


'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


_lowercase    :    str			     =  logging.get_logger(__name__)

_lowercase    :    List[str]			     =  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_lowercase    :    str			     =  {
    """vocab_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
        ),
        """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """squeezebert/squeezebert-uncased""": (
            """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
        ),
        """squeezebert/squeezebert-mnli-headless""": (
            """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
        ),
    },
}

_lowercase    :    Dict			     =  {
    """squeezebert/squeezebert-uncased""": 512,
    """squeezebert/squeezebert-mnli""": 512,
    """squeezebert/squeezebert-mnli-headless""": 512,
}


_lowercase    :    Tuple			     =  {
    """squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
    """squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Optional[Any]							= VOCAB_FILES_NAMES
				__magic_name__       :   Optional[Any]							= PRETRAINED_VOCAB_FILES_MAP
				__magic_name__       :   Optional[int]							= PRETRAINED_INIT_CONFIGURATION
				__magic_name__       :   Dict							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__       :   Optional[Any]							= SqueezeBertTokenizer




				def __init__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				List[str]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[int]=True   ,					lowerCAmelCase    :				str="[UNK]"   ,					lowerCAmelCase    :				Optional[int]="[SEP]"   ,					lowerCAmelCase    :				int="[PAD]"   ,					lowerCAmelCase    :				Union[str, Any]="[CLS]"   ,					lowerCAmelCase    :				Union[str, Any]="[MASK]"   ,					lowerCAmelCase    :				Tuple=True   ,					lowerCAmelCase    :				Tuple=None   ,					**lowerCAmelCase    :				Union[str, Any]   ,					)->  List[Any]:

										"""simple docstring"""




										super().__init__(
										    lowerCAmelCase   ,					tokenizer_file=lowerCAmelCase   ,					do_lower_case=lowerCAmelCase   ,					unk_token=lowerCAmelCase   ,					sep_token=lowerCAmelCase   ,					pad_token=lowerCAmelCase   ,					cls_token=lowerCAmelCase   ,					mask_token=lowerCAmelCase   ,					tokenize_chinese_chars=lowerCAmelCase   ,					strip_accents=lowerCAmelCase   ,					**lowerCAmelCase   ,					)

										UpperCAmelCase         =			json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
										if (
										    normalizer_state.get('''lowercase'''   ,					lowerCAmelCase   ) != do_lower_case
										    or normalizer_state.get('''strip_accents'''   ,					lowerCAmelCase   ) != strip_accents
										    or normalizer_state.get('''handle_chinese_chars'''   ,					lowerCAmelCase   ) != tokenize_chinese_chars
										):
																UpperCAmelCase         =			getattr(lowerCAmelCase   ,					normalizer_state.pop('''type'''   )   )
																UpperCAmelCase         =			do_lower_case
																UpperCAmelCase         =			strip_accents
																UpperCAmelCase         =			tokenize_chinese_chars
																UpperCAmelCase         =			normalizer_class(**lowerCAmelCase   )

										UpperCAmelCase         =			do_lower_case




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

										"""simple docstring"""




										UpperCAmelCase         =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

										if token_ids_a:
																output += token_ids_a + [self.sep_token_id]

										return output




				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   ) * [0] + len(token_ids_a + sep   ) * [1]





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

										"""simple docstring"""




										UpperCAmelCase         =			self._tokenizer.model.save(lowerCAmelCase   ,					name=lowerCAmelCase   )
										return tuple(lowerCAmelCase   )









'''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)}""")





