



'''simple docstring'''





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__      					=     {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__      					=     ["FNetTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__      					=     ["FNetTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__      					=     [
     "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

 SCREAMING_SNAKE_CASE__      					=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''simple docstring'''





import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer


class 				snake_case (tf.keras.layers.Layer   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_ = None     ,UpperCAmelCase_ = None      )  ->							List[Any]:
           super().__init__()
           lowercase__		       =						pad_token_id
           lowercase__		       =						max_length
           lowercase__		       =						vocab
           lowercase__		       =						merges
           lowercase__		       =						BytePairTokenizer(UpperCAmelCase_     ,UpperCAmelCase_     ,sequence_length=UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						[" ".join(UpperCAmelCase_      ) for m in tokenizer.bpe_ranks.keys()]
           lowercase__		       =						tokenizer.get_vocab()
           return cls(UpperCAmelCase_     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Optional[Any]:
           lowercase__		       =						GPTaTokenizer.from_pretrained(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )
           return cls.from_tokenizer(UpperCAmelCase_     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_      )  ->							Optional[Any]:
           return cls(**UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Tuple:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							Tuple:
           lowercase__		       =						self.tf_tokenizer(UpperCAmelCase_      )
           lowercase__		       =						tf.ones_like(UpperCAmelCase_      )

           if self.pad_token_id is not None:
               # pad the tokens up to max length
               lowercase__		       =						max_length if max_length is not None else self.max_length

               if max_length is not None:
                   lowercase__       ,       lowercase__		       =						pad_model_inputs(
                       UpperCAmelCase_     ,max_seq_length=UpperCAmelCase_     ,pad_value=self.pad_token_id      )

           return {"attention_mask": attention_mask, "input_ids": input_ids}




