



'''simple docstring'''

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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_xlnet import XLNetTokenizer
else:
       __lowerCAmelCase						      =       None


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCAmelCase						      =       {
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json''',
    },
}

__lowerCAmelCase						      =       {
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

__lowerCAmelCase						      =       '''▁'''

# Segments (not really needed)
__lowerCAmelCase						      =       0
__lowerCAmelCase						      =       1
__lowerCAmelCase						      =       2
__lowerCAmelCase						      =       3
__lowerCAmelCase						      =       4

class      __magic_name__						(   __lowercase       ):
       lowerCAmelCase	:   List[str]           = VOCAB_FILES_NAMES
       lowerCAmelCase	:   Union[str, Any]           = PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase	:   List[str]           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase	:   Any           = 'left'
       lowerCAmelCase	:   Tuple           = XLNetTokenizer







       def __init__(							self :	List[Any]     ,_UpperCAmelCase :	List[Any]=None     ,_UpperCAmelCase :	Tuple=None     ,_UpperCAmelCase :	str=False     ,_UpperCAmelCase :	Dict=True     ,_UpperCAmelCase :	int=False     ,_UpperCAmelCase :	int="<s>"     ,_UpperCAmelCase :	int="</s>"     ,_UpperCAmelCase :	List[str]="<unk>"     ,_UpperCAmelCase :	Optional[int]="<sep>"     ,_UpperCAmelCase :	int="<pad>"     ,_UpperCAmelCase :	Optional[Any]="<cls>"     ,_UpperCAmelCase :	Any="<mask>"     ,_UpperCAmelCase :	Optional[Any]=["<eop>", "<eod>"]     ,**_UpperCAmelCase :	str     ,):
         # Mask token behave like a normal word, i.e. include the space before it
         _a					:				Optional[Any]				=							AddedToken(_a     ,lstrip=_a     ,rstrip=_a		) if isinstance(_a     ,_a		) else mask_token

         super().__init__(
             vocab_file=_a     ,tokenizer_file=_a     ,do_lower_case=_a     ,remove_space=_a     ,keep_accents=_a     ,bos_token=_a     ,eos_token=_a     ,unk_token=_a     ,sep_token=_a     ,pad_token=_a     ,cls_token=_a     ,mask_token=_a     ,additional_special_tokens=_a     ,**_a     ,)

         _a					:				Optional[int]				=							3
         _a					:				Union[str, Any]				=							do_lower_case
         _a					:				List[str]				=							remove_space
         _a					:				List[Any]				=							keep_accents
         _a					:				List[str]				=							vocab_file
         _a					:				List[Any]				=							False if not self.vocab_file else True







       def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	str     ,_UpperCAmelCase :	str = None		):
         _a					:				Optional[int]				=							[self.sep_token_id]
         _a					:				Union[str, Any]				=							[self.cls_token_id]
         if token_ids_a is None:
           return token_ids_a + sep + cls
         return token_ids_a + sep + token_ids_a + sep + cls







       def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	Dict = None		):
         _a					:				List[str]				=							[self.sep_token_id]
         _a					:				List[str]				=							[2]

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







       def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	str = None		):
         if not self.can_save_slow_tokenizer:
           raise ValueError(
               'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
               'tokenizer.'		)

         if not os.path.isdir(_a		):
           logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
           return
         _a					:				str				=							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		):
           copyfile(self.vocab_file     ,_a		)

         return (out_vocab_file,)









'''simple docstring'''

import heapq



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				set[int]:
		_a					:				list[list]				=							[]

		# 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
		_a					:				str				=							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
				_a					:				Optional[int]				=							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]:
								_a					:				Dict				=							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)}""")




