






from __future__ import annotations

import numpy as np

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:Optional[int]   =   np.shape(snake_case			)
				if rows != columns:
								__magic_name__		:Dict   =   (
								    '''\'table\' has to be of square shaped array but got a '''
								    f'''{rows}x{columns} array:\n{table}'''
								)
								raise ValueError(snake_case			)

				__magic_name__		:List[str]   =   np.zeros((rows, columns)			)
				__magic_name__		:Union[str, Any]   =   np.zeros((rows, columns)			)
				for i in range(snake_case			):
								for j in range(snake_case			):
												__magic_name__		:List[str]   =   sum(lower[i][k] * upper[k][j] for k in range(snake_case			)			)
												if upper[j][j] == 0:
																raise ArithmeticError('''No LU decomposition exists'''			)
												__magic_name__		:str   =   (table[i][j] - total) / upper[j][j]
								__magic_name__		:int   =   1
								for j in range(snake_case,	snake_case			):
												__magic_name__		:Any   =   sum(lower[i][k] * upper[k][j] for k in range(snake_case			)			)
												__magic_name__		:Dict   =   table[i][j] - total
				return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()












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      ):

						def __init__(		self :Optional[int]     ,			__snake_case :Dict[str, int]     ,			__snake_case :List[str]     ,			__snake_case :int = None     ,			__snake_case :int = None			):
										'''simple docstring'''

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

						@classmethod
						def      A__    (		cls :List[Any]     ,			__snake_case :GPTaTokenizer     ,			*__snake_case :int     ,			**__snake_case :Any			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=[""" """.join(__snake_case			) for m in tokenizer.bpe_ranks.keys()]
										__magic_name__						:						str										=tokenizer.get_vocab()
										return cls(__snake_case     ,			__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Dict     ,			__snake_case :Union[str, os.PathLike]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :int			):
										'''simple docstring'''

										__magic_name__						:						Dict										=GPTaTokenizer.from_pretrained(__snake_case     ,			*__snake_case     ,			**__snake_case			)
										return cls.from_tokenizer(__snake_case     ,			*__snake_case     ,			**__snake_case			)

						@classmethod
						def      A__    (		cls :Optional[Any]     ,			__snake_case :List[Any]			):
										'''simple docstring'''

										return cls(**__snake_case			)

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

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


						def      A__    (		self :List[Any]     ,			__snake_case :Dict     ,			__snake_case :int = None			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=self.tf_tokenizer(__snake_case			)
										__magic_name__						:						Tuple										=tf.ones_like(__snake_case			)

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

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

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




