




from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate




@dataclass(frozen=UpperCamelCase__      )
class 		__A   (      UpperCamelCase__      ):
						# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
						UpperCamelCase      =       field(default="""question-answering-extractive"""       ,    metadata={"""include_in_asdict_even_if_is_default""": True}      )
						UpperCamelCase      =       Features({"""question""": Value("""string"""      ), """context""": Value("""string"""      )}      )
						UpperCamelCase      =       Features(
						    {
						        """answers""": Sequence(
						            {
						                """text""": Value("""string"""      ),
						                """answer_start""": Value("""int32"""      ),
						            }      )
						    }      )
						UpperCamelCase      =       "question"
						UpperCamelCase      =       "context"
						UpperCamelCase      =       "answers"

						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}










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}




