



'''simple docstring'''

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       "▁"

__lowerCAmelCase						      =       {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

__lowerCAmelCase						      =       {
    "vocab_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"
        ),
    },
    "spm_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"
        )
    },
}

__lowerCAmelCase						      =       {
    "facebook/s2t-small-librispeech-asr": 1_024,
}

__lowerCAmelCase						      =       ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

__lowerCAmelCase						      =       {"mustc": MUSTC_LANGS}

class      __magic_name__						(   a_       ):
       lowerCAmelCase	:   Union[str, Any]           = VOCAB_FILES_NAMES
       lowerCAmelCase	:   int           = PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase	:   List[str]           = MAX_MODEL_INPUT_SIZES
       lowerCAmelCase	:   Dict           = ["input_ids", "attention_mask"]

       lowerCAmelCase	:   List[int]           = []







       def __init__(							self :	List[Any]     ,_UpperCAmelCase :	Tuple     ,_UpperCAmelCase :	int     ,_UpperCAmelCase :	Optional[int]="<s>"     ,_UpperCAmelCase :	Dict="</s>"     ,_UpperCAmelCase :	str="<pad>"     ,_UpperCAmelCase :	List[Any]="<unk>"     ,_UpperCAmelCase :	str=False     ,_UpperCAmelCase :	List[Any]=False     ,_UpperCAmelCase :	Optional[int]=None     ,_UpperCAmelCase :	str=None     ,_UpperCAmelCase :	int = None     ,**_UpperCAmelCase :	List[str]     ,):
         _a					:				Union[str, Any]				=							{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=_UpperCAmelCase     ,eos_token=_UpperCAmelCase     ,unk_token=_UpperCAmelCase     ,pad_token=_UpperCAmelCase     ,do_upper_case=_UpperCAmelCase     ,do_lower_case=_UpperCAmelCase     ,tgt_lang=_UpperCAmelCase     ,lang_codes=_UpperCAmelCase     ,sp_model_kwargs=self.sp_model_kwargs     ,**_UpperCAmelCase     ,)
         _a					:				str				=							do_upper_case
         _a					:				Tuple				=							do_lower_case

         _a					:				Dict				=							load_json(_UpperCAmelCase		)
         _a					:				str				=							{v: k for k, v in self.encoder.items()}
         _a					:				int				=							spm_file
         _a					:				str				=							load_spm(_UpperCAmelCase     ,self.sp_model_kwargs		)

         if lang_codes is not None:
           _a					:				Any				=							lang_codes
           _a					:				List[Any]				=							LANGUAGES[lang_codes]
           _a					:				Any				=							[F"""<lang:{lang}>""" for lang in self.langs]
           _a					:				str				=							{lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""		) for lang in self.langs}

           _a					:				Optional[Any]				=							self.lang_tokens
           _a					:				str				=							tgt_lang if tgt_lang is not None else self.langs[0]

           self.set_tgt_lang_special_tokens(self._tgt_lang		)
         else:
           _a					:				str				=							{}







       @property
       def 							__lowercase      (							self :	Dict		):
         return len(self.encoder		)







       @property
       def 							__lowercase      (							self :	List[Any]		):
         return self._tgt_lang







       @tgt_lang.setter
       def 							__lowercase      (							self :	Any     ,_UpperCAmelCase :	List[Any]		):
         _a					:				Tuple				=							new_tgt_lang
         self.set_tgt_lang_special_tokens(_UpperCAmelCase		)







       def 							__lowercase      (							self :	int     ,_UpperCAmelCase :	Dict		):
         _a					:				List[Any]				=							self.lang_code_to_id[tgt_lang]
         _a					:				List[Any]				=							[lang_code_id]







       def 							__lowercase      (							self :	List[str]     ,_UpperCAmelCase :	str		):
         return self.sp_model.encode(_UpperCAmelCase     ,out_type=_UpperCAmelCase		)







       def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Dict		):
         return self.encoder.get(_UpperCAmelCase     ,self.encoder[self.unk_token]		)







       def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	Dict		):
         return self.decoder.get(_UpperCAmelCase     ,self.unk_token		)







       def 							__lowercase      (							self :	Union[str, Any]     ,_UpperCAmelCase :	Any		):
         _a					:				Optional[int]				=							[]
         _a					:				int				=							''
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             _a					:				Optional[int]				=							self.sp_model.decode(_UpperCAmelCase		)
             out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
             _a					:				Any				=							[]
           else:
             current_sub_tokens.append(_UpperCAmelCase		)
         _a					:				List[str]				=							self.sp_model.decode(_UpperCAmelCase		)
         out_string += decoded.upper() if self.do_upper_case else decoded
         return out_string.strip()







       def 							__lowercase      (							self :	Optional[Any]     ,_UpperCAmelCase :	Dict     ,_UpperCAmelCase :	Tuple=None		):







         if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]







       def 							__lowercase      (							self :	str     ,_UpperCAmelCase :	Union[str, Any]     ,_UpperCAmelCase :	List[str] = None     ,_UpperCAmelCase :	int = False		):







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

         _a					:				str				=							[1] * len(self.prefix_tokens		)
         _a					:				Union[str, Any]				=							[1]
         if token_ids_a is None:
           return prefix_ones + ([0] * len(_UpperCAmelCase		)) + suffix_ones
         return prefix_ones + ([0] * len(_UpperCAmelCase		)) + ([0] * len(_UpperCAmelCase		)) + suffix_ones







       def 							__lowercase      (							self :	Union[str, Any]		):
         _a					:				Optional[Any]				=							self.encoder.copy()
         vocab.update(self.added_tokens_encoder		)
         return vocab







       def __getstate__(							self :	str		):
         _a					:				Optional[Any]				=							self.__dict__.copy()
         _a					:				Tuple				=							None
         return state







       def __setstate__(							self :	Union[str, Any]     ,_UpperCAmelCase :	Tuple		):
         _a					:				Optional[int]				=							d

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

         _a					:				Optional[Any]				=							load_spm(self.spm_file     ,self.sp_model_kwargs		)







       def 							__lowercase      (							self :	List[Any]     ,_UpperCAmelCase :	Optional[Any]     ,_UpperCAmelCase :	List[str] = None		):
         _a					:				int				=							Path(_UpperCAmelCase		)
         assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
         _a					:				str				=							save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
         )
         _a					:				Optional[Any]				=							save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
         )

         save_json(self.encoder     ,_UpperCAmelCase		)

         if os.path.abspath(self.spm_file		) != os.path.abspath(_UpperCAmelCase		) and os.path.isfile(self.spm_file		):
           copyfile(self.spm_file     ,_UpperCAmelCase		)
         elif not os.path.isfile(self.spm_file		):
           with open(_UpperCAmelCase     ,'wb'		) as fi:
             _a					:				str				=							self.sp_model.serialized_model_proto()
             fi.write(_UpperCAmelCase		)

         return (str(_UpperCAmelCase		), str(_UpperCAmelCase		))



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				Any:
  _a					:				Optional[int]				=							sentencepiece.SentencePieceProcessor(**lowerCamelCase__  )
  spm.Load(str(lowerCamelCase__  )  )
  return spm



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				int:




  with open(lowerCamelCase__						,      'r'  ) as f:
    return json.load(lowerCamelCase__  )



def 					__lowerCamelCase					(				lowerCAmelCase_						,      lowerCAmelCase_  )      ->				List[Any]:




  with open(lowerCamelCase__						,      'w'  ) as f:
    json.dump(lowerCamelCase__						,      lowerCamelCase__						,      indent=2  )









'''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)}""")




