


"""simple docstring"""






import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase     =  logging.get_logger(__name__)

_lowerCamelCase     =  {'vocab_file': 'sentencepiece.model'}

_lowerCamelCase     =  {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

_lowerCamelCase     =  {
    'google/rembert': 256,
}


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Union[str, Any]      							=					VOCAB_FILES_NAMES
				_lowerCAmelCase      :      int      							=					PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase      :      Union[str, Any]      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=False       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__="[CLS]"       ,		UpperCAmelCase__="[SEP]"       ,		UpperCAmelCase__="[UNK]"       ,		UpperCAmelCase__="[SEP]"       ,		UpperCAmelCase__="[PAD]"       ,		UpperCAmelCase__="[CLS]"       ,		UpperCAmelCase__="[MASK]"       ,		**UpperCAmelCase__       ,		):
								super().__init__(
								    do_lower_case=UpperCAmelCase__       ,		remove_space=UpperCAmelCase__       ,		keep_accents=UpperCAmelCase__       ,		bos_token=UpperCAmelCase__       ,		eos_token=UpperCAmelCase__       ,		unk_token=UpperCAmelCase__       ,		sep_token=UpperCAmelCase__       ,		pad_token=UpperCAmelCase__       ,		cls_token=UpperCAmelCase__       ,		mask_token=UpperCAmelCase__       ,		**UpperCAmelCase__       ,		)

								SCREAMING_SNAKE_CASE__     							=				do_lower_case
								SCREAMING_SNAKE_CASE__     							=				remove_space
								SCREAMING_SNAKE_CASE__     							=				keep_accents
								SCREAMING_SNAKE_CASE__     							=				vocab_file

								SCREAMING_SNAKE_CASE__     							=				spm.SentencePieceProcessor()
								self.sp_model.Load(UpperCAmelCase__     )



				@property
				def   lowerCAmelCase__		(						self     ):
								return len(self.sp_model     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				{self.convert_ids_to_tokens(UpperCAmelCase__     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab



				def __getstate__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.__dict__.copy()
								SCREAMING_SNAKE_CASE__     							=				None
								return state



				def __setstate__(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				d
								SCREAMING_SNAKE_CASE__     							=				spm.SentencePieceProcessor()
								self.sp_model.Load(self.vocab_file     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=False     ):
								SCREAMING_SNAKE_CASE__     							=				self.sp_model.EncodeAsPieces(UpperCAmelCase__     )
								return pieces



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								return self.sp_model.PieceToId(UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								return self.sp_model.IdToPiece(UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.sp_model.decode_pieces(UpperCAmelCase__     )
								return out_string



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				[self.sep_token_id]
								SCREAMING_SNAKE_CASE__     							=				[self.cls_token_id]
								if token_ids_a is None:
												return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = False     ):

								if already_has_special_tokens:
												if token_ids_a is not None:
																raise ValueError(
																    "You should not supply a second sequence if the provided sequence of "
																    "ids is already formatted with special tokens for the model."     )
												return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
												return [1] + ([0] * len(UpperCAmelCase__     )) + [1] + ([0] * len(UpperCAmelCase__     )) + [1]
								return [1] + ([0] * len(UpperCAmelCase__     )) + [1]



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				[self.sep_token_id]
								SCREAMING_SNAKE_CASE__     							=				[self.cls_token_id]

								if token_ids_a is None:
												return len(cls + token_ids_a + sep     ) * [0]
								return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								if not os.path.isdir(UpperCAmelCase__     ):
												logger.error("Vocabulary path ({}) should be a directory".format(UpperCAmelCase__     )     )
												return
								SCREAMING_SNAKE_CASE__     							=				os.path.join(
								    UpperCAmelCase__       ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

								if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCAmelCase__     ):
												copyfile(self.vocab_file       ,		UpperCAmelCase__     )

								return (out_vocab_file,)




"""simple docstring"""






import heapq


def __lowercase     (      lowerCamelCase_  :      dict  ):
				SCREAMING_SNAKE_CASE__     							=				[]

				# 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(lowerCamelCase_						,					[-1 * len(lowerCamelCase_  ), (key, value)]  )

				# chosen_vertices = set of chosen vertices
				SCREAMING_SNAKE_CASE__     							=				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
								SCREAMING_SNAKE_CASE__     							=				heapq.heappop(lowerCamelCase_  )[1][0]
								chosen_vertices.add(lowerCamelCase_  )

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCamelCase     =  {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)}""")
