
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     :							Optional[Any]			,			lowerCAmelCase_     :							Dict[str, int]			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							int = None			,			lowerCAmelCase_     :							int = None     ):

      """simple docstring"""
      super().__init__()
      _A:  str					=       pad_token_id
      _A:  Dict					=       max_length
      _A:  Any					=       vocab
      _A:  str					=       merges
      _A:  int					=       BytePairTokenizer(__lowercase			,			__lowercase			,			sequence_length=__lowercase     )


   @classmethod
   def  __magic_name__				(					cls     :							Tuple			,			lowerCAmelCase_     :							GPTaTokenizer			,			*lowerCAmelCase_     :							Optional[int]			,			**lowerCAmelCase_     :							int     ):

      """simple docstring"""
      _A:  int					=       [""" """.join(__lowercase     ) for m in tokenizer.bpe_ranks.keys()]
      _A:  str					=       tokenizer.get_vocab()
      return cls(__lowercase			,			__lowercase			,			*__lowercase			,			**__lowercase     )


   @classmethod
   def  __magic_name__				(					cls     :							Optional[Any]			,			lowerCAmelCase_     :							Union[str, os.PathLike]			,			*lowerCAmelCase_     :							List[Any]			,			**lowerCAmelCase_     :							Optional[Any]     ):

      """simple docstring"""
      _A:  int					=       GPTaTokenizer.from_pretrained(__lowercase			,			*__lowercase			,			**__lowercase     )
      return cls.from_tokenizer(__lowercase			,			*__lowercase			,			**__lowercase     )


   @classmethod
   def  __magic_name__				(					cls     :							Union[str, Any]			,			lowerCAmelCase_     :							str     ):

      """simple docstring"""
      return cls(**__lowercase     )


   def  __magic_name__				(					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  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							int = None     ):

      """simple docstring"""
      _A:  Optional[Any]					=       self.tf_tokenizer(__lowercase     )
      _A:  List[Any]					=       tf.ones_like(__lowercase     )

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

         if max_length is not None:
            _A:  List[Any]					=       pad_model_inputs(
                __lowercase			,			max_seq_length=__lowercase			,			pad_value=self.pad_token_id     )

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











def 				lowerCamelCase__      (    __lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     Union[str, Any]		):
  __UpperCAmelCase							:	Tuple           =  [1]
  for i in range(2  ,		__lowerCamelCase		):
    factorials.append(factorials[-1] * i		)
  assert 0 <= k < factorials[-1] * n, "k out of bounds"

  __UpperCAmelCase							:	Optional[Any]           =  []
  __UpperCAmelCase							:	str           =  list(range(__lowerCamelCase		)		)

  # Find permutation
  while factorials:
    __UpperCAmelCase							:	Any           =  factorials.pop()
    __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  divmod(__lowerCamelCase  ,		__lowerCamelCase		)
    permutation.append(elements[number]		)
    elements.remove(elements[number]		)
  permutation.append(elements[0]		)

  return permutation


if __name__ == "__main__":
       import doctest

       doctest.testmod()


