




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     __a(	tf.keras.layers.Layer  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None    )					->						Union[str, Any]:
         super().__init__()
         UpperCAmelCase_     :				Union[str, Any]  =     pad_token_id
         UpperCAmelCase_     :				Union[str, Any]  =     max_length
         UpperCAmelCase_     :				Any  =     vocab
         UpperCAmelCase_     :				Optional[Any]  =     merges
         UpperCAmelCase_     :				Union[str, Any]  =     BytePairTokenizer(_lowercase  ,_lowercase  ,sequence_length=_lowercase    )





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						str:
         UpperCAmelCase_     :				List[Any]  =     [''' '''.join(_lowercase    ) for m in tokenizer.bpe_ranks.keys()]
         UpperCAmelCase_     :				Optional[Any]  =     tokenizer.get_vocab()
         return cls(_lowercase  ,_lowercase  ,*_lowercase  ,**_lowercase    )





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,*_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						int:
         UpperCAmelCase_     :				Union[str, Any]  =     GPTaTokenizer.from_pretrained(_lowercase  ,*_lowercase  ,**_lowercase    )
         return cls.from_tokenizer(_lowercase  ,*_lowercase  ,**_lowercase    )





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE    )					->						List[str]:
         return cls(**_lowercase    )





    def 				a__ ( self    )					->						List[Any]:
         return {
             "vocab": self.vocab,
             "merges": self.merges,
             "max_length": self.max_length,
             "pad_token_id": self.pad_token_id,
         }





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = None    )					->						Optional[Any]:
         UpperCAmelCase_     :				int  =     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_     :				Union[str, Any]  =     max_length if max_length is not None else self.max_length

              if max_length is not None:
                   UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     pad_model_inputs(
                       _lowercase  ,max_seq_length=_lowercase  ,pad_value=self.pad_token_id    )

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


'''simple docstring'''

SCREAMING_SNAKE_CASE							:				int     	=			8.3_1_4_4_5_9_8




def 							_UpperCamelCase				(   lowerCAmelCase__:					float		,lowerCAmelCase__:					float   )      -> float:
							if temperature < 0:
														raise Exception('Temperature cannot be less than 0 K'   )
							if molar_mass <= 0:
														raise Exception('Molar mass cannot be less than or equal to 0 kg/mol'   )
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
							import doctest

							# run doctest
							doctest.testmod()

							# example
							SCREAMING_SNAKE_CASE							:				List[str]     	=			300
							SCREAMING_SNAKE_CASE							:				List[Any]     	=			28
							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			rms_speed_of_molecule(temperature, molar_mass)
							print(f"Vrms of Nitrogen gas at 300 K is {vrms} m/s")





