



from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


A_		:     Optional[Any]        =     logging.get_logger(__name__)

# General docstring
A_		:     Optional[int]        =     '''RegNetConfig'''

# Base docstring
A_		:     Union[str, Any]        =     '''facebook/regnet-y-040'''
A_		:     int        =     [1, 10_88, 7, 7]

# Image classification docstring
A_		:     Any        =     '''facebook/regnet-y-040'''
A_		:     Dict        =     '''tabby, tabby cat'''

A_		:     Optional[Any]        =     [
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



class 		_lowercase		(       nn.Module	):

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int = 3							,   __lowerCAmelCase			:       int = 1							,   __lowerCAmelCase			:       int = 1							,   __lowerCAmelCase			:       Optional[str] = "relu"							,   )       ->				str:


    """simple docstring"""






    super().__init__()
    a						=		nn.Convad(
        __lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=__lowerCAmelCase							,   stride=__lowerCAmelCase							,   padding=kernel_size // 2							,   groups=__lowerCAmelCase							,   bias=__lowerCAmelCase							,   )
    a						=		nn.BatchNormad(__lowerCAmelCase							)
    a						=		ACTaFN[activation] if activation is not None else nn.Identity()




   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       List[str]							)       ->				Dict:


    """simple docstring"""






    a						=		self.convolution(__lowerCAmelCase							)
    a						=		self.normalization(__lowerCAmelCase							)
    a						=		self.activation(__lowerCAmelCase							)
    return hidden_state



class 		_lowercase		(       nn.Module	):

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       RegNetConfig							)       ->				Dict:


    """simple docstring"""






    super().__init__()
    a						=		RegNetConvLayer(
        config.num_channels							,   config.embedding_size							,   kernel_size=3							,   stride=2							,   activation=config.hidden_act							)
    a						=		config.num_channels




   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       Dict							)       ->				Tuple:


    """simple docstring"""






    a						=		pixel_values.shape[1]
    if num_channels != self.num_channels:
     raise ValueError(
         "Make sure that the channel dimension of the pixel values match with the one set in the configuration."							)
    a						=		self.embedder(__lowerCAmelCase							)
    return hidden_state



class 		_lowercase		(       nn.Module	):

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int = 2							)       ->				Optional[int]:


    """simple docstring"""






    super().__init__()
    a						=		nn.Convad(__lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=1							,   stride=__lowerCAmelCase							,   bias=__lowerCAmelCase							)
    a						=		nn.BatchNormad(__lowerCAmelCase							)




   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       Tensor							)       ->				Tensor:


    """simple docstring"""






    a						=		self.convolution(__lowerCAmelCase							)
    a						=		self.normalization(__lowerCAmelCase							)
    return hidden_state



class 		_lowercase		(       nn.Module	):

   def __init__(      self			:       Any							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							)       ->				List[Any]:


    """simple docstring"""






    super().__init__()

    a						=		nn.AdaptiveAvgPoolad((1, 1)							)
    a						=		nn.Sequential(
        nn.Convad(__lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=1							)							,   nn.ReLU()							,   nn.Convad(__lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=1							)							,   nn.Sigmoid()							,   )




   def 		A							(      self			:       str							,   __lowerCAmelCase			:       Any							)       ->				str:


    """simple docstring"""






    a						=		self.pooler(__lowerCAmelCase							)
    a						=		self.attention(__lowerCAmelCase							)
    a						=		hidden_state * attention
    return hidden_state



class 		_lowercase		(       nn.Module	):

   def __init__(      self			:       Optional[int]							,   __lowerCAmelCase			:       RegNetConfig							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int = 1							)       ->				Any:


    """simple docstring"""






    super().__init__()
    a						=		in_channels != out_channels or stride != 1
    a						=		max(1							,   out_channels // config.groups_width							)
    a						=		(
        RegNetShortCut(__lowerCAmelCase							,   __lowerCAmelCase							,   stride=__lowerCAmelCase							) if should_apply_shortcut else nn.Identity()
    )
    a						=		nn.Sequential(
        RegNetConvLayer(__lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=1							,   activation=config.hidden_act							)							,   RegNetConvLayer(__lowerCAmelCase							,   __lowerCAmelCase							,   stride=__lowerCAmelCase							,   groups=__lowerCAmelCase							,   activation=config.hidden_act							)							,   RegNetConvLayer(__lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=1							,   activation=__lowerCAmelCase							)							,   )
    a						=		ACTaFN[config.hidden_act]




   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       List[Any]							)       ->				int:


    """simple docstring"""






    a						=		hidden_state
    a						=		self.layer(__lowerCAmelCase							)
    a						=		self.shortcut(__lowerCAmelCase							)
    hidden_state += residual
    a						=		self.activation(__lowerCAmelCase							)
    return hidden_state



class 		_lowercase		(       nn.Module	):

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       RegNetConfig							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int = 1							)       ->				Optional[Any]:


    """simple docstring"""






    super().__init__()
    a						=		in_channels != out_channels or stride != 1
    a						=		max(1							,   out_channels // config.groups_width							)
    a						=		(
        RegNetShortCut(__lowerCAmelCase							,   __lowerCAmelCase							,   stride=__lowerCAmelCase							) if should_apply_shortcut else nn.Identity()
    )
    a						=		nn.Sequential(
        RegNetConvLayer(__lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=1							,   activation=config.hidden_act							)							,   RegNetConvLayer(__lowerCAmelCase							,   __lowerCAmelCase							,   stride=__lowerCAmelCase							,   groups=__lowerCAmelCase							,   activation=config.hidden_act							)							,   RegNetSELayer(__lowerCAmelCase							,   reduced_channels=int(round(in_channels / 4							)							)							)							,   RegNetConvLayer(__lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=1							,   activation=__lowerCAmelCase							)							,   )
    a						=		ACTaFN[config.hidden_act]




   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       List[str]							)       ->				Optional[int]:


    """simple docstring"""






    a						=		hidden_state
    a						=		self.layer(__lowerCAmelCase							)
    a						=		self.shortcut(__lowerCAmelCase							)
    hidden_state += residual
    a						=		self.activation(__lowerCAmelCase							)
    return hidden_state



class 		_lowercase		(       nn.Module	):

   def __init__(      self			:       Any							,   __lowerCAmelCase			:       RegNetConfig							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int = 2							,   __lowerCAmelCase			:       int = 2							,   )       ->				int:


    """simple docstring"""






    super().__init__()

    a						=		RegNetXLayer if config.layer_type == "x" else RegNetYLayer

    a						=		nn.Sequential(
        # downsampling is done in the first layer with stride of 2
        layer(
            __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   stride=__lowerCAmelCase							,   )							,   *[layer(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							) for _ in range(depth - 1							)]							,   )




   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       Tuple							)       ->				List[Any]:


    """simple docstring"""






    a						=		self.layers(__lowerCAmelCase							)
    return hidden_state



class 		_lowercase		(       nn.Module	):

   def __init__(      self			:       Tuple							,   __lowerCAmelCase			:       RegNetConfig							)       ->				str:


    """simple docstring"""






    super().__init__()
    a						=		nn.ModuleList([]							)
    # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
    self.stages.append(
        RegNetStage(
            __lowerCAmelCase							,   config.embedding_size							,   config.hidden_sizes[0]							,   stride=2 if config.downsample_in_first_stage else 1							,   depth=config.depths[0]							,   )							)
    a						=		zip(config.hidden_sizes							,   config.hidden_sizes[1:]							)
    for (in_channels, out_channels), depth in zip(__lowerCAmelCase							,   config.depths[1:]							):
     self.stages.append(RegNetStage(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   depth=__lowerCAmelCase							)							)




   def 		A							(      self			:       int							,   __lowerCAmelCase			:       Tensor							,   __lowerCAmelCase			:       bool = False							,   __lowerCAmelCase			:       bool = True							)       ->				BaseModelOutputWithNoAttention:


    """simple docstring"""






    a						=		() if output_hidden_states else None

    for stage_module in self.stages:
     if output_hidden_states:
      a						=		hidden_states + (hidden_state,)

     a						=		stage_module(__lowerCAmelCase							)

    if output_hidden_states:
     a						=		hidden_states + (hidden_state,)

    if not return_dict:
     return tuple(v for v in [hidden_state, hidden_states] if v is not None							)

    return BaseModelOutputWithNoAttention(last_hidden_state=__lowerCAmelCase							,   hidden_states=__lowerCAmelCase							)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			RegNetConfig
   _UpperCAmelCase     	=			'''regnet'''
   _UpperCAmelCase     	=			'''pixel_values'''
   _UpperCAmelCase     	=			True

   def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[int]							)       ->				Tuple:


    """simple docstring"""






    if isinstance(__lowerCAmelCase							,   nn.Convad							):
     nn.init.kaiming_normal_(module.weight							,   mode="fan_out"							,   nonlinearity="relu"							)
    elif isinstance(__lowerCAmelCase							,   (nn.BatchNormad, nn.GroupNorm)							):
     nn.init.constant_(module.weight							,   1							)
     nn.init.constant_(module.bias							,   0							)




   def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       str=False							)       ->				Tuple:


    """simple docstring"""






    if isinstance(__lowerCAmelCase							,   __lowerCAmelCase							):
     a						=		value

A_		:     Dict        =     r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

A_		:     str        =     r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''



@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.''',   UpperCAmelCase__,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 		_lowercase		(       UpperCAmelCase__	):

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       int							)       ->				Optional[int]:


    """simple docstring"""






    super().__init__(__lowerCAmelCase							)
    a						=		config
    a						=		RegNetEmbeddings(__lowerCAmelCase							)
    a						=		RegNetEncoder(__lowerCAmelCase							)
    a						=		nn.AdaptiveAvgPoolad((1, 1)							)
    # Initialize weights and apply final processing
    self.post_init()




   @add_start_docstrings_to_model_forward(__lowerCAmelCase							)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC							,   output_type=__lowerCAmelCase							,   config_class=_CONFIG_FOR_DOC							,   modality="vision"							,   expected_output=_EXPECTED_OUTPUT_SHAPE							,   )
   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       Tensor							,   __lowerCAmelCase			:       Optional[bool] = None							,   __lowerCAmelCase			:       Optional[bool] = None							)       ->				BaseModelOutputWithPoolingAndNoAttention:


    """simple docstring"""






    a						=		(
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    a						=		return_dict if return_dict is not None else self.config.use_return_dict

    a						=		self.embedder(__lowerCAmelCase							)

    a						=		self.encoder(
        __lowerCAmelCase							,   output_hidden_states=__lowerCAmelCase							,   return_dict=__lowerCAmelCase							)

    a						=		encoder_outputs[0]

    a						=		self.pooler(__lowerCAmelCase							)

    if not return_dict:
     return (last_hidden_state, pooled_output) + encoder_outputs[1:]

    return BaseModelOutputWithPoolingAndNoAttention(
        last_hidden_state=__lowerCAmelCase							,   pooler_output=__lowerCAmelCase							,   hidden_states=encoder_outputs.hidden_states							,   )



@add_start_docstrings(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    ''',   UpperCAmelCase__,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 		_lowercase		(       UpperCAmelCase__	):

   def __init__(      self			:       Tuple							,   __lowerCAmelCase			:       Dict							)       ->				Union[str, Any]:


    """simple docstring"""






    super().__init__(__lowerCAmelCase							)
    a						=		config.num_labels
    a						=		RegNetModel(__lowerCAmelCase							)
    # classification head
    a						=		nn.Sequential(
        nn.Flatten()							,   nn.Linear(config.hidden_sizes[-1]							,   config.num_labels							) if config.num_labels > 0 else nn.Identity()							,   )
    # initialize weights and apply final processing
    self.post_init()




   @add_start_docstrings_to_model_forward(__lowerCAmelCase							)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT							,   output_type=__lowerCAmelCase							,   config_class=_CONFIG_FOR_DOC							,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,   )
   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       Optional[torch.FloatTensor] = None							,   __lowerCAmelCase			:       Optional[torch.LongTensor] = None							,   __lowerCAmelCase			:       Optional[bool] = None							,   __lowerCAmelCase			:       Optional[bool] = None							,   )       ->				ImageClassifierOutputWithNoAttention:


    """simple docstring"""






    a						=		return_dict if return_dict is not None else self.config.use_return_dict

    a						=		self.regnet(__lowerCAmelCase							,   output_hidden_states=__lowerCAmelCase							,   return_dict=__lowerCAmelCase							)

    a						=		outputs.pooler_output if return_dict else outputs[1]

    a						=		self.classifier(__lowerCAmelCase							)

    a						=		None

    if labels is not None:
     if self.config.problem_type is None:
      if self.num_labels == 1:
       a						=		"regression"
      elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
       a						=		"single_label_classification"
      else:
       a						=		"multi_label_classification"
     if self.config.problem_type == "regression":
      a						=		MSELoss()
      if self.num_labels == 1:
       a						=		loss_fct(logits.squeeze()							,   labels.squeeze()							)
      else:
       a						=		loss_fct(__lowerCAmelCase							,   __lowerCAmelCase							)
     elif self.config.problem_type == "single_label_classification":
      a						=		CrossEntropyLoss()
      a						=		loss_fct(logits.view(-1							,   self.num_labels							)							,   labels.view(-1							)							)
     elif self.config.problem_type == "multi_label_classification":
      a						=		BCEWithLogitsLoss()
      a						=		loss_fct(__lowerCAmelCase							,   __lowerCAmelCase							)

    if not return_dict:
     a						=		(logits,) + outputs[2:]
     return (loss,) + output if loss is not None else output

    return ImageClassifierOutputWithNoAttention(loss=__lowerCAmelCase							,   logits=__lowerCAmelCase							,   hidden_states=outputs.hidden_states							)






from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


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



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			['''audio_values''', '''audio_mask''']

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       Dict=2048							,   __lowerCAmelCase			:       List[Any]=1							,   __lowerCAmelCase			:       Dict=[16, 16]							,   __lowerCAmelCase			:       str=128							,   __lowerCAmelCase			:       Optional[int]=4_4100							,   __lowerCAmelCase			:       int=86							,   __lowerCAmelCase			:       Optional[Any]=2048							,   __lowerCAmelCase			:       str=0.0							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				Union[str, Any]:


    """simple docstring"""






    super().__init__(
        feature_size=__lowerCAmelCase							,   sampling_rate=__lowerCAmelCase							,   padding_value=__lowerCAmelCase							,   **__lowerCAmelCase							,   )

    a						=		spectrogram_length
    a						=		num_channels
    a						=		patch_size
    a						=		feature_size // self.patch_size[1]
    a						=		n_fft
    a						=		sampling_rate // hop_length_to_sampling_rate
    a						=		sampling_rate
    a						=		padding_value
    a						=		mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2							,   num_mel_filters=__lowerCAmelCase							,   min_frequency=0.0							,   max_frequency=2_2_0_5_0.0							,   sampling_rate=__lowerCAmelCase							,   norm="slaney"							,   mel_scale="slaney"							,   ).T

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       np.array							)       ->				np.ndarray:


    """simple docstring"""






    a						=		spectrogram(
        __lowerCAmelCase							,   window_function(self.n_fft							,   "hann"							)							,   frame_length=self.n_fft							,   hop_length=self.hop_length							,   power=2.0							,   mel_filters=self.mel_filters.T							,   log_mel="dB"							,   db_range=8_0.0							,   )
    a						=		log_spec[:, :-1]
    a						=		log_spec - 2_0.0
    a						=		np.clip(log_spec / 4_0.0							,   -2.0							,   0.0							) + 1.0
    return log_spec




   def __call__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,   __lowerCAmelCase			:       Optional[Union[str, TensorType]] = None							,   __lowerCAmelCase			:       Optional[bool] = True							,   __lowerCAmelCase			:       Optional[int] = None							,   __lowerCAmelCase			:       bool = False							,   __lowerCAmelCase			:       bool = False							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				BatchFeature:


    """simple docstring"""






    if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
      raise ValueError(
          "This feature extractor is set to support sampling rate"
          f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
          f""" with {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."							)

    a						=		isinstance(__lowerCAmelCase							,   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}"""							)
    a						=		is_batched_numpy or (
        isinstance(__lowerCAmelCase							,   (list, tuple)							) and (isinstance(raw_speech[0]							,   (np.ndarray, tuple, list)							))
    )
    if is_batched:
     a						=		[np.asarray([speech]							,   dtype=np.floataa							).T for speech in raw_speech]
    elif not is_batched and not isinstance(__lowerCAmelCase							,   np.ndarray							):
     a						=		np.asarray(__lowerCAmelCase							,   dtype=np.floataa							)
    elif isinstance(__lowerCAmelCase							,   np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
     a						=		raw_speech.astype(np.floataa							)
    # always return batch
    if not is_batched:
     a						=		[np.asarray([raw_speech]							).T]

    # Convert audio signals to log mel spectrograms, truncate by time axis
    a						=		[
        self._np_extract_fbank_features(waveform.squeeze()							).T[: self.spectrogram_length] for waveform in raw_speech
    ]
    if isinstance(audio_features[0]							,   __lowerCAmelCase							):
     a						=		[np.asarray(__lowerCAmelCase							,   dtype=np.floataa							) for feature in audio_features]

    # Create audio attention mask
    a						=		max(
        [ceil(feature.shape[0] / self.patch_size[0]							) * self.freq_len for feature in audio_features]							)  # The maximum number of audio patches in a batch
    if return_attention_mask:
     a						=		[
         (ceil(feature.shape[0] / self.patch_size[0]							) * self.freq_len) * [1]
         + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]							) * self.freq_len) * [0]
         for feature in audio_features
     ]
     a						=		np.array(__lowerCAmelCase							).astype(np.floataa							)

    # convert into correct format for padding
    a						=		max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
    a						=		np.ones([len(__lowerCAmelCase							), 1, max_time_len, self.feature_size]							).astype(np.floataa							)
    a						=		padded_audio_features * self.padding_value
    for i in range(len(__lowerCAmelCase							)							):
     a						=		audio_features[i]
     a						=		feature

    # return as BatchFeature
    if return_attention_mask:
     a						=		{"audio_values": padded_audio_features, "audio_mask": audio_mask}
    else:
     a						=		{"audio_values": padded_audio_features}

    a						=		BatchFeature(data=__lowerCAmelCase							,   tensor_type=__lowerCAmelCase							)
    return encoded_inputs

