'''simple docstring'''

import copy
import random

from transformers import CLIPTokenizer


class     __UpperCAmelCase       ( __a		):





  def __init__(	self    ,     *_lowerCamelCase    ,     **_lowerCamelCase ):
     super().__init__(*_lowerCamelCase    ,     **_lowerCamelCase )
     lowerCAmelCase_			=      {}





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     *_lowerCamelCase    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      super().add_tokens(_lowerCamelCase    ,     *_lowerCamelCase    ,     **_lowerCamelCase )
     if num_added_tokens == 0:
        raise ValueError(
            F'''The tokenizer already contains the token {placeholder_token}. Please pass a different'''
            ''' `placeholder_token` that is not already in the tokenizer.''' )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     *_lowerCamelCase    ,     _lowerCamelCase=1    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      []
     if num_vec_per_token == 1:
        self.try_adding_tokens(_lowerCamelCase    ,     *_lowerCamelCase    ,     **_lowerCamelCase )
        output.append(_lowerCamelCase )
     else:
        lowerCAmelCase_			=      []
        for i in range(_lowerCamelCase ):
           lowerCAmelCase_			=      placeholder_token + F'''_{i}'''
           self.try_adding_tokens(_lowerCamelCase    ,     *_lowerCamelCase    ,     **_lowerCamelCase )
           output.append(_lowerCamelCase )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
     for token in self.token_map:
        if token in placeholder_token:
           raise ValueError(
               F'''The tokenizer already has placeholder token {token} that can get confused with'''
               F''' {placeholder_token}keep placeholder tokens independent''' )
     lowerCAmelCase_			=      output





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase=False    ,     _lowerCamelCase=1.0 ):
     if isinstance(_lowerCamelCase    ,     _lowerCamelCase ):
        lowerCAmelCase_			=      []
        for i in range(len(_lowerCamelCase ) ):
           output.append(self.replace_placeholder_tokens_in_text(text[i]    ,     vector_shuffle=_lowerCamelCase ) )
        return output
     for placeholder_token in self.token_map:
        if placeholder_token in text:
           lowerCAmelCase_			=      self.token_map[placeholder_token]
           lowerCAmelCase_			=      tokens[: 1 + int(len(_lowerCamelCase ) * prop_tokens_to_load )]
           if vector_shuffle:
              lowerCAmelCase_			=      copy.copy(_lowerCamelCase )
              random.shuffle(_lowerCamelCase )
           lowerCAmelCase_			=      text.replace(_lowerCamelCase    ,     ''' '''.join(_lowerCamelCase ) )
     return text





  def __call__(	self    ,     _lowerCamelCase    ,     *_lowerCamelCase    ,     _lowerCamelCase=False    ,     _lowerCamelCase=1.0    ,     **_lowerCamelCase ):
     return super().__call__(
         self.replace_placeholder_tokens_in_text(
             _lowerCamelCase    ,     vector_shuffle=_lowerCamelCase    ,     prop_tokens_to_load=_lowerCamelCase )    ,     *_lowerCamelCase    ,     **_lowerCamelCase    ,     )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     *_lowerCamelCase    ,     _lowerCamelCase=False    ,     _lowerCamelCase=1.0    ,     **_lowerCamelCase ):
     return super().encode(
         self.replace_placeholder_tokens_in_text(
             _lowerCamelCase    ,     vector_shuffle=_lowerCamelCase    ,     prop_tokens_to_load=_lowerCamelCase )    ,     *_lowerCamelCase    ,     **_lowerCamelCase    ,     )






'''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__)





