




'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase    :    Tuple			     =  logging.get_logger(__name__)

_lowercase    :    List[str]			     =  {"""vocab_file""": """sentencepiece.bpe.model"""}

_lowercase    :    int			     =  {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

_lowercase    :    Optional[int]			     =  {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

_lowercase    :    Dict			     =  """▁"""






class  UpperCamelCase__(     UpperCamelCase__ ):
				__magic_name__       :   str							= VOCAB_FILES_NAMES
				__magic_name__       :   str							= PRETRAINED_VOCAB_FILES_MAP
				__magic_name__       :   Union[str, Any]							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__       :   Dict							= ["input_ids", "attention_mask"]




				def __init__(	self    :				List[Any]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Any="<s>"   ,					lowerCAmelCase    :				List[Any]="</s>"   ,					lowerCAmelCase    :				Optional[Any]="</s>"   ,					lowerCAmelCase    :				Optional[int]="<s>"   ,					lowerCAmelCase    :				Tuple="<unk>"   ,					lowerCAmelCase    :				List[Any]="<pad>"   ,					lowerCAmelCase    :				Dict="<mask>"   ,					lowerCAmelCase    :				Optional[Dict[str, Any]] = None   ,					**lowerCAmelCase    :				Tuple   ,					)->  None:

										"""simple docstring"""




										UpperCAmelCase         =			AddedToken(__A   ,					lstrip=__A   ,					rstrip=__A   ) if isinstance(__A   ,					__A   ) else mask_token

										UpperCAmelCase         =			{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=__A   ,					eos_token=__A   ,					unk_token=__A   ,					sep_token=__A   ,					cls_token=__A   ,					pad_token=__A   ,					mask_token=__A   ,					sp_model_kwargs=self.sp_model_kwargs   ,					**__A   ,					)

										UpperCAmelCase         =			vocab_file
										UpperCAmelCase         =			spm.SentencePieceProcessor(**self.sp_model_kwargs   )
										self.sp_model.Load(str(__A   )   )

										UpperCAmelCase         =			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

										UpperCAmelCase         =			len(self.sp_model   ) - 1
										UpperCAmelCase         =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}




				def   a__(	self    :				Union[str, Any]   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				Optional[List[int]] = None   )->  List[int]:

										"""simple docstring"""




										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										UpperCAmelCase         =			[self.cls_token_id]
										UpperCAmelCase         =			[self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep




				def   a__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				Optional[List[int]] = None   ,					lowerCAmelCase    :				bool = False   )->  List[int]:

										"""simple docstring"""




										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=__A   ,					token_ids_a=__A   ,					already_has_special_tokens=__A   )

										if token_ids_a is None:
																return [1] + ([0] * len(__A   )) + [1]
										return [1] + ([0] * len(__A   )) + [1, 1] + ([0] * len(__A   )) + [1]




				def   a__(	self    :				Tuple   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				Optional[List[int]] = None   )->  List[int]:

										"""simple docstring"""




										UpperCAmelCase         =			[self.sep_token_id]
										UpperCAmelCase         =			[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 + sep + token_ids_a + sep   ) * [0]




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

										"""simple docstring"""




										return len(self.sp_model   )




				def   a__(	self    :				Optional[Any]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			{self.convert_ids_to_tokens(__A   ): i for i in range(self.vocab_size   )}
										vocab.update(self.added_tokens_encoder   )
										return vocab




				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				str   )->  List[str]:

										"""simple docstring"""




										return self.sp_model.encode(__A   ,					out_type=__A   )




				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										UpperCAmelCase         =			self.sp_model.PieceToId(__A   )

										return spm_id if spm_id else self.unk_token_id




				def   a__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]   )->  int:

										"""simple docstring"""




										if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(__A   )




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Union[str, Any]   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			[]
										UpperCAmelCase         =			''''''
										UpperCAmelCase         =			False
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						if not prev_is_special:
																												out_string += " "
																						out_string += self.sp_model.decode(__A   ) + token
																						UpperCAmelCase         =			True
																						UpperCAmelCase         =			[]
																else:
																						current_sub_tokens.append(__A   )
																						UpperCAmelCase         =			False
										out_string += self.sp_model.decode(__A   )
										return out_string.strip()




				def __getstate__(	self    :				List[str]   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			self.__dict__.copy()
										UpperCAmelCase         =			None
										return state




				def __setstate__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				List[Any]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			d

										# for backward compatibility
										if not hasattr(self   ,					'''sp_model_kwargs'''   ):
																UpperCAmelCase         =			{}

										UpperCAmelCase         =			spm.SentencePieceProcessor(**self.sp_model_kwargs   )
										self.sp_model.Load(self.vocab_file   )





				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[str] = None   )->  Tuple[str]:

										"""simple docstring"""




										if not os.path.isdir(__A   ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
																return
										UpperCAmelCase         =			os.path.join(
										    __A   ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

										if os.path.abspath(self.vocab_file   ) != os.path.abspath(__A   ) and os.path.isfile(self.vocab_file   ):
																copyfile(self.vocab_file   ,					__A   )
										elif not os.path.isfile(self.vocab_file   ):
																with open(__A   ,					'''wb'''   ) as fi:
																						UpperCAmelCase         =			self.sp_model.serialized_model_proto()
																						fi.write(__A   )

										return (out_vocab_file,)









'''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)}""")





