = None						,				_UpperCamelCase :       Tuple = None						,				_UpperCamelCase :       Tuple = None						,				_UpperCamelCase :       Tuple = None						,				_UpperCamelCase :       List[Any] = None						,				_UpperCamelCase :       Dict = None						,				_UpperCamelCase :       Dict = ChannelDimension.FIRST						,				**_UpperCamelCase :       str						,				)       ->					PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =							do_resize if do_resize is not None else self.do_resize
												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           =							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           =							size if size is not None else self.size
												SCREAMING_SNAKE_CASE           =							get_size_dict(__a						,				default_to_square=__a   )
												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"   )

												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."   )

												SCREAMING_SNAKE_CASE           =							make_batched(__a   )

												SCREAMING_SNAKE_CASE           =							[
												    [
												        self._preprocess_image(
												            image=__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=__a						,				image_std=__a						,				data_format=__a						,				)
												        for img in video
												    ]
												    for video in videos
												]

												SCREAMING_SNAKE_CASE           =							{"pixel_values": videos}
												return BatchFeature(data=__a						,				tensor_type=__a   )






"""simple docstring"""





import heapq



def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					# for each node and his adjacency list add them and the rank of the node to queue
					# using heapq module the queue will be filled like a Priority Queue
					# heapq works with a min priority queue, so I used -1*len(v) to build it
					for key, value in graph.items():
										# O(log(n))
										heapq.heappush(__snake_case,	[-1 * len(__snake_case      ), (key, value)]      )

					# chosen_vertices = set of chosen vertices
					_UpperCamelCase									=    set()

					# while queue isn't empty and there are still edges
					#   (queue[0][0] is the rank of the node with max rank)
					while queue and queue[0][0] != 0:
										# extract vertex with max rank from queue and add it to chosen_vertices
										_UpperCamelCase									=    heapq.heappop(__snake_case      )[1][0]
										chosen_vertices.add(__snake_case      )

										# Remove all arcs adjacent to argmax
										for elem in queue:
															# if v haven't adjacent node, skip
															if elem[0] == 0:
																				continue
															# if argmax is reachable from elem
															# remove argmax from elem's adjacent list and update his rank
															if argmax in elem[1][1]:
																				_UpperCamelCase									=    elem[1][1].index(__snake_case      )
																				del elem[1][1][index]
																				elem[0] += 1
        # re-order the queue
										heapq.heapify(__snake_case      )
					return chosen_vertices


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a								=						{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
				print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")






