



'''simple docstring'''





from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  "EncodecFeatureExtractor"
						a__   =  ("T5Tokenizer", "T5TokenizerFast")







						def __init__(	self      :			Dict	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[Any] )      ->				Optional[int]:
									super().__init__(__lowerCamelCase	,	__lowerCamelCase )
									A							:					int              =						self.feature_extractor
									A							:					List[Any]              =						False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			Tuple=None	,	__lowerCamelCase      :			Dict=True )      ->				Union[str, Any]:
									return self.tokenizer.get_decoder_prompt_ids(task=__lowerCamelCase	,	language=__lowerCamelCase	,	no_timestamps=__lowerCamelCase )







						def __call__(	self      :			Dict	,	*__lowerCamelCase      :			Tuple	,	**__lowerCamelCase      :			Any )      ->				Optional[Any]:
									# For backward compatibility
									if self._in_target_context_manager:
												return self.current_processor(*__lowerCamelCase	,	**__lowerCamelCase )

									A							:					Any              =						kwargs.pop("audio"	,	__lowerCamelCase )
									A							:					int              =						kwargs.pop("sampling_rate"	,	__lowerCamelCase )
									A							:					Any              =						kwargs.pop("text"	,	__lowerCamelCase )
									if len(__lowerCamelCase ) > 0:
												A							:					Optional[Any]              =						args[0]
												A							:					Dict              =						args[1:]

									if audio is None and text is None:
												raise ValueError("You need to specify either an `audio` or `text` input to process." )

									if text is not None:
												A							:					Union[str, Any]              =						self.tokenizer(__lowerCamelCase	,	**__lowerCamelCase )

									if audio is not None:
												A							:					Dict              =						self.feature_extractor(__lowerCamelCase	,	*__lowerCamelCase	,	sampling_rate=__lowerCamelCase	,	**__lowerCamelCase )

									if audio is None:
												return inputs

									elif text is None:
												return audio_inputs

									else:
												A							:					int              =						audio_inputs["input_values"]
												if "padding_mask" in audio_inputs:
															A							:					Dict              =						audio_inputs["padding_mask"]
												return inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	*__lowerCamelCase      :			int	,	**__lowerCamelCase      :			Union[str, Any] )      ->				Optional[Any]:
									A							:					Optional[Any]              =						kwargs.pop("audio"	,	__lowerCamelCase )
									A							:					Tuple              =						kwargs.pop("padding_mask"	,	__lowerCamelCase )

									if len(__lowerCamelCase ) > 0:
												A							:					Any              =						args[0]
												A							:					Tuple              =						args[1:]

									if audio_values is not None:
												return self._decode_audio(__lowerCamelCase	,	padding_mask=__lowerCamelCase )
									else:
												return self.tokenizer.batch_decode(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	*__lowerCamelCase      :			Tuple	,	**__lowerCamelCase      :			Optional[Any] )      ->				Optional[int]:
									return self.tokenizer.decode(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Optional = None )      ->				List[np.ndarray]:
									A							:					List[str]              =						to_numpy(__lowerCamelCase )
									A							:					List[str]              =						audio_values.shape

									if padding_mask is None:
												return list(__lowerCamelCase )

									A							:					List[Any]              =						to_numpy(__lowerCamelCase )

									# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
									# token (so that the generated audio values are **not** treated as padded tokens)
									A							:					Optional[int]              =						seq_len - padding_mask.shape[-1]
									A							:					Union[str, Any]              =						1 - self.feature_extractor.padding_value
									A							:					Any              =						np.pad(__lowerCamelCase	,	((0, 0), (0, difference))	,	"constant"	,	constant_values=__lowerCamelCase )

									A							:					List[Any]              =						audio_values.tolist()
									for i in range(__lowerCamelCase ):
												A							:					Tuple              =						np.asarray(audio_values[i] )[
												    padding_mask[i][None, :] != self.feature_extractor.padding_value
												]
												A							:					Union[str, Any]              =						sliced_audio.reshape(__lowerCamelCase	,	-1 )

									return audio_values




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

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

@dataclass(frozen=_A						)
class 	lowerCamelCase_    ( _A						):






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







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict[str, str]:
									return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}