





'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE   =      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
_SCREAMING_SNAKE_CASE   =      {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

_SCREAMING_SNAKE_CASE   =      {
    '''RUCAIBox/mvp''': 1_0_2_4,
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			int     =    VOCAB_FILES_NAMES
	a      :			List[str]     =    PRETRAINED_VOCAB_FILES_MAP
	a      :			Optional[int]     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a      :			str     =    ["input_ids", "attention_mask"]
	a      :			int     =    MvpTokenizer


	def __init__(self       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase="replace"       ,_lowerCamelCase="<s>"       ,_lowerCamelCase="</s>"       ,_lowerCamelCase="</s>"       ,_lowerCamelCase="<s>"       ,_lowerCamelCase="<unk>"       ,_lowerCamelCase="<pad>"       ,_lowerCamelCase="<mask>"       ,_lowerCamelCase=False       ,_lowerCamelCase=True       ,**_lowerCamelCase       ,)							->							Optional[int]:







				'''simple docstring'''





				super().__init__(
				    _lowerCamelCase       ,_lowerCamelCase       ,tokenizer_file=_lowerCamelCase       ,errors=_lowerCamelCase       ,bos_token=_lowerCamelCase       ,eos_token=_lowerCamelCase       ,sep_token=_lowerCamelCase       ,cls_token=_lowerCamelCase       ,unk_token=_lowerCamelCase       ,pad_token=_lowerCamelCase       ,mask_token=_lowerCamelCase       ,add_prefix_space=_lowerCamelCase       ,trim_offsets=_lowerCamelCase       ,**_lowerCamelCase       ,)

				__lowercase			  =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
				if pre_tok_state.get('''add_prefix_space'''       ,_lowerCamelCase	) != add_prefix_space:
							__lowercase			  =							getattr(_lowerCamelCase       ,pre_tok_state.pop('''type'''	)	)
							__lowercase			  =							add_prefix_space
							__lowercase			  =							pre_tok_class(**_lowerCamelCase	)

				__lowercase			  =							add_prefix_space

				# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
				__lowercase			  =							'''post_processor'''
				__lowercase			  =							getattr(self.backend_tokenizer       ,_lowerCamelCase       ,_lowerCamelCase	)
				if tokenizer_component_instance:
							__lowercase			  =							json.loads(tokenizer_component_instance.__getstate__()	)

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
										__lowercase			  =							tuple(state['''sep''']	)
							if "cls" in state:
										__lowercase			  =							tuple(state['''cls''']	)

							__lowercase			  =							False

							if state.get('''add_prefix_space'''       ,_lowerCamelCase	) != add_prefix_space:
										__lowercase			  =							add_prefix_space
										__lowercase			  =							True

							if state.get('''trim_offsets'''       ,_lowerCamelCase	) != trim_offsets:
										__lowercase			  =							trim_offsets
										__lowercase			  =							True

							if changes_to_apply:
										__lowercase			  =							getattr(_lowerCamelCase       ,state.pop('''type'''	)	)
										__lowercase			  =							component_class(**_lowerCamelCase	)
										setattr(self.backend_tokenizer       ,_lowerCamelCase       ,_lowerCamelCase	)


	@property
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				if self._mask_token is None:
							if self.verbose:
										logger.error('''Using mask_token, but it is not set yet.'''	)
							return None
				return str(self._mask_token	)


	@mask_token.setter
	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase			  =							AddedToken(_lowerCamelCase       ,lstrip=_lowerCamelCase       ,rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase       ,_lowerCamelCase	) else value
				__lowercase			  =							value


	def   _UpperCAmelCase       (self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							BatchEncoding:







				'''simple docstring'''





				__lowercase			  =							kwargs.get('''is_split_into_words'''       ,_lowerCamelCase	)

				if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
							    '''to use it with pretokenized inputs.'''	)

				return super()._batch_encode_plus(*_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							BatchEncoding:







				'''simple docstring'''





				__lowercase			  =							kwargs.get('''is_split_into_words'''       ,_lowerCamelCase	)

				if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
							    '''to use it with pretokenized inputs.'''	)

				return super()._encode_plus(*_lowerCamelCase       ,**_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							Tuple[str]:







				'''simple docstring'''





				__lowercase			  =							self._tokenizer.model.save(_lowerCamelCase       ,name=_lowerCamelCase	)
				return tuple(_lowerCamelCase	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							Tuple:







				'''simple docstring'''





				__lowercase			  =							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
							return output

				return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							List[int]:







				'''simple docstring'''





				__lowercase			  =							[self.sep_token_id]
				__lowercase			  =							[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]












'''simple docstring'''



import heapq




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

			# 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
			__lowercase			  =							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
						__lowercase			  =							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]:
												__lowercase			  =							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()

	_SCREAMING_SNAKE_CASE   =      {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)}''')





