




from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCamelCase__              =						logging.get_logger(__name__)


class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  = ["""input_values""", """attention_mask"""]

					def __init__(				self ,       __lowercase=1 ,       __lowercase=16_000 ,       __lowercase=128 ,       __lowercase=1_024 ,       __lowercase=0.0 ,       __lowercase=True ,       __lowercase=-4.2_67_73_93 ,       __lowercase=4.5_68_99_74 ,       __lowercase=False ,       **__lowercase ,       ):







						'''simple docstring'''




						super().__init__(feature_size=__lowercase ,       sampling_rate=__lowercase ,       padding_value=__lowercase ,       **__lowercase					)
						lowerCamelCase_							:		Optional[int]							   =							num_mel_bins
						lowerCamelCase_							:		Optional[int]							   =							max_length
						lowerCamelCase_							:		List[str]							   =							do_normalize
						lowerCamelCase_							:		Any							   =							mean
						lowerCamelCase_							:		Tuple							   =							std
						lowerCamelCase_							:		Optional[int]							   =							return_attention_mask

					def 	lowercase_							(				self ,       __lowercase ,       __lowercase ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							torch.from_numpy(__lowercase					).unsqueeze(0					)
						lowerCamelCase_							:		Tuple							   =							ta_kaldi.fbank(
						    __lowercase ,       htk_compat=__lowercase ,       sample_frequency=self.sampling_rate ,       use_energy=__lowercase ,       window_type='hanning' ,       num_mel_bins=self.num_mel_bins ,       dither=0.0 ,       frame_shift=10 ,       )

						lowerCamelCase_							:		str							   =							fbank.shape[0]
						lowerCamelCase_							:		Union[str, Any]							   =							max_length - n_frames

						# pad or truncate, depending on difference
						if difference > 0:
							lowerCamelCase_							:		Any							   =							torch.nn.ZeroPadad((0, 0, 0, difference)					)
							lowerCamelCase_							:		Optional[int]							   =							pad_module(__lowercase					)
						elif difference < 0:
							lowerCamelCase_							:		Optional[Any]							   =							fbank[0:max_length, :]

						lowerCamelCase_							:		int							   =							fbank.numpy()

						return fbank

					def 	lowercase_							(				self ,       __lowercase					):







						'''simple docstring'''




						return (input_values - (self.mean)) / (self.std * 2)





					def __call__(				self ,       __lowercase ,       __lowercase = None ,       __lowercase = None ,       **__lowercase ,       ):







						'''simple docstring'''




						if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
								raise ValueError(
								    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
								    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
								    f" {self.sampling_rate} and not {sampling_rate}."					)
						else:
							logger.warning(
							    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
							    'Failing to do so can result in silent errors that might be hard to debug.'					)

						lowerCamelCase_							:		Dict							   =							isinstance(__lowercase ,       np.ndarray					) and len(raw_speech.shape					) > 1
						if is_batched_numpy and len(raw_speech.shape					) > 2:
							raise ValueError(f"Only mono-channel audio is supported for input to {self}"					)
						lowerCamelCase_							:		List[str]							   =							is_batched_numpy or (
						    isinstance(__lowercase ,       (list, tuple)					) and (isinstance(raw_speech[0] ,       (np.ndarray, tuple, list)					))
						)

						if is_batched:
							lowerCamelCase_							:		List[str]							   =							[np.asarray(__lowercase ,       dtype=np.floataa					) for speech in raw_speech]
						elif not is_batched and not isinstance(__lowercase ,       np.ndarray					):
							lowerCamelCase_							:		Any							   =							np.asarray(__lowercase ,       dtype=np.floataa					)
						elif isinstance(__lowercase ,       np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
							lowerCamelCase_							:		Tuple							   =							raw_speech.astype(np.floataa					)

						# always return batch
						if not is_batched:
							lowerCamelCase_							:		int							   =							[raw_speech]

						# extract fbank features and pad/truncate to max_length
						lowerCamelCase_							:		str							   =							[self._extract_fbank_features(__lowercase ,       max_length=self.max_length					) for waveform in raw_speech]

						# convert into BatchFeature
						lowerCamelCase_							:		Dict							   =							BatchFeature({'input_values': features}					)

						# make sure list is in array format
						lowerCamelCase_							:		List[str]							   =							padded_inputs.get('input_values'					)
						if isinstance(input_values[0] ,       __lowercase					):
							lowerCamelCase_							:		int							   =							[np.asarray(__lowercase ,       dtype=np.floataa					) for feature in input_values]

						# normalization
						if self.do_normalize:
							lowerCamelCase_							:		str							   =							[self.normalize(__lowercase					) for feature in input_values]

						if return_tensors is not None:
							lowerCamelCase_							:		Dict							   =							padded_inputs.convert_to_tensors(__lowercase					)

						return padded_inputs






from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__              =						[
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCamelCase__              =						get_logger(__name__)


UpperCamelCase__              =						{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def 					snake_case					(							A			)							->							Optional[TaskTemplate]:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							task_template_dict.get('task'			)
	if task_name is None:
		logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(A			)}"			)
		return None
	lowerCamelCase_							:		Any							   =							NAMEaTEMPLATE.get(A			)
	return template.from_dict(A			)
