



def snake_case__	(      __SCREAMING_SNAKE_CASE			)		->				Optional[Any]:
		UpperCAmelCase_						       =					set()
		# edges = list of graph's edges
		UpperCAmelCase_						       =					get_edges(UpperCamelCase__			)

		# While there are still elements in edges list, take an arbitrary edge
		# (from_node, to_node) and add his extremity to chosen_vertices and then
		# remove all arcs adjacent to the from_node and to_node
		while edges:
				UpperCAmelCase_						       =					edges.pop()
				chosen_vertices.add(UpperCamelCase__			)
				chosen_vertices.add(UpperCamelCase__			)
				for edge in edges.copy():
						if from_node in edge or to_node in edge:
								edges.discard(UpperCamelCase__			)
		return chosen_vertices







def snake_case__	(      __SCREAMING_SNAKE_CASE			)		->				Optional[int]:
		UpperCAmelCase_						       =					set()
		for from_node, to_nodes in graph.items():
				for to_node in to_nodes:
						edges.add((from_node, to_node)			)
		return edges


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
			# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")





'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_			:  Optional[int]										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[str]										=   {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		List[Any]     				=    '''fnet'''


 def __init__(   self   ,   __lowercase=32_000   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu_new"   ,   __lowercase=0.1   ,   __lowercase=512   ,   __lowercase=4   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=False   ,   __lowercase=512   ,   __lowercase=3   ,   __lowercase=1   ,   __lowercase=2   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(pad_token_id=__lowercase   ,   bos_token_id=__lowercase   ,   eos_token_id=__lowercase   ,   **__lowercase    )

  __A	:		Union[str, Any]	  =      vocab_size
  __A	:		Dict	  =      max_position_embeddings
  __A	:		List[str]	  =      hidden_size
  __A	:		Tuple	  =      num_hidden_layers
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		Dict	  =      hidden_act
  __A	:		List[str]	  =      hidden_dropout_prob
  __A	:		str	  =      initializer_range
  __A	:		Dict	  =      type_vocab_size
  __A	:		int	  =      layer_norm_eps
  __A	:		Tuple	  =      use_tpu_fourier_optimizations
  __A	:		Optional[Any]	  =      tpu_short_seq_length






