



'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a__    :      Optional[int]      				=      logging.get_logger(__name__)

a__    :      Any      				=      {'vocab_file': 'sentencepiece.bpe.model'}

a__    :      List[Any]      				=      {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

a__    :      Tuple      				=      {
    'camembert-base': 5_1_2,
}

a__    :      Optional[int]      				=      '▁'
class 							UpperCAmelCase__	(     UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE          =					VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE          =					PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE          =					["input_ids", "attention_mask"]



 def __init__(	self		,						lowercase		,						lowercase="<s>"		,						lowercase="</s>"		,						lowercase="</s>"		,						lowercase="<s>"		,						lowercase="<unk>"		,						lowercase="<pad>"		,						lowercase="<mask>"		,						lowercase=["<s>NOTUSED", "</s>NOTUSED"]		,						lowercase = None		,						**lowercase		,						)	->				List[str]:
  __UpperCamelCase					   =	AddedToken(_snake_case		,						lstrip=_snake_case		,						rstrip=_snake_case      ) if isinstance(_snake_case		,						_snake_case      ) else mask_token

  __UpperCamelCase					   =	{} if sp_model_kwargs is None else sp_model_kwargs

  super().__init__(
      bos_token=_snake_case		,						eos_token=_snake_case		,						unk_token=_snake_case		,						sep_token=_snake_case		,						cls_token=_snake_case		,						pad_token=_snake_case		,						mask_token=_snake_case		,						additional_special_tokens=_snake_case		,						sp_model_kwargs=self.sp_model_kwargs		,						**_snake_case		,						)
  __UpperCamelCase					   =	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
  self.sp_model.Load(str(_snake_case      )      )
  __UpperCamelCase					   =	vocab_file
  # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
  # sentencepiece vocabulary (this is the case for <s> and </s>
  __UpperCamelCase					   =	{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
  __UpperCamelCase					   =	len(self.fairseq_tokens_to_ids      )
  __UpperCamelCase					   =	len(self.sp_model      ) + len(self.fairseq_tokens_to_ids      )
  __UpperCamelCase					   =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}



 def  __lowerCamelCase					(	self		,						lowercase		,						lowercase = None      )	->				List[Any]:


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



 def  __lowerCamelCase					(	self		,						lowercase		,						lowercase = None		,						lowercase = False      )	->				Any:


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

  if token_ids_a is None:
   return [1] + ([0] * len(_snake_case      )) + [1]
  return [1] + ([0] * len(_snake_case      )) + [1, 1] + ([0] * len(_snake_case      )) + [1]



 def  __lowerCamelCase					(	self		,						lowercase		,						lowercase = None      )	->				Optional[int]:
  __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 + sep + token_ids_a + sep      ) * [0]



 @property
 def  __lowerCamelCase					(	self      )	->				Union[str, Any]:
  return len(self.fairseq_tokens_to_ids      ) + len(self.sp_model      )



 def  __lowerCamelCase					(	self      )	->				Optional[Any]:
  __UpperCamelCase					   =	{self.convert_ids_to_tokens(_snake_case      ): i for i in range(self.vocab_size      )}
  vocab.update(self.added_tokens_encoder      )
  return vocab



 def  __lowerCamelCase					(	self		,						lowercase      )	->				Optional[int]:
  return self.sp_model.encode(_snake_case		,						out_type=_snake_case      )



 def  __lowerCamelCase					(	self		,						lowercase      )	->				int:


  if token in self.fairseq_tokens_to_ids:
   return self.fairseq_tokens_to_ids[token]
  elif self.sp_model.PieceToId(_snake_case      ) == 0:
   # Convert sentence piece unk token to fairseq unk token index
   return self.unk_token_id
  return self.fairseq_offset + self.sp_model.PieceToId(_snake_case      )



 def  __lowerCamelCase					(	self		,						lowercase      )	->				Dict:


  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  __lowerCamelCase					(	self		,						lowercase      )	->				Any:
  __UpperCamelCase					   =	[]
  __UpperCamelCase					   =	""""""
  __UpperCamelCase					   =	False
  for token in tokens:
   # make sure that special tokens are not decoded using sentencepiece model
   if token in self.all_special_tokens:
    if not prev_is_special:
     out_string += " "
    out_string += self.sp_model.decode(_snake_case      ) + token
    __UpperCamelCase					   =	True
    __UpperCamelCase					   =	[]
   else:
    current_sub_tokens.append(_snake_case      )
    __UpperCamelCase					   =	False
  out_string += self.sp_model.decode(_snake_case      )
  return out_string.strip()



 def __getstate__(	self      )	->				Any:
  __UpperCamelCase					   =	self.__dict__.copy()
  __UpperCamelCase					   =	None
  return state



 def __setstate__(	self		,						lowercase      )	->				List[str]:
  __UpperCamelCase					   =	d

  # for backward compatibility
  if not hasattr(self		,						"""sp_model_kwargs"""      ):
   __UpperCamelCase					   =	{}

  __UpperCamelCase					   =	spm.SentencePieceProcessor(**self.sp_model_kwargs      )
  self.sp_model.Load(self.vocab_file      )




 def  __lowerCamelCase					(	self		,						lowercase		,						lowercase = None      )	->				Optional[Any]:


  if not os.path.isdir(_snake_case      ):
   logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
   return
  __UpperCamelCase					   =	os.path.join(
      _snake_case		,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

  if os.path.abspath(self.vocab_file      ) != os.path.abspath(_snake_case      ) and os.path.isfile(self.vocab_file      ):
   copyfile(self.vocab_file		,						_snake_case      )
  elif not os.path.isfile(self.vocab_file      ):
   with open(_snake_case		,						"""wb"""      ) as fi:
    __UpperCamelCase					   =	self.sp_model.serialized_model_proto()
    fi.write(_snake_case      )

  return (out_vocab_file,)










import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
							# Create universe of discourse in Python using linspace ()
							SCREAMING_SNAKE_CASE__   			=   np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

							# Create two fuzzy sets by defining any membership function
							# (trapmf(), gbellmf(), gaussmf(), etc).
							SCREAMING_SNAKE_CASE__   			=   [0, 2_5, 5_0]
							SCREAMING_SNAKE_CASE__   			=   [2_5, 5_0, 7_5]
							SCREAMING_SNAKE_CASE__   			=   fuzz.membership.trimf(X, abca)
							SCREAMING_SNAKE_CASE__   			=   fuzz.membership.trimf(X, abca)

							# Compute the different operations using inbuilt functions.
							SCREAMING_SNAKE_CASE__   			=   np.ones(7_5)
							SCREAMING_SNAKE_CASE__   			=   np.zeros((7_5,))
							# 1. Union = max(µA(x), µB(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_or(X, young, X, middle_aged)[1]
							# 2. Intersection = min(µA(x), µB(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, young, X, middle_aged)[1]
							# 3. Complement (A) = (1- min(µA(x))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_not(young)
							# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
							# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
							SCREAMING_SNAKE_CASE__   			=   young + middle_aged - (young * middle_aged)
							# 6. Algebraic Product = (µA(x) * µB(x))
							SCREAMING_SNAKE_CASE__   			=   young * middle_aged
							# 7. Bounded Sum = min[1,(µA(x), µB(x))]
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
							# 8. Bounded difference = min[0,(µA(x), µB(x))]
							SCREAMING_SNAKE_CASE__   			=   fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

							# max-min composition
							# max-product composition

							# Plot each set A, set B and each operation result using plot() and subplot().
							from matplotlib import pyplot as plt

							plt.figure()

							plt.subplot(4, 3, 1)
							plt.plot(X, young)
							plt.title('''Young''')
							plt.grid(True)

							plt.subplot(4, 3, 2)
							plt.plot(X, middle_aged)
							plt.title('''Middle aged''')
							plt.grid(True)

							plt.subplot(4, 3, 3)
							plt.plot(X, union)
							plt.title('''union''')
							plt.grid(True)

							plt.subplot(4, 3, 4)
							plt.plot(X, intersection)
							plt.title('''intersection''')
							plt.grid(True)

							plt.subplot(4, 3, 5)
							plt.plot(X, complement_a)
							plt.title('''complement_a''')
							plt.grid(True)

							plt.subplot(4, 3, 6)
							plt.plot(X, difference)
							plt.title('''difference a/b''')
							plt.grid(True)

							plt.subplot(4, 3, 7)
							plt.plot(X, alg_sum)
							plt.title('''alg_sum''')
							plt.grid(True)

							plt.subplot(4, 3, 8)
							plt.plot(X, alg_product)
							plt.title('''alg_product''')
							plt.grid(True)

							plt.subplot(4, 3, 9)
							plt.plot(X, bdd_sum)
							plt.title('''bdd_sum''')
							plt.grid(True)

							plt.subplot(4, 3, 1_0)
							plt.plot(X, bdd_difference)
							plt.title('''bdd_difference''')
							plt.grid(True)

							plt.subplots_adjust(hspace=0.5)
							plt.show()




