






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      ):


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase = None       ,    __UpperCamelCase = None						)->						Optional[int]:
           super().__init__()
           __lowerCAmelCase             =       pad_token_id
           __lowerCAmelCase             =       max_length
           __lowerCAmelCase             =       vocab
           __lowerCAmelCase             =       merges
           __lowerCAmelCase             =       BytePairTokenizer(__UpperCamelCase       ,    __UpperCamelCase       ,    sequence_length=__UpperCamelCase						)


      @classmethod
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						str:
           __lowerCAmelCase             =       [" ".join(__UpperCamelCase						) for m in tokenizer.bpe_ranks.keys()]
           __lowerCAmelCase             =       tokenizer.get_vocab()
           return cls(__UpperCamelCase       ,    __UpperCamelCase       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)


      @classmethod
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)->						Any:
           __lowerCAmelCase             =       GPTaTokenizer.from_pretrained(__UpperCamelCase       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)
           return cls.from_tokenizer(__UpperCamelCase       ,    *__UpperCamelCase       ,    **__UpperCamelCase						)


      @classmethod
      def 			__UpperCAmelCase		(						cls       ,    __UpperCamelCase						)->						Optional[Any]:
           return cls(**__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase = None						)->						Optional[int]:
           __lowerCAmelCase             =       self.tf_tokenizer(__UpperCamelCase						)
           __lowerCAmelCase             =       tf.ones_like(__UpperCamelCase						)

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

                if max_length is not None:
                     __lowerCAmelCase      ,			__lowerCAmelCase             =       pad_model_inputs(
                         __UpperCamelCase       ,    max_seq_length=__UpperCamelCase       ,    pad_value=self.pad_token_id						)

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














from math import pow, sqrt





def    __lowerCAmelCase   (    *__snake_case							):
     __lowerCAmelCase             =       len(__snake_case							) > 0 and all(value > 0.0 for value in values							)
     return result





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     return (
         round(sqrt(molar_mass_a / molar_mass_a							)       ,			6							)
         if validate(__snake_case       ,			__snake_case							)
         else ValueError("Input Error: Molar mass values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(effusion_rate * sqrt(molar_mass_a / molar_mass_a							)       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(effusion_rate / sqrt(molar_mass_a / molar_mass_a							)       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(molar_mass / pow(effusion_rate_a / effusion_rate_a       ,			2							)       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     return (
         round(pow(effusion_rate_a / effusion_rate_a       ,			2							) / molar_mass       ,			6							)
         if validate(__snake_case       ,			__snake_case       ,			__snake_case							)
         else ValueError(
             "Input Error: Molar mass and effusion rate values must greater than 0."							)
     )






