





import copy
import random

from transformers import CLIPTokenizer



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  def __init__(    self			:		Dict ,  *lowerCAmelCase_			:		List[Any] ,  **lowerCAmelCase_			:		Optional[int]					)							->						List[str]:
     super().__init__(*lowerCAmelCase_ ,  **lowerCAmelCase_					)
     __lowerCAmelCase							     =     {}






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		str ,  *lowerCAmelCase_			:		List[Any] ,  **lowerCAmelCase_			:		str					)							->						Optional[Any]:
     __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     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Optional[int] ,  *lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str=1 ,  **lowerCAmelCase_			:		Dict					)							->						int:
     __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     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Any=False ,  lowerCAmelCase_			:		Dict=1.0					)							->						Optional[int]:
     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			:		Optional[int] ,  lowerCAmelCase_			:		Tuple ,  *lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		Optional[int]=False ,  lowerCAmelCase_			:		Tuple=1.0 ,  **lowerCAmelCase_			:		str					)							->						Any:
     return super().__call__(
         self.replace_placeholder_tokens_in_text(
             lowerCAmelCase_ ,  vector_shuffle=lowerCAmelCase_ ,  prop_tokens_to_load=lowerCAmelCase_					) ,  *lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Any ,  *lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Union[str, Any]=False ,  lowerCAmelCase_			:		str=1.0 ,  **lowerCAmelCase_			:		Any					)							->						List[str]:
     return super().encode(
         self.replace_placeholder_tokens_in_text(
             lowerCAmelCase_ ,  vector_shuffle=lowerCAmelCase_ ,  prop_tokens_to_load=lowerCAmelCase_					) ,  *lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case  :   Optional[Any]						   =  {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   int						   =  ['FNetTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   Tuple						   =  ['FNetTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :   List[str]						   =  [
         'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
         'FNetForMaskedLM',
         'FNetForMultipleChoice',
         'FNetForNextSentencePrediction',
         'FNetForPreTraining',
         'FNetForQuestionAnswering',
         'FNetForSequenceClassification',
         'FNetForTokenClassification',
         'FNetLayer',
         'FNetModel',
         'FNetPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet import FNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_fnet_fast import FNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_fnet import (
              FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              FNetForMaskedLM,
              FNetForMultipleChoice,
              FNetForNextSentencePrediction,
              FNetForPreTraining,
              FNetForQuestionAnswering,
              FNetForSequenceClassification,
              FNetForTokenClassification,
              FNetLayer,
              FNetModel,
              FNetPreTrainedModel,
          )


else:
     import sys

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

