'''simple docstring'''

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_ta import TaTokenizer
else:
 A_     :						Optional[int]    							=None


A_     :						str    							=logging.get_logger(__name__)

A_     :						Dict    							={'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

A_     :						int    							={
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
A_     :						Optional[Any]    							={
    '''t5-small''': 5_12,
    '''t5-base''': 5_12,
    '''t5-large''': 5_12,
    '''t5-3b''': 5_12,
    '''t5-11b''': 5_12,
}


class     __UpperCAmelCase       ( __a		):
  __A :				int											= VOCAB_FILES_NAMES
  __A :				str											= PRETRAINED_VOCAB_FILES_MAP
  __A :				List[str]											= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __A :				List[str]											= ['input_ids', 'attention_mask']
  __A :				int											= TaTokenizer

  __A :				List[int]											= []





  def __init__(	self    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase="</s>"    ,     _lowerCamelCase="<unk>"    ,     _lowerCamelCase="<pad>"    ,     _lowerCamelCase=100    ,     _lowerCamelCase=None    ,     **_lowerCamelCase    ,     ):
     # Add extra_ids to the special token list
     if extra_ids > 0 and additional_special_tokens is None:
        lowerCAmelCase_			=      [F'''<extra_id_{i}>''' for i in range(_lowerCamelCase )]
     elif extra_ids > 0 and additional_special_tokens is not None:
        # Check that we have the right number of extra special tokens
        lowerCAmelCase_			=      len(set(filter(lambda _lowerCamelCase : bool('''extra_id_''' in str(_lowerCamelCase ) )    ,     _lowerCamelCase ) ) )
        if extra_tokens != extra_ids:
           raise ValueError(
               F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
               ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
               ''' tokens''' )

     super().__init__(
         _lowerCamelCase    ,     tokenizer_file=_lowerCamelCase    ,     eos_token=_lowerCamelCase    ,     unk_token=_lowerCamelCase    ,     pad_token=_lowerCamelCase    ,     extra_ids=_lowerCamelCase    ,     additional_special_tokens=_lowerCamelCase    ,     **_lowerCamelCase    ,     )

     lowerCAmelCase_			=      vocab_file
     lowerCAmelCase_			=      False if not self.vocab_file else True
     lowerCAmelCase_			=      extra_ids





  @staticmethod
  def 						UpperCAmelCase_	(	_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
        lowerCAmelCase_			=      TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
        if init_max_model_length is not None and init_max_model_length != max_model_length:
           return init_max_model_length
        elif init_max_model_length is None:
           warnings.warn(
               '''This tokenizer was incorrectly instantiated with a model max length of'''
               F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
               ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
               ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
               F''' {pretrained_model_name_or_path} automatically truncating your input to'''
               F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
               F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
               ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
               ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''    ,     _lowerCamelCase    ,     )

     return max_model_length





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     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
     lowerCAmelCase_			=      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 )
        logger.info(F'''Copy vocab file to {out_vocab_file}''' )

     return (out_vocab_file,)





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
        return self.prefix_tokens + token_ids_a
     else:
        lowerCAmelCase_			=      token_ids_a + [self.eos_token_id]
        return self.prefix_tokens + token_ids_a + token_ids_a





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      [self.eos_token_id]

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





  def 						UpperCAmelCase_	(	self ):
     return list(
         set(filter(lambda _lowerCamelCase : bool(re.search(R'''<extra_id_\d+>'''    ,     _lowerCamelCase ) ) is not None    ,     self.additional_special_tokens ) ) )







  def 						UpperCAmelCase_	(	self ):
     return [self.convert_tokens_to_ids(_lowerCamelCase ) for token in self.get_sentinel_tokens()]






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A_     :						Optional[Any]    							={
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Dict    							=['''SqueezeBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Optional[int]    							=[
     '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SqueezeBertForMaskedLM''',
     '''SqueezeBertForMultipleChoice''',
     '''SqueezeBertForQuestionAnswering''',
     '''SqueezeBertForSequenceClassification''',
     '''SqueezeBertForTokenClassification''',
     '''SqueezeBertModel''',
     '''SqueezeBertModule''',
     '''SqueezeBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 A_     :						List[str]    							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





