





'''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  SCREAMING_SNAKE_CASE    (tf.keras.layers.Layer							):


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None):

          '''simple docstring'''






          super().__init__()
          __A						: Any      =       pad_token_id
          __A						: Tuple      =       max_length
          __A						: Any      =       vocab
          __A						: str      =       merges
          __A						: Dict      =       BytePairTokenizer(_UpperCAmelCase						,	_UpperCAmelCase						,	sequence_length=_UpperCAmelCase)


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls						,	_UpperCAmelCase						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          __A						: List[Any]      =       [' '.join(_UpperCAmelCase) for m in tokenizer.bpe_ranks.keys()]
          __A						: Tuple      =       tokenizer.get_vocab()
          return cls(_UpperCAmelCase						,	_UpperCAmelCase						,	*_UpperCAmelCase						,	**_UpperCAmelCase)


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls						,	_UpperCAmelCase						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          __A						: List[str]      =       GPTaTokenizer.from_pretrained(_UpperCAmelCase						,	*_UpperCAmelCase						,	**_UpperCAmelCase)
          return cls.from_tokenizer(_UpperCAmelCase						,	*_UpperCAmelCase						,	**_UpperCAmelCase)


     @classmethod
     def 							SCREAMING_SNAKE_CASE  (						cls						,	_UpperCAmelCase):

          '''simple docstring'''






          return cls(**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return {
              "vocab": self.vocab,
              "merges": self.merges,
              "max_length": self.max_length,
              "pad_token_id": self.pad_token_id,
          }


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = None):

          '''simple docstring'''






          __A						: Any      =       self.tf_tokenizer(_UpperCAmelCase)
          __A						: Any      =       tf.ones_like(_UpperCAmelCase)

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

               if max_length is not None:
                    __A ,__A						: List[str]      =       pad_model_inputs(
                        _UpperCAmelCase						,	max_seq_length=_UpperCAmelCase						,	pad_value=self.pad_token_id)

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






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__    :      List[str]						=    {
    '''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
    '''processing_git''': ['''GitProcessor'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase__    :      Any						=    [
      '''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GitForCausalLM''',
      '''GitModel''',
      '''GitPreTrainedModel''',
      '''GitVisionModel''',
  ]

if TYPE_CHECKING:
  from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
  from .processing_git import GitProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_git import (
        GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        GitForCausalLM,
        GitModel,
        GitPreTrainedModel,
        GitVisionModel,
    )

else:
  import sys

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