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  __magic_name__  (		tf.keras.layers.Layer    ):





   """simple docstring"""




   def __init__(	self					:  List[Any]		,  _lowercase					:  Dict[str, int]		,  _lowercase					:  List[str]		,  _lowercase					:  int = None		,  _lowercase					:  int = None							):



    """simple docstring"""



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



   @classmethod
   def   lowerCAmelCase						(	cls					:  Tuple		,  _lowercase					:  GPTaTokenizer		,  *_lowercase					:  Union[str, Any]		,  **_lowercase					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= [''' '''.join(_lowercase							) for m in tokenizer.bpe_ranks.keys()]
    _UpperCamelCase:     int							= tokenizer.get_vocab()
    return cls(_lowercase		,  _lowercase		,  *_lowercase		,  **_lowercase							)



   @classmethod
   def   lowerCAmelCase						(	cls					:  Union[str, Any]		,  _lowercase					:  Union[str, os.PathLike]		,  *_lowercase					:  List[str]		,  **_lowercase					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= GPTaTokenizer.from_pretrained(_lowercase		,  *_lowercase		,  **_lowercase							)
    return cls.from_tokenizer(_lowercase		,  *_lowercase		,  **_lowercase							)



   @classmethod
   def   lowerCAmelCase						(	cls					:  Union[str, Any]		,  _lowercase					:  Tuple							):



    """simple docstring"""



    return cls(**_lowercase							)



   def   lowerCAmelCase						(	self					:  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					:  Optional[Any]		,  _lowercase					:  Any		,  _lowercase					:  int = None							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= self.tf_tokenizer(_lowercase							)
    _UpperCamelCase:     Dict							= tf.ones_like(_lowercase							)

    if self.pad_token_id is not None:
     # pad the tokens up to max length
     _UpperCamelCase:     Optional[int]							= max_length if max_length is not None else self.max_length

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

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

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_			    =							logging.get_logger(__name__)




class  __magic_name__  (		__a    ):





   """simple docstring"""




   def __init__(	self					:  str		,  *_lowercase					:  str		,  **_lowercase					:  int							):



    """simple docstring"""



    warnings.warn(
        '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
        ''' Please use PerceiverImageProcessor instead.'''		,  _lowercase		,  )
    super().__init__(*_lowercase		,  **_lowercase							)