



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  lowerCamelCase__    (							tf.keras.layers.Layer):




  '''simple docstring'''



  def __init__(self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = None				)  ->    Any:

      """simple docstring"""






      super().__init__()
      lowerCAmelCase__  :       List[Any]				=  pad_token_id
      lowerCAmelCase__  :       Optional[Any]				=  max_length
      lowerCAmelCase__  :       Optional[int]				=  vocab
      lowerCAmelCase__  :       Tuple				=  merges
      lowerCAmelCase__  :       Dict				=  BytePairTokenizer(__SCREAMING_SNAKE_CASE	,__SCREAMING_SNAKE_CASE	,sequence_length=__SCREAMING_SNAKE_CASE				)



  @classmethod
  def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,*__lowerCamelCase	,**__lowerCamelCase				)  ->    str:

      """simple docstring"""






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



  @classmethod
  def 	lowerCAmelCase__   (cls	,__lowerCamelCase	,*__lowerCamelCase	,**__lowerCamelCase				)  ->    Optional[Any]:

      """simple docstring"""






      lowerCAmelCase__  :       Optional[int]				=  GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE	,*__SCREAMING_SNAKE_CASE	,**__SCREAMING_SNAKE_CASE				)
      return cls.from_tokenizer(__SCREAMING_SNAKE_CASE	,*__SCREAMING_SNAKE_CASE	,**__SCREAMING_SNAKE_CASE				)



  @classmethod
  def 	lowerCAmelCase__   (cls	,__lowerCamelCase				)  ->    int:

      """simple docstring"""






      return cls(**__SCREAMING_SNAKE_CASE				)



  def 	lowerCAmelCase__   (self				)  ->    Dict:

      """simple docstring"""






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



  def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    Optional[Any]:

      """simple docstring"""






      lowerCAmelCase__  :       List[Any]				=  self.tf_tokenizer(__SCREAMING_SNAKE_CASE				)
      lowerCAmelCase__  :       List[Any]				=  tf.ones_like(__SCREAMING_SNAKE_CASE				)

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

          if max_length is not None:
              lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  pad_model_inputs(
                  __SCREAMING_SNAKE_CASE	,max_seq_length=__SCREAMING_SNAKE_CASE	,pad_value=self.pad_token_id				)

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any ,lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       str				=  [0 for i in range(r + 1)]
				# nc0 = 1
				lowerCAmelCase__  :       Tuple				=  1
				for i in range(1 ,n + 1):
								# to compute current row from previous row.
								lowerCAmelCase__  :       Optional[int]				=  min(lowerCamelCase_ ,lowerCamelCase_)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]

print(binomial_coefficient(n=1_0, r=5))

