






"""simple docstring"""






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

import sentencepiece as spm

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


UpperCAmelCase_   : Dict   			= logging.get_logger(__name__)

UpperCAmelCase_   : Tuple   			= """▁"""

UpperCAmelCase_   : Optional[Any]   			= {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase_   : str   			= {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCAmelCase_   : str   			= {
    """facebook/xglm-564M""": 2048,
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  VOCAB_FILES_NAMES
      __UpperCamelCase	 =  PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase	 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase	 =  ["input_ids", "attention_mask"]
      def __init__(						self     :  List[Any]						,	lowercase_     :  str						,	lowercase_     :  Tuple="<s>"						,	lowercase_     :  Any="</s>"						,	lowercase_     :  Optional[int]="</s>"						,	lowercase_     :  List[Any]="<s>"						,	lowercase_     :  Union[str, Any]="<unk>"						,	lowercase_     :  Union[str, Any]="<pad>"						,	lowercase_     :  Optional[Dict[str, Any]] = None						,	**lowercase_     :  Tuple						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{} if sp_model_kwargs is None else sp_model_kwargs

          # Compatibility with the original tokenizer
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		7
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		[F'<madeupword{i}>' for i in range(self.num_madeup_words)]

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		kwargs.get('''additional_special_tokens'''						,	[])
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

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

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(str(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		1

          # Mimic fairseq token-to-id alignment for the first 4 token
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		len(self.sp_model)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
          self.fairseq_tokens_to_ids.update(lowercase_)

          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
      def __getstate__(						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.__dict__.copy()
          SCREAMING_SNAKE_CASE_      :  str    						=		None
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.sp_model.serialized_model_proto()
          return state
      def __setstate__(						self     :  Tuple						,	lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		d

          # for backward compatibility
          if not hasattr(self						,	'''sp_model_kwargs'''):
              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		{}

          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None):




          '''simple docstring'''


          if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
          SCREAMING_SNAKE_CASE_      :  Dict    						=		[self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None						,	lowercase_     :  bool = False):




          '''simple docstring'''


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

          if token_ids_a is None:
              return [1] + ([0] * len(lowercase_))
          return [1] + ([0] * len(lowercase_)) + [1, 1] + ([0] * len(lowercase_))
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  List[int]						,	lowercase_     :  Optional[List[int]] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		[self.sep_token_id]

          if token_ids_a is None:
              return len(sep + token_ids_a) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		{self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab
      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  str):




          '''simple docstring'''


          return self.sp_model.encode(lowercase_						,	out_type=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.sp_model.PieceToId(lowercase_)

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Optional[Any]):




          '''simple docstring'''


          if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset)
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		''''''.join(lowercase_).replace(lowercase_						,	''' ''').strip()
          return out_string



      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  Optional[str] = None):




          '''simple docstring'''


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

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

          return (out_vocab_file,)




def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= [0] * len(a_						)
				__A   			= []
				__A   			= [1] * len(a_						)

				for values in graph.values():
								for i in values:
												indegree[i] += 1

				for i in range(len(a_						)						):
								if indegree[i] == 0:
												queue.append(a_						)

				while queue:
								__A   			= queue.pop(0						)
								for x in graph[vertex]:
												indegree[x] -= 1

												if long_dist[vertex] + 1 > long_dist[x]:
																__A   			= long_dist[vertex] + 1

												if indegree[x] == 0:
																queue.append(a_						)

				print(max(a_						)						)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE							:List[Any]							 =				{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
