



'''simple docstring'''





import os
import re
import unicodedata
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


lowercase_			=  logging.get_logger(__name__)
lowercase_			=  {"vocab_file": "spiece.model"}

lowercase_			=  {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
}

lowercase_			=  {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

lowercase_			=  "▁"






class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	Any										=	VOCAB_FILES_NAMES
 __lowerCamelCase   :	int										=	PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase   :	Optional[int]										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase   :	Tuple										=	['input_ids', 'token_type_ids']



 def __init__(self			,       A			,       A=False			,       A=True			,       A=True			,       A="<unk>"			,       A="[SEP]"			,       A="<pad>"			,       A="[CLS]"			,       A="[MASK]"			,       A = None			,       **A			,       )   ->					None:


     """simple docstring"""



     _a     =  (
         AddedToken(A			,       lstrip=A			,       rstrip=A			,       normalized=A	)
         if isinstance(A			,       A	)
         else mask_token
     )

     _a     =  {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=A			,       remove_space=A			,       keep_accents=A			,       unk_token=A			,       sep_token=A			,       pad_token=A			,       cls_token=A			,       mask_token=A			,       sp_model_kwargs=self.sp_model_kwargs			,       **A			,       )

     _a     =  do_lower_case
     _a     =  remove_space
     _a     =  keep_accents
     _a     =  vocab_file

     _a     =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
     self.sp_model.Load(A	)



 @property
 def  a__  (self	)   ->					List[str]:


     """simple docstring"""



     return len(self.sp_model	)



 def  a__  (self	)   ->					List[Any]:


     """simple docstring"""



     _a     =  {self.convert_ids_to_tokens(A	): i for i in range(self.vocab_size	)}
     vocab.update(self.added_tokens_encoder	)
     return vocab



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


     """simple docstring"""



     _a     =  self.__dict__.copy()
     _a     =  None
     return state



 def __setstate__(self			,       A	)   ->					Optional[int]:


     """simple docstring"""



     _a     =  d

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

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



 def  a__  (self			,       A	)   ->					Dict:


     """simple docstring"""



     if self.remove_space:
         _a     =  ''' '''.join(inputs.strip().split()	)
     else:
         _a     =  inputs
     _a     =  outputs.replace('''``'''			,       '''"'''	).replace('''\'\''''			,       '''"'''	)

     if not self.keep_accents:
         _a     =  unicodedata.normalize('''NFKD'''			,       A	)
         _a     =  ''''''.join([c for c in outputs if not unicodedata.combining(A	)]	)
     if self.do_lower_case:
         _a     =  outputs.lower()

     return outputs



 def  a__  (self			,       A	)   ->					List[str]:


     """simple docstring"""



     _a     =  self.preprocess_text(A	)
     _a     =  self.sp_model.encode(A			,       out_type=A	)
     _a     =  []
     for piece in pieces:
         if len(A	) > 1 and piece[-1] == str(''','''	) and piece[-2].isdigit():
             _a     =  self.sp_model.EncodeAsPieces(piece[:-1].replace(A			,       ''''''	)	)
             if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                 if len(cur_pieces[0]	) == 1:
                     _a     =  cur_pieces[1:]
                 else:
                     _a     =  cur_pieces[0][1:]
             cur_pieces.append(piece[-1]	)
             new_pieces.extend(A	)
         else:
             new_pieces.append(A	)

     return new_pieces



 def  a__  (self			,       A	)   ->					Dict:


     """simple docstring"""



     return self.sp_model.PieceToId(A	)



 def  a__  (self			,       A	)   ->					Union[str, Any]:


     """simple docstring"""



     return self.sp_model.IdToPiece(A	)



 def  a__  (self			,       A	)   ->					Any:


     """simple docstring"""



     _a     =  []
     _a     =  ''''''
     _a     =  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(A	) + token
             _a     =  True
             _a     =  []
         else:
             current_sub_tokens.append(A	)
             _a     =  False
     out_string += self.sp_model.decode(A	)
     return out_string.strip()



 def  a__  (self			,       A			,       A = False			,       A = None			,       A = True			,       **A			,       )   ->					str:


     """simple docstring"""



     _a     =  kwargs.pop('''use_source_tokenizer'''			,       A	)

     _a     =  self.convert_ids_to_tokens(A			,       skip_special_tokens=A	)

     # To avoid mixing byte-level and unicode for byte-level BPT
     # we need to build string separately for added tokens and byte-level tokens
     # cf. https://github.com/huggingface/transformers/issues/1133
     _a     =  []
     _a     =  []
     for token in filtered_tokens:
         if skip_special_tokens and token in self.all_special_ids:
             continue
         if token in self.added_tokens_encoder:
             if current_sub_text:
                 sub_texts.append(self.convert_tokens_to_string(A	)	)
                 _a     =  []
             sub_texts.append(A	)
         else:
             current_sub_text.append(A	)
     if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(A	)	)

     # Mimic the behavior of the Rust tokenizer:
     # No space after <unk>
     if spaces_between_special_tokens:
         _a     =  re.sub(R'''(<unk>) '''			,       R'''\1'''			,       ''' '''.join(A	)	)
     else:
         _a     =  ''''''.join(A	)

     _a     =  (
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
         _a     =  self.clean_up_tokenization(A	)
         return clean_text
     else:
         return text



 def  a__  (self			,       A			,       A = None	)   ->					List[int]:


     """simple docstring"""



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



 def  a__  (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 not None:
         return [1] + ([0] * len(A	)) + [1] + ([0] * len(A	)) + [1]
     return [1] + ([0] * len(A	)) + [1]



 def  a__  (self			,       A			,       A = None	)   ->					List[int]:


     """simple docstring"""



     _a     =  [self.sep_token_id]
     _a     =  [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	) * [0] + len(token_ids_a + sep	) * [1]



 def  a__  (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
     _a     =  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:
             _a     =  self.sp_model.serialized_model_proto()
             fi.write(A	)

     return (out_vocab_file,)








'''simple docstring'''





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


lowercase_			=  importlib.metadata.version("numpy")
lowercase_			=  ".".join([str(x) for x in sys.version_info[:3]])






class __A      (  A						):



 '''simple docstring'''







 def  a__  (self	)   ->					Any:


      """simple docstring"""



      require_version_core('''numpy<1000.4.5'''	)
      require_version_core('''numpy<1000.4'''	)
      require_version_core('''numpy<1000'''	)

      # le
      require_version_core('''numpy<=1000.4.5'''	)
      require_version_core(f'''numpy<={numpy_ver}'''	)

      # eq
      require_version_core(f'''numpy=={numpy_ver}'''	)

      # ne
      require_version_core('''numpy!=1000.4.5'''	)

      # ge
      require_version_core('''numpy>=1.0'''	)
      require_version_core('''numpy>=1.0.0'''	)
      require_version_core(f'''numpy>={numpy_ver}'''	)

      # gt
      require_version_core('''numpy>1.0.0'''	)

      # mix
      require_version_core('''numpy>1.0.0,<1000'''	)

      # requirement w/o version
      require_version_core('''numpy'''	)

      # unmet requirements due to version conflict
      for req in ["numpy==1.0.0", "numpy>=1000.0.0", f'''numpy<{numpy_ver}''']:
           try:
                require_version_core(A	)
           except ImportError as e:
                self.assertIn(f'''{req} is required'''			,       str(A	)	)
                self.assertIn('''but found'''			,       str(A	)	)

        # unmet requirements due to missing module
      for req in ["numpipypie>1", "numpipypie2"]:
           try:
                require_version_core(A	)
           except importlib.metadata.PackageNotFoundError as e:
                self.assertIn(f'''The \'{req}\' distribution was not found and is required by this application'''			,       str(A	)	)
                self.assertIn('''Try: pip install transformers -U'''			,       str(A	)	)

        # bogus requirements formats:
        # 1. whole thing
      for req in ["numpy??1.0.0", "numpy1.0.0"]:
           try:
                require_version_core(A	)
           except ValueError as e:
                self.assertIn('''requirement needs to be in the pip package format'''			,       str(A	)	)
        # 2. only operators
      for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
           try:
                require_version_core(A	)
           except ValueError as e:
                self.assertIn('''need one of '''			,       str(A	)	)



 def  a__  (self	)   ->					int:


      """simple docstring"""



      require_version('''python>=3.6.0'''	)

      # not matching requirements
      for req in ["python>9.9.9", "python<3.0.0"]:
           try:
                require_version_core(A	)
           except ImportError as e:
                self.assertIn(f'''{req} is required'''			,       str(A	)	)
                self.assertIn(f'''but found python=={python_ver}'''			,       str(A	)	)



