




'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase    :    Any			     =  logging.get_logger(__name__)

_lowercase    :    Dict			     =  {
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Dict							= "beit"




				def __init__(	self    :				List[Any]   ,					lowerCAmelCase    :				str=8192   ,					lowerCAmelCase    :				Dict=768   ,					lowerCAmelCase    :				int=12   ,					lowerCAmelCase    :				Optional[Any]=12   ,					lowerCAmelCase    :				Optional[int]=3072   ,					lowerCAmelCase    :				Dict="gelu"   ,					lowerCAmelCase    :				Optional[int]=0.0   ,					lowerCAmelCase    :				Optional[int]=0.0   ,					lowerCAmelCase    :				Optional[Any]=0.02   ,					lowerCAmelCase    :				Union[str, Any]=1E-12   ,					lowerCAmelCase    :				Optional[Any]=224   ,					lowerCAmelCase    :				Union[str, Any]=16   ,					lowerCAmelCase    :				str=3   ,					lowerCAmelCase    :				str=False   ,					lowerCAmelCase    :				str=False   ,					lowerCAmelCase    :				int=False   ,					lowerCAmelCase    :				List[str]=False   ,					lowerCAmelCase    :				Dict=0.1   ,					lowerCAmelCase    :				str=0.1   ,					lowerCAmelCase    :				int=True   ,					lowerCAmelCase    :				Any=[3, 5, 7, 11]   ,					lowerCAmelCase    :				Union[str, Any]=[1, 2, 3, 6]   ,					lowerCAmelCase    :				List[str]=True   ,					lowerCAmelCase    :				str=0.4   ,					lowerCAmelCase    :				Optional[Any]=256   ,					lowerCAmelCase    :				Optional[Any]=1   ,					lowerCAmelCase    :				Any=False   ,					lowerCAmelCase    :				Any=255   ,					**lowerCAmelCase    :				Optional[Any]   ,					)->  Optional[Any]:

										"""simple docstring"""




										super().__init__(**lowerCAmelCase   )

										UpperCAmelCase         =			vocab_size
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_hidden_layers
										UpperCAmelCase         =			num_attention_heads
										UpperCAmelCase         =			intermediate_size
										UpperCAmelCase         =			hidden_act
										UpperCAmelCase         =			hidden_dropout_prob
										UpperCAmelCase         =			attention_probs_dropout_prob
										UpperCAmelCase         =			initializer_range
										UpperCAmelCase         =			layer_norm_eps

										UpperCAmelCase         =			image_size
										UpperCAmelCase         =			patch_size
										UpperCAmelCase         =			num_channels
										UpperCAmelCase         =			use_mask_token
										UpperCAmelCase         =			use_absolute_position_embeddings
										UpperCAmelCase         =			use_relative_position_bias
										UpperCAmelCase         =			use_shared_relative_position_bias
										UpperCAmelCase         =			layer_scale_init_value
										UpperCAmelCase         =			drop_path_rate
										UpperCAmelCase         =			use_mean_pooling
										# decode head attributes (semantic segmentation)
										UpperCAmelCase         =			out_indices
										UpperCAmelCase         =			pool_scales
										# auxiliary head attributes (semantic segmentation)
										UpperCAmelCase         =			use_auxiliary_head
										UpperCAmelCase         =			auxiliary_loss_weight
										UpperCAmelCase         =			auxiliary_channels
										UpperCAmelCase         =			auxiliary_num_convs
										UpperCAmelCase         =			auxiliary_concat_input
										UpperCAmelCase         =			semantic_loss_ignore_index







class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Dict							= version.parse("1.11" )




				@property
				def   a__(	self    :				str   )->  Mapping[str, Mapping[int, str]]:

										"""simple docstring"""




										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										    ]   )





				@property
				def   a__(	self    :				Optional[int]   )->  float:

										"""simple docstring"""




										return 1E-4









'''simple docstring'''



import heapq


def    lowerCamelCase__   ( A     :    dict  ):




						'''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(A   ,     [-1 * len(A  ), (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(A  )[1][0]
												chosen_vertices.add(A  )

												# 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(A  )
																								del elem[1][1][index]
																								elem[0] += 1
        # re-order the queue
												heapq.heapify(A  )
						return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowercase    :    Optional[int]			     =  {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)}""")





