


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 							_UpperCamelCase (						tf.keras.layers.Layer						):

   '''simple docstring'''







   def __init__(  self  : Tuple       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : List[str]       ,  _lowerCamelCase  : Any = None       ,  _lowerCamelCase  : Any = None							):






         '''simple docstring'''



         super().__init__()
         __lowerCamelCase     :      List[Any]       						=						pad_token_id
         __lowerCamelCase     :      Tuple       						=						max_length
         __lowerCamelCase     :      List[Any]       						=						vocab
         __lowerCamelCase     :      int       						=						merges
         __lowerCamelCase     :      Tuple       						=						BytePairTokenizer(_lowercase       ,  _lowercase       ,  sequence_length=_lowercase							)

   @classmethod
   def   _snake_case    (  cls  : Dict       ,  _lowerCamelCase  : Tuple       ,  *_lowerCamelCase  : Optional[Any]       ,  **_lowerCamelCase  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						[' '.join(_lowercase							) for m in tokenizer.bpe_ranks.keys()]
         __lowerCamelCase     :      Union[str, Any]       						=						tokenizer.get_vocab()
         return cls(_lowercase       ,  _lowercase       ,  *_lowercase       ,  **_lowercase							)

   @classmethod
   def   _snake_case    (  cls  : Union[str, Any]       ,  _lowerCamelCase  : List[Any]       ,  *_lowerCamelCase  : List[str]       ,  **_lowerCamelCase  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						GPTaTokenizer.from_pretrained(_lowercase       ,  *_lowercase       ,  **_lowercase							)
         return cls.from_tokenizer(_lowercase       ,  *_lowercase       ,  **_lowercase							)

   @classmethod
   def   _snake_case    (  cls  : Dict       ,  _lowerCamelCase  : Tuple							):






         '''simple docstring'''



         return cls(**_lowercase							)

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



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


   def   _snake_case    (  self  : Any       ,  _lowerCamelCase  : Optional[Any]       ,  _lowerCamelCase  : Optional[Any] = None							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						self.tf_tokenizer(_lowercase							)
         __lowerCamelCase     :      str       						=						tf.ones_like(_lowercase							)

         if self.pad_token_id is not None:
               # pad the tokens up to max length
               __lowerCamelCase     :      List[str]       						=						max_length if max_length is not None else self.max_length

               if max_length is not None:
                     __lowerCamelCase     :      str       						=						pad_model_inputs(
                         _lowercase       ,  max_seq_length=_lowercase       ,  pad_value=self.pad_token_id							)

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase    :						Optional[Any]     =							{
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[Any]     =							[
        'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Swinv2ForImageClassification',
        'Swinv2ForMaskedImageModeling',
        'Swinv2Model',
        'Swinv2PreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_swinva import (
            SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinvaForImageClassification,
            SwinvaForMaskedImageModeling,
            SwinvaModel,
            SwinvaPreTrainedModel,
        )


else:
    import sys

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



