


'''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_camembert import CamembertTokenizer
else:
 UpperCAmelCase_ :     List[Any]								=      None


UpperCAmelCase_ :     Dict								=      logging.get_logger(__name__)

UpperCAmelCase_ :     str								=      {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_ :     str								=      {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase_ :     Optional[Any]								=      {
    'camembert-base': 5_1_2,
}

UpperCAmelCase_ :     Any								=      '▁'

class 						_lowerCamelCase					(    lowerCAmelCase__				):




 '''simple docstring'''
 __lowercase				:		str    							=						VOCAB_FILES_NAMES
 __lowercase				:		List[Any]    							=						PRETRAINED_VOCAB_FILES_MAP
 __lowercase				:		Union[str, Any]    							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase				:		int    							=						["input_ids", "attention_mask"]
 __lowercase				:		Dict    							=						CamembertTokenizer


 def __init__(      self      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase="<s>"      ,      __lowercase="</s>"      ,      __lowercase="</s>"      ,      __lowercase="<s>"      ,      __lowercase="<unk>"      ,      __lowercase="<pad>"      ,      __lowercase="<mask>"      ,      __lowercase=["<s>NOTUSED", "</s>NOTUSED"]      ,      **__lowercase      ,      ):

  """simple docstring"""







  __A  :		Union[str, Any]       =			AddedToken(_lowerCamelCase      ,      lstrip=_lowerCamelCase      ,      rstrip=_lowerCamelCase						) if isinstance(_lowerCamelCase      ,      _lowerCamelCase						) else mask_token

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

  __A  :		List[str]       =			vocab_file
  __A  :		List[Any]       =			False if not self.vocab_file else True


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  __A  :		Dict       =			[self.cls_token_id]
  __A  :		Any       =			[self.sep_token_id]
  return cls + token_ids_a + sep + sep + token_ids_a + sep


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			[self.sep_token_id]
  __A  :		Optional[int]       =			[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]







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  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(_lowerCamelCase						):
   logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
   return
  __A  :		int       =			os.path.join(
      _lowerCamelCase      ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

  if os.path.abspath(self.vocab_file						) != os.path.abspath(_lowerCamelCase						):
   copyfile(self.vocab_file      ,      _lowerCamelCase						)

  return (out_vocab_file,)




'''simple docstring'''
from heapq import heappop, heappush

import numpy as np


def 			_lowercase       (				UpperCamelCase__       :					np.ndarray,				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					bool,				):
 __A     ,__A  :		Optional[Any]       =			grid.shape
 __A  :		List[Any]       =			[-1, 1, 0, 0]
 __A  :		Optional[int]       =			[0, 0, -1, 1]
 if allow_diagonal:
  dx += [-1, -1, 1, 1]
  dy += [-1, 1, -1, 1]

 __A     ,__A  :		int       =			[(0, source)], set()
 __A  :		Any       =			np.full((rows, cols),				np.inf			)
 __A  :		int       =			0
 __A  :		Any       =			np.empty((rows, cols),				dtype=UpperCamelCase__			)
 __A  :		List[Any]       =			None

 while queue:
  ((__A)     ,(__A))  :		List[Any]       =			heappop(UpperCamelCase__			)
  if (x, y) in visited:
   continue
  visited.add((x, y)			)

  if (x, y) == destination:
   __A  :		Tuple       =			[]
   while (x, y) != source:
    path.append((x, y)			)
    __A     ,__A  :		int       =			predecessors[x, y]
   path.append(UpperCamelCase__			)  # add the source manually
   path.reverse()
   return matrix[destination], path

  for i in range(len(UpperCamelCase__			)			):
   __A     ,__A  :		List[Any]       =			x + dx[i], y + dy[i]
   if 0 <= nx < rows and 0 <= ny < cols:
    __A  :		str       =			grid[nx][ny]
    if next_node == 1 and matrix[nx, ny] > dist + 1:
     heappush(UpperCamelCase__,				(dist + 1, (nx, ny))			)
     __A  :		int       =			dist + 1
     __A  :		List[str]       =			(x, y)

 return np.inf, []


if __name__ == "__main__":
 import doctest

 doctest.testmod()






