


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 							lowercase_					(			tf.keras.layers.Layer):






							"""simple docstring"""







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







													"""simple docstring"""


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


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







													"""simple docstring"""


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


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







													"""simple docstring"""


													a_		      =		GPTaTokenizer.from_pretrained(_UpperCAmelCase      ,     *_UpperCAmelCase      ,     **_UpperCAmelCase		)
													return cls.from_tokenizer(_UpperCAmelCase      ,     *_UpperCAmelCase      ,     **_UpperCAmelCase		)


							@classmethod
							def     lowercase__  ( cls      ,     _UpperCAmelCase		):







													"""simple docstring"""


													return cls(**_UpperCAmelCase		)


							def     lowercase__  ( self		):







													"""simple docstring"""


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






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







													"""simple docstring"""


													a_		      =		self.tf_tokenizer(_UpperCAmelCase		)
													a_		      =		tf.ones_like(_UpperCAmelCase		)

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

																			if max_length is not None:
																									a_      , a_		      =		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"""

def 		_snake_case	(   _snake_case				:   str				)       ->   str:






      '''simple docstring'''


      return " ".join(
          ''.join(word[::-1]				) if len(_snake_case				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('''Hey wollef sroirraw'''))

