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

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


snake_case__				= logging.get_logger(__name__)

snake_case__				= '▁'

snake_case__				= {'vocab_file': 'prophetnet.tokenizer'}

snake_case__				= {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

snake_case__				= {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

snake_case__				= {
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}
def       __magic_name__(  __UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      _lowerCamelCase			    =				collections.OrderedDict()
      with open(__UpperCAmelCase ,						'''r''' ,						encoding='''utf-8'''		) as reader:
            _lowerCamelCase			    =				reader.readlines()
      for index, token in enumerate(__UpperCAmelCase		):
            _lowerCamelCase			    =				token.rstrip('''\n'''		)
            _lowerCamelCase			    =				index
      return vocab




class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  VOCAB_FILES_NAMES
     A_										=  PRETRAINED_VOCAB_FILES_MAP
     A_										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_										=  ['input_ids', 'attention_mask']



     def __init__(					self				,				A_				,				A_="[SEP]"				,				A_="[SEP]"				,				A_="[SEP]"				,				A_="[UNK]"				,				A_="[PAD]"				,				A_="[CLS]"				,				A_="[MASK]"				,				A_ = None				,				**A_				,				) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				{} if sp_model_kwargs is None else sp_model_kwargs

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

           try:
                 import sentencepiece as spm
           except ImportError:
                 logger.warning(
                     '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                     ''' pip install sentencepiece'''						)
                 raise

           _lowerCamelCase			    =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
           self.sp_model.Load(str(A_						)						)
           _lowerCamelCase			    =				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'

           # put special tokens and [unused] tokens into the vocab
           _lowerCamelCase			    =				{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

           for i in range(10						):
                 _lowerCamelCase			    =				F'[unused{i}]'
                 _lowerCamelCase			    =				5 + i

           # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
           _lowerCamelCase			    =				12
           _lowerCamelCase			    =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
           for k in self.fairseq_tokens_to_ids.keys():
                 self.unique_no_split_tokens.append(A_						)



     def __getstate__(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				self.__dict__.copy()
           _lowerCamelCase			    =				None
           return state



     def __setstate__(					self				,				A_						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				d
           try:
                 import sentencepiece as spm
           except ImportError:
                 logger.warning(
                     '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                     ''' pip install sentencepiece'''						)
                 raise

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

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



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None				,				A_ = False						) ->				List[int]:






           """simple docstring"""

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

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



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				List[int]:






           """simple docstring"""
           _lowerCamelCase			    =				[self.sep_token_id]

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



     @property
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           return len(self.sp_model						) + self.fairseq_offset



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				{self.convert_ids_to_tokens(A_						): i for i in range(self.vocab_size						)}
           vocab.update(self.added_tokens_encoder						)
           return vocab



     def 		UpperCamelCase_						(					self				,				A_						) ->				str:






           """simple docstring"""
           return self.sp_model.encode(A_				,				out_type=A_						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[Any]:






           """simple docstring"""
           if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
           _lowerCamelCase			    =				self.sp_model.PieceToId(A_						)

           # 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 		UpperCamelCase_						(					self				,				A_						) ->				Tuple:






           """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 		UpperCamelCase_						(					self				,				A_						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				''''''.join(A_						).replace(A_				,				''' '''						).strip()
           return out_string



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				Tuple[str]:






           """simple docstring"""
           if not os.path.isdir(A_						):
                 logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
                 return
           _lowerCamelCase			    =				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_						) and os.path.isfile(self.vocab_file						):
                 copyfile(self.vocab_file				,				A_						)
           elif not os.path.isfile(self.vocab_file						):
                 with open(A_				,				'''wb'''						) as fi:
                       _lowerCamelCase			    =				self.sp_model.serialized_model_proto()
                       fi.write(A_						)

           return (out_vocab_file,)





     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				List[int]:






           """simple docstring"""

           if token_ids_a is None:
                 return token_ids_a + [self.sep_token_id]
           _lowerCamelCase			    =				[self.sep_token_id]
           return token_ids_a + sep + token_ids_a + sep
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     snake_case__				= np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

     # Create two fuzzy sets by defining any membership function
     # (trapmf(), gbellmf(), gaussmf(), etc).
     snake_case__				= [0, 25, 50]
     snake_case__				= [25, 50, 75]
     snake_case__				= fuzz.membership.trimf(X, abca)
     snake_case__				= fuzz.membership.trimf(X, abca)

     # Compute the different operations using inbuilt functions.
     snake_case__				= np.ones(75)
     snake_case__				= np.zeros((75,))
     # 1. Union = max(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_or(X, young, X, middle_aged)[1]
     # 2. Intersection = min(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_and(X, young, X, middle_aged)[1]
     # 3. Complement (A) = (1- min(µA(x))
     snake_case__				= fuzz.fuzzy_not(young)
     # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
     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))]
     snake_case__				= young + middle_aged - (young * middle_aged)
     # 6. Algebraic Product = (µA(x) * µB(x))
     snake_case__				= young * middle_aged
     # 7. Bounded Sum = min[1,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
     # 8. Bounded difference = min[0,(µA(x), µB(x))]
     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, 10)
     plt.plot(X, bdd_difference)
     plt.title('bdd_difference')
     plt.grid(True)

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