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


  """simple docstring"""

  def __init__(		self      :       Optional[int]   ,				lowercase_      :       Dict[str, int]   ,				lowercase_      :       List[str]   ,				lowercase_      :       int = None   ,				lowercase_      :       int = None      ):







    '''simple docstring'''


    super().__init__()
    lowercase_     =       pad_token_id
    lowercase_     =       max_length
    lowercase_     =       vocab
    lowercase_     =       merges
    lowercase_     =       BytePairTokenizer(lowercase_   ,				lowercase_   ,				sequence_length=lowercase_      )




  @classmethod
  def 		lowerCamelCase__  (		cls      :       int   ,				lowercase_      :       GPTaTokenizer   ,				*lowercase_      :       Optional[Any]   ,				**lowercase_      :       Tuple      ):







    '''simple docstring'''


    lowercase_     =       [""" """.join(lowercase_      ) for m in tokenizer.bpe_ranks.keys()]
    lowercase_     =       tokenizer.get_vocab()
    return cls(lowercase_   ,				lowercase_   ,				*lowercase_   ,				**lowercase_      )




  @classmethod
  def 		lowerCamelCase__  (		cls      :       Dict   ,				lowercase_      :       Union[str, os.PathLike]   ,				*lowercase_      :       List[str]   ,				**lowercase_      :       Optional[int]      ):







    '''simple docstring'''


    lowercase_     =       GPTaTokenizer.from_pretrained(lowercase_   ,				*lowercase_   ,				**lowercase_      )
    return cls.from_tokenizer(lowercase_   ,				*lowercase_   ,				**lowercase_      )




  @classmethod
  def 		lowerCamelCase__  (		cls      :       Tuple   ,				lowercase_      :       List[Any]      ):







    '''simple docstring'''


    return cls(**lowercase_      )




  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


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





  def 		lowerCamelCase__  (		self      :       int   ,				lowercase_      :       Tuple   ,				lowercase_      :       int = None      ):







    '''simple docstring'''


    lowercase_     =       self.tf_tokenizer(lowercase_      )
    lowercase_     =       tf.ones_like(lowercase_      )

    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(
            lowercase_   ,				max_seq_length=lowercase_   ,				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


__snake_case						  =		{
    """configuration_git""": ["""GIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GitConfig""", """GitVisionConfig"""],
    """processing_git""": ["""GitProcessor"""],
}

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

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

