



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      :			int	,	__lowerCamelCase      :			Dict[str, int]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			int = None	,	__lowerCamelCase      :			int = None )      ->				str:
									super().__init__()
									A							:					Optional[Any]              =						pad_token_id
									A							:					Dict              =						max_length
									A							:					Tuple              =						vocab
									A							:					str              =						merges
									A							:					int              =						BytePairTokenizer(__lowerCamelCase	,	__lowerCamelCase	,	sequence_length=__lowerCamelCase )







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Any	,	__lowerCamelCase      :			GPTaTokenizer	,	*__lowerCamelCase      :			List[str]	,	**__lowerCamelCase      :			Optional[int] )      ->				List[str]:
									A							:					Any              =						[" ".join(__lowerCamelCase ) for m in tokenizer.bpe_ranks.keys()]
									A							:					List[Any]              =						tokenizer.get_vocab()
									return cls(__lowerCamelCase	,	__lowerCamelCase	,	*__lowerCamelCase	,	**__lowerCamelCase )







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, os.PathLike]	,	*__lowerCamelCase      :			Optional[int]	,	**__lowerCamelCase      :			Tuple )      ->				int:
									A							:					List[Any]              =						GPTaTokenizer.from_pretrained(__lowerCamelCase	,	*__lowerCamelCase	,	**__lowerCamelCase )
									return cls.from_tokenizer(__lowerCamelCase	,	*__lowerCamelCase	,	**__lowerCamelCase )







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			List[Any]	,	__lowerCamelCase      :			List[Any] )      ->				Dict:
									return cls(**__lowerCamelCase )







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			int = None )      ->				int:
									A							:					Optional[Any]              =						self.tf_tokenizer(__lowerCamelCase )
									A							:					int              =						tf.ones_like(__lowerCamelCase )

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

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

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




from collections import deque

from .hash_table import HashTable

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''







						def __init__(	self      :			Union[str, Any]	,	*__lowerCamelCase      :			Dict	,	**__lowerCamelCase      :			int )      ->				Optional[int]:
									super().__init__(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						deque([] ) if self.values[key] is None else self.values[key]
									self.values[key].appendleft(__lowerCamelCase )
									A							:					Dict              =						self.values[key]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									return (
									    sum(self.charge_factor - len(__lowerCamelCase ) for slot in self.values )
									    / self.size_table
									    * self.charge_factor
									)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			List[Any]=None )      ->				Optional[int]:
									if not (
									    len(self.values[key] ) == self.charge_factor and self.values.count(__lowerCamelCase ) == 0
									):
												return key
									return super()._collision_resolution(__lowerCamelCase	,	__lowerCamelCase )