





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 PaddingStrategy, TensorType, logging


UpperCamelCase__   : List[str]					  = logging.get_logger(__name__)







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Union[str, Any]			    =       ['input_features', 'attention_mask']





					def __init__(						self    :  int ,					__lowercase    :  Union[str, Any]=80 ,					__lowercase    :  Tuple=1_60_00 ,					__lowercase    :  str=80 ,					__lowercase    :  Tuple=0.0 ,					__lowercase    :  Union[str, Any]=True ,					__lowercase    :  List[str]=True ,					__lowercase    :  Any=True ,					**__lowercase    :  Any ,					):






						'''simple docstring'''







						super().__init__(feature_size=__UpperCamelCase ,					sampling_rate=__UpperCamelCase ,					padding_value=__UpperCamelCase ,					**__UpperCamelCase		)
						UpperCAmelCase_								=     num_mel_bins
						UpperCAmelCase_								=     do_ceptral_normalize
						UpperCAmelCase_								=     normalize_means
						UpperCAmelCase_								=     normalize_vars
						UpperCAmelCase_								=     True





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  np.ndarray ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
						UpperCAmelCase_								=     torch.from_numpy(__UpperCamelCase		).unsqueeze(0		)
						UpperCAmelCase_								=     ta_kaldi.fbank(__UpperCamelCase ,					num_mel_bins=self.num_mel_bins ,					sample_frequency=self.sampling_rate		)
						return features.numpy()





					@staticmethod
					def        SCREAMING_SNAKE_CASE							(						__lowercase    :  np.ndarray ,					__lowercase    :  int ,					__lowercase    :  Optional[bool] = True ,					__lowercase    :  Optional[bool] = True ,					__lowercase    :  float = 0.0 ,					):






						'''simple docstring'''







						if normalize_means:
							UpperCAmelCase_								=     x[:input_length].mean(axis=0		)
							UpperCAmelCase_								=     np.subtract(__UpperCamelCase ,					__UpperCamelCase		)
						if normalize_vars:
							UpperCAmelCase_								=     x[:input_length].std(axis=0		)
							UpperCAmelCase_								=     np.divide(__UpperCamelCase ,					__UpperCamelCase		)

						if input_length < x.shape[0]:
							UpperCAmelCase_								=     padding_value

						# make sure array is in float32
						UpperCAmelCase_								=     x.astype(np.floataa		)

						return x





					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  List[np.ndarray] ,					__lowercase    :  Optional[np.ndarray] = None		):






						'''simple docstring'''







						UpperCAmelCase_								=     attention_mask.sum(-1		) if attention_mask is not None else [x.shape[0] for x in input_features]
						return [
						    self.utterance_cmvn(__UpperCamelCase ,					__UpperCamelCase ,					self.normalize_means ,					self.normalize_vars ,					self.padding_value		)
						    for x, n in zip(__UpperCamelCase ,					__UpperCamelCase		)
						]





					def __call__(						self    :  Dict ,					__lowercase    :  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,					__lowercase    :  Union[bool, str, PaddingStrategy] = False ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  bool = False ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[Union[str, TensorType]] = None ,					__lowercase    :  Optional[int] = None ,					__lowercase    :  Optional[bool] = None ,					**__lowercase    :  List[Any] ,					):






						'''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."""		)

						UpperCAmelCase_								=     isinstance(__UpperCamelCase ,					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}"""		)
						UpperCAmelCase_								=     is_batched_numpy or (
						    isinstance(__UpperCamelCase ,					(list, tuple)		) and (isinstance(raw_speech[0] ,					(np.ndarray, tuple, list)		))
						)

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

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

						# extract fbank features
						UpperCAmelCase_								=     [self._extract_fbank_features(__UpperCamelCase		) for waveform in raw_speech]

						# convert into correct format for padding
						UpperCAmelCase_								=     BatchFeature({"""input_features""": features}		)

						UpperCAmelCase_								=     self.pad(
						    __UpperCamelCase ,					padding=__UpperCamelCase ,					max_length=__UpperCamelCase ,					truncation=__UpperCamelCase ,					pad_to_multiple_of=__UpperCamelCase ,					return_attention_mask=__UpperCamelCase ,					**__UpperCamelCase ,					)

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

						UpperCAmelCase_								=     padded_inputs.get("""attention_mask"""		)
						if attention_mask is not None:
							UpperCAmelCase_								=     [np.asarray(__UpperCamelCase ,					dtype=np.intaa		) for array in attention_mask]

						# Utterance-level cepstral mean and variance normalization
						if self.do_ceptral_normalize:
							UpperCAmelCase_								=     (
							    np.array(__UpperCamelCase ,					dtype=np.intaa		)
							    if self._get_padding_strategies(__UpperCamelCase ,					max_length=__UpperCamelCase		) is not PaddingStrategy.DO_NOT_PAD
							    else None
							)
							UpperCAmelCase_								=     self.normalize(
							    padded_inputs["""input_features"""] ,					attention_mask=__UpperCamelCase		)

						if return_tensors is not None:
							UpperCAmelCase_								=     padded_inputs.convert_to_tensors(__UpperCamelCase		)

						return padded_inputs









from torch import nn







class   _UpperCamelCase  (       nn.Module  ):







					'''simple docstring'''

					def __init__(						self    :  Dict ,					__lowercase    :  List[str] ,					__lowercase    :  Dict		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     class_size
						UpperCAmelCase_								=     embed_size
						# self.mlp1 = nn.Linear(embed_size, embed_size)
						# self.mlp2 = (nn.Linear(embed_size, class_size))
						UpperCAmelCase_								=     nn.Linear(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.mlp(__lowercase		)
						return logits


