"""simple docstring"""





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		,			a		,			a		,			a = None		,			a = None							)       ->					Optional[int]:
             super().__init__()
             lowercase__			:		Tuple       =	pad_token_id
             lowercase__			:		Optional[int]       =	max_length
             lowercase__			:		Optional[Any]       =	vocab
             lowercase__			:		List[Any]       =	merges
             lowercase__			:		Any       =	BytePairTokenizer(lowerCAmelCase__		,			lowerCAmelCase__		,			sequence_length=lowerCAmelCase__							)


      @classmethod
      def 		_UpperCAmelCase       (   cls		,			a		,			*a		,			**a							)       ->					Optional[Any]:
             lowercase__			:		str       =	[""" """.join(lowerCAmelCase__							) for m in tokenizer.bpe_ranks.keys()]
             lowercase__			:		Dict       =	tokenizer.get_vocab()
             return cls(lowerCAmelCase__		,			lowerCAmelCase__		,			*lowerCAmelCase__		,			**lowerCAmelCase__							)


      @classmethod
      def 		_UpperCAmelCase       (   cls		,			a		,			*a		,			**a							)       ->					Optional[Any]:
             lowercase__			:		str       =	GPTaTokenizer.from_pretrained(lowerCAmelCase__		,			*lowerCAmelCase__		,			**lowerCAmelCase__							)
             return cls.from_tokenizer(lowerCAmelCase__		,			*lowerCAmelCase__		,			**lowerCAmelCase__							)


      @classmethod
      def 		_UpperCAmelCase       (   cls		,			a							)       ->					Union[str, Any]:
             return cls(**lowerCAmelCase__							)


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


      def 		_UpperCAmelCase       (   self		,			a		,			a = None							)       ->					Optional[int]:
             lowercase__			:		int       =	self.tf_tokenizer(lowerCAmelCase__							)
             lowercase__			:		List[Any]       =	tf.ones_like(lowerCAmelCase__							)

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

                    if max_length is not None:
                           lowercase__			:		Tuple       =	pad_model_inputs(
                               lowerCAmelCase__		,			max_seq_length=lowerCAmelCase__		,			pad_value=self.pad_token_id							)

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






'''simple docstring'''




from __future__ import annotations


def 				lowerCAmelCase_   (	_lowerCamelCase:   list[int]       ,      _lowerCamelCase:   list[int]       ,      _lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		list(range(len(_lowerCamelCase					)					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[v / w for v, w in zip(_lowerCamelCase       ,      _lowerCamelCase					)]
		index.sort(key=lambda _lowerCamelCase					: ratio[i]       ,      reverse=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		float           =		0
		__SCREAMING_SNAKE_CASE       :		list[float]           =		[0] * len(_lowerCamelCase					)
		for i in index:
				if weight[i] <= capacity:
						__SCREAMING_SNAKE_CASE       :		str           =		1
						max_value += value[i]
						capacity -= weight[i]
				else:
						__SCREAMING_SNAKE_CASE       :		int           =		capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
						import doctest

						doctest.testmod()