
'''simple docstring'''

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__				:	int					=						logging.get_logger(__name__)






class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :str				       =       ['input_values', 'attention_mask']





	def __init__(   self     :    Optional[Any] ,							__magic_name__     :    str=1 ,							__magic_name__     :    int=16000 ,							__magic_name__     :    Optional[int]=128 ,							__magic_name__     :    Tuple=1024 ,							__magic_name__     :    Optional[int]=0.0 ,							__magic_name__     :    Tuple=True ,							__magic_name__     :    Tuple=-4.267_7393 ,							__magic_name__     :    int=4.568_9974 ,							__magic_name__     :    Dict=False ,							**__magic_name__     :    int ,							):


				"""simple docstring"""

				super().__init__(feature_size=__magic_name__ ,							sampling_rate=__magic_name__ ,							padding_value=__magic_name__ ,							**__magic_name__							)
				lowerCAmelCase__									=							num_mel_bins
				lowerCAmelCase__									=							max_length
				lowerCAmelCase__									=							do_normalize
				lowerCAmelCase__									=							mean
				lowerCAmelCase__									=							std
				lowerCAmelCase__									=							return_attention_mask





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple ,							__magic_name__     :    np.ndarray ,							__magic_name__     :    int ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							torch.from_numpy(__magic_name__							).unsqueeze(0							)
				lowerCAmelCase__									=							ta_kaldi.fbank(
				    __magic_name__ ,							htk_compat=__magic_name__ ,							sample_frequency=self.sampling_rate ,							use_energy=__magic_name__ ,							window_type="hanning" ,							num_mel_bins=self.num_mel_bins ,							dither=0.0 ,							frame_shift=10 ,							)

				lowerCAmelCase__									=							fbank.shape[0]
				lowerCAmelCase__									=							max_length - n_frames

				# pad or truncate, depending on difference
				if difference > 0:
							lowerCAmelCase__									=							torch.nn.ZeroPadad((0, 0, 0, difference)							)
							lowerCAmelCase__									=							pad_module(__magic_name__							)
				elif difference < 0:
							lowerCAmelCase__									=							fbank[0:max_length, :]

				lowerCAmelCase__									=							fbank.numpy()

				return fbank





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							__magic_name__     :    np.ndarray							):


				"""simple docstring"""

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







	def __call__(   self     :    str ,							__magic_name__     :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,							__magic_name__     :    Optional[int] = None ,							__magic_name__     :    Optional[Union[str, TensorType]] = None ,							**__magic_name__     :    Tuple ,							):


				"""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__									=							isinstance(__magic_name__ ,							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__									=							is_batched_numpy or (
				    isinstance(__magic_name__ ,							(list, tuple)							) and (isinstance(raw_speech[0] ,							(np.ndarray, tuple, list)							))
				)

				if is_batched:
							lowerCAmelCase__									=							[np.asarray(__magic_name__ ,							dtype=np.floataa							) for speech in raw_speech]
				elif not is_batched and not isinstance(__magic_name__ ,							np.ndarray							):
							lowerCAmelCase__									=							np.asarray(__magic_name__ ,							dtype=np.floataa							)
				elif isinstance(__magic_name__ ,							np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
							lowerCAmelCase__									=							raw_speech.astype(np.floataa							)

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

				# extract fbank features and pad/truncate to max_length
				lowerCAmelCase__									=							[self._extract_fbank_features(__magic_name__ ,							max_length=self.max_length							) for waveform in raw_speech]

				# convert into BatchFeature
				lowerCAmelCase__									=							BatchFeature({"input_values": features}							)

				# make sure list is in array format
				lowerCAmelCase__									=							padded_inputs.get("input_values"							)
				if isinstance(input_values[0] ,							__magic_name__							):
							lowerCAmelCase__									=							[np.asarray(__magic_name__ ,							dtype=np.floataa							) for feature in input_values]

				# normalization
				if self.do_normalize:
							lowerCAmelCase__									=							[self.normalize(__magic_name__							) for feature in input_values]

				if return_tensors is not None:
							lowerCAmelCase__									=							padded_inputs.convert_to_tensors(__magic_name__							)

				return padded_inputs







'''simple docstring'''

UpperCAmelCase__				:	List[str]					=						"\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

UpperCAmelCase__				:	Any					=						[{"type": "code", "content": INSTALL_CONTENT}]
UpperCAmelCase__				:	Union[str, Any]					=						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





