
"""simple docstring"""







from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


lowerCamelCase__   					=		logging.get_logger(__name__)


# General docstring
lowerCamelCase__   					=		"""MobileNetV1Config"""

# Base docstring
lowerCamelCase__   					=		"""google/mobilenet_v1_1.0_224"""
lowerCamelCase__   					=		[1, 1_024, 7, 7]

# Image classification docstring
lowerCamelCase__   					=		"""google/mobilenet_v1_1.0_224"""
lowerCamelCase__   					=		"""tabby, tabby cat"""


lowerCamelCase__   					=		[
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase=None		):
	__lowerCAmelCase			:						List[str]									=      {}

	if isinstance(_UpperCamelCase       ,  _UpperCamelCase		):
		__lowerCAmelCase			:						Tuple									=      model.mobilenet_va
	else:
		__lowerCAmelCase			:						List[Any]									=      model

	__lowerCAmelCase			:						Any									=      'MobilenetV1/Conv2d_0/'
	__lowerCAmelCase			:						List[str]									=      backbone.conv_stem.convolution.weight
	__lowerCAmelCase			:						Optional[int]									=      backbone.conv_stem.normalization.bias
	__lowerCAmelCase			:						Dict									=      backbone.conv_stem.normalization.weight
	__lowerCAmelCase			:						Optional[int]									=      backbone.conv_stem.normalization.running_mean
	__lowerCAmelCase			:						str									=      backbone.conv_stem.normalization.running_var

	for i in range(13		):
		__lowerCAmelCase			:						Union[str, Any]									=      i + 1
		__lowerCAmelCase			:						List[str]									=      i * 2

		__lowerCAmelCase			:						List[Any]									=      backbone.layer[pt_index]
		__lowerCAmelCase			:						Union[str, Any]									=      F"MobilenetV1/Conv2d_{tf_index}_depthwise/"
		__lowerCAmelCase			:						Any									=      pointer.convolution.weight
		__lowerCAmelCase			:						Tuple									=      pointer.normalization.bias
		__lowerCAmelCase			:						Tuple									=      pointer.normalization.weight
		__lowerCAmelCase			:						List[str]									=      pointer.normalization.running_mean
		__lowerCAmelCase			:						Dict									=      pointer.normalization.running_var

		__lowerCAmelCase			:						int									=      backbone.layer[pt_index + 1]
		__lowerCAmelCase			:						str									=      F"MobilenetV1/Conv2d_{tf_index}_pointwise/"
		__lowerCAmelCase			:						Union[str, Any]									=      pointer.convolution.weight
		__lowerCAmelCase			:						int									=      pointer.normalization.bias
		__lowerCAmelCase			:						Tuple									=      pointer.normalization.weight
		__lowerCAmelCase			:						List[Any]									=      pointer.normalization.running_mean
		__lowerCAmelCase			:						Tuple									=      pointer.normalization.running_var

	if isinstance(_UpperCamelCase       ,  _UpperCamelCase		):
		__lowerCAmelCase			:						List[str]									=      'MobilenetV1/Logits/Conv2d_1c_1x1/'
		__lowerCAmelCase			:						Optional[Any]									=      model.classifier.weight
		__lowerCAmelCase			:						List[str]									=      model.classifier.bias

	return tf_to_pt_map
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		):
	try:
		import numpy as np
		import tensorflow as tf
	except ImportError:
		logger.error(
		    'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
		    'https://www.tensorflow.org/install/ for installation instructions.'		)
		raise

	# Load weights from TF model
	__lowerCAmelCase			:						List[str]									=      tf.train.list_variables(_UpperCamelCase		)
	__lowerCAmelCase			:						Optional[Any]									=      {}
	for name, shape in init_vars:
		logger.info(F"Loading TF weight {name} with shape {shape}"		)
		__lowerCAmelCase			:						Any									=      tf.train.load_variable(_UpperCamelCase       ,  _UpperCamelCase		)
		__lowerCAmelCase			:						Dict									=      array

	# Build TF to PyTorch weights loading map
	__lowerCAmelCase			:						Optional[Any]									=      _build_tf_to_pytorch_map(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		)

	for name, pointer in tf_to_pt_map.items():
		logger.info(F"Importing {name}"		)
		if name not in tf_weights:
			logger.info(F"{name} not in tf pre-trained weights, skipping"		)
			continue

		__lowerCAmelCase			:						Any									=      tf_weights[name]

		if "depthwise_weights" in name:
			logger.info('Transposing depthwise'		)
			__lowerCAmelCase			:						Dict									=      np.transpose(_UpperCamelCase       ,  (2, 3, 0, 1)		)
		elif "weights" in name:
			logger.info('Transposing'		)
			if len(pointer.shape		) == 2:  # copying into linear layer
				__lowerCAmelCase			:						Optional[Any]									=      array.squeeze().transpose()
			else:
				__lowerCAmelCase			:						Any									=      np.transpose(_UpperCamelCase       ,  (3, 2, 0, 1)		)

		if pointer.shape != array.shape:
			raise ValueError(F"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"		)

		logger.info(F"Initialize PyTorch weight {name} {array.shape}"		)
		__lowerCAmelCase			:						str									=      torch.from_numpy(_UpperCamelCase		)

		tf_weights.pop(_UpperCamelCase       ,  _UpperCamelCase		)
		tf_weights.pop(name + '/RMSProp'       ,  _UpperCamelCase		)
		tf_weights.pop(name + '/RMSProp_1'       ,  _UpperCamelCase		)
		tf_weights.pop(name + '/ExponentialMovingAverage'       ,  _UpperCamelCase		)

	logger.info(F"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()		)}"		)
	return model
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase		):
	__lowerCAmelCase			,   __lowerCAmelCase			:						List[str]									=      features.shape[-2:]
	__lowerCAmelCase			,   __lowerCAmelCase			:						Dict									=      conv_layer.stride
	__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      conv_layer.kernel_size

	if in_height % stride_height == 0:
		__lowerCAmelCase			:						Dict									=      max(kernel_height - stride_height       ,  0		)
	else:
		__lowerCAmelCase			:						Optional[int]									=      max(kernel_height - (in_height % stride_height)       ,  0		)

	if in_width % stride_width == 0:
		__lowerCAmelCase			:						Tuple									=      max(kernel_width - stride_width       ,  0		)
	else:
		__lowerCAmelCase			:						Tuple									=      max(kernel_width - (in_width % stride_width)       ,  0		)

	__lowerCAmelCase			:						Optional[Any]									=      pad_along_width // 2
	__lowerCAmelCase			:						int									=      pad_along_width - pad_left
	__lowerCAmelCase			:						str									=      pad_along_height // 2
	__lowerCAmelCase			:						Tuple									=      pad_along_height - pad_top

	__lowerCAmelCase			:						Optional[int]									=      (pad_left, pad_right, pad_top, pad_bottom)
	return nn.functional.pad(_UpperCamelCase       ,  _UpperCamelCase       ,  'constant'       ,  0.0		)




class  A__       (	nn.Module):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = 1       ,		_SCREAMING_SNAKE_CASE = 1       ,		_SCREAMING_SNAKE_CASE = False       ,		_SCREAMING_SNAKE_CASE = True       ,		_SCREAMING_SNAKE_CASE = True       ,		):
			super().__init__()
			__lowerCAmelCase			:						Any									=      config

			if in_channels % groups != 0:
				raise ValueError(f"Input channels ({in_channels}) are not divisible by {groups} groups." )
			if out_channels % groups != 0:
				raise ValueError(f"Output channels ({out_channels}) are not divisible by {groups} groups." )

			__lowerCAmelCase			:						Dict									=      0 if config.tf_padding else int((kernel_size - 1) / 2 )

			__lowerCAmelCase			:						Optional[int]									=      nn.Convad(
			    in_channels=_SCREAMING_SNAKE_CASE       ,		out_channels=_SCREAMING_SNAKE_CASE       ,		kernel_size=_SCREAMING_SNAKE_CASE       ,		stride=_SCREAMING_SNAKE_CASE       ,		padding=_SCREAMING_SNAKE_CASE       ,		groups=_SCREAMING_SNAKE_CASE       ,		bias=_SCREAMING_SNAKE_CASE       ,		padding_mode='zeros'       ,		)

			if use_normalization:
				__lowerCAmelCase			:						Dict									=      nn.BatchNormad(
				    num_features=_SCREAMING_SNAKE_CASE       ,		eps=config.layer_norm_eps       ,		momentum=0.9997       ,		affine=_SCREAMING_SNAKE_CASE       ,		track_running_stats=_SCREAMING_SNAKE_CASE       ,		)
			else:
				__lowerCAmelCase			:						int									=      None

			if use_activation:
				if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE ):
					__lowerCAmelCase			:						List[Any]									=      ACTaFN[use_activation]
				elif isinstance(config.hidden_act       ,		_SCREAMING_SNAKE_CASE ):
					__lowerCAmelCase			:						List[Any]									=      ACTaFN[config.hidden_act]
				else:
					__lowerCAmelCase			:						List[Any]									=      config.hidden_act
			else:
				__lowerCAmelCase			:						Any									=      None







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if self.config.tf_padding:
				__lowerCAmelCase			:						Dict									=      apply_tf_padding(_SCREAMING_SNAKE_CASE       ,		self.convolution )
			__lowerCAmelCase			:						str									=      self.convolution(_SCREAMING_SNAKE_CASE )
			if self.normalization is not None:
				__lowerCAmelCase			:						Union[str, Any]									=      self.normalization(_SCREAMING_SNAKE_CASE )
			if self.activation is not None:
				__lowerCAmelCase			:						Optional[Any]									=      self.activation(_SCREAMING_SNAKE_CASE )
			return features




class  A__       (	_lowerCamelCase):
		A_						:       int				   =	MobileNetVaConfig
		A_						:       Optional[int]				   =	load_tf_weights_in_mobilenet_va
		A_						:       int				   =	'mobilenet_v1'
		A_						:       Dict				   =	'pixel_values'
		A_						:       int				   =	False

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			if isinstance(_SCREAMING_SNAKE_CASE       ,		(nn.Linear, nn.Convad) ):
				module.weight.data.normal_(mean=0.0       ,		std=self.config.initializer_range )
				if module.bias is not None:
					module.bias.data.zero_()
			elif isinstance(_SCREAMING_SNAKE_CASE       ,		nn.BatchNormad ):
				module.bias.data.zero_()
				module.weight.data.fill_(1.0 )



lowerCamelCase__   					=		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 ([`MobileNetV1Config`]): 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.
"""

lowerCamelCase__   					=		R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__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 [`~utils.ModelOutput`] instead of a plain tuple.
"""




@add_start_docstrings(
    'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'       ,     _lowerCamelCase       ,     )
class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = True ):
			super().__init__(_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      config

			__lowerCAmelCase			:						Optional[Any]									=      32
			__lowerCAmelCase			:						Optional[int]									=      max(int(depth * config.depth_multiplier )       ,		config.min_depth )

			__lowerCAmelCase			:						Optional[Any]									=      MobileNetVaConvLayer(
			    _SCREAMING_SNAKE_CASE       ,		in_channels=config.num_channels       ,		out_channels=_SCREAMING_SNAKE_CASE       ,		kernel_size=3       ,		stride=2       ,		)

			__lowerCAmelCase			:						str									=      [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

			__lowerCAmelCase			:						List[str]									=      nn.ModuleList()
			for i in range(13 ):
				__lowerCAmelCase			:						List[str]									=      out_channels

				if strides[i] == 2 or i == 0:
					depth *= 2
					__lowerCAmelCase			:						List[str]									=      max(int(depth * config.depth_multiplier )       ,		config.min_depth )

				self.layer.append(
				    MobileNetVaConvLayer(
				        _SCREAMING_SNAKE_CASE       ,		in_channels=_SCREAMING_SNAKE_CASE       ,		out_channels=_SCREAMING_SNAKE_CASE       ,		kernel_size=3       ,		stride=strides[i]       ,		groups=_SCREAMING_SNAKE_CASE       ,		) )

				self.layer.append(
				    MobileNetVaConvLayer(
				        _SCREAMING_SNAKE_CASE       ,		in_channels=_SCREAMING_SNAKE_CASE       ,		out_channels=_SCREAMING_SNAKE_CASE       ,		kernel_size=1       ,		) )

			__lowerCAmelCase			:						Optional[Any]									=      nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None

			# Initialize weights and apply final processing
			self.post_init()

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE ):
			raise NotImplementedError







		@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE )
		@add_code_sample_docstrings(
		    checkpoint=_CHECKPOINT_FOR_DOC       ,		output_type=_SCREAMING_SNAKE_CASE       ,		config_class=_CONFIG_FOR_DOC       ,		modality='vision'       ,		expected_output=_EXPECTED_OUTPUT_SHAPE       ,		)
		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		):
			__lowerCAmelCase			:						int									=      (
			    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
			)
			__lowerCAmelCase			:						Any									=      return_dict if return_dict is not None else self.config.use_return_dict

			if pixel_values is None:
				raise ValueError('You have to specify pixel_values' )

			__lowerCAmelCase			:						List[str]									=      self.conv_stem(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						List[str]									=      () if output_hidden_states else None

			for i, layer_module in enumerate(self.layer ):
				__lowerCAmelCase			:						Optional[int]									=      layer_module(_SCREAMING_SNAKE_CASE )

				if output_hidden_states:
					__lowerCAmelCase			:						int									=      all_hidden_states + (hidden_states,)

			__lowerCAmelCase			:						int									=      hidden_states

			if self.pooler is not None:
				__lowerCAmelCase			:						Optional[int]									=      torch.flatten(self.pooler(_SCREAMING_SNAKE_CASE )       ,		start_dim=1 )
			else:
				__lowerCAmelCase			:						Optional[Any]									=      None

			if not return_dict:
				return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )

			return BaseModelOutputWithPoolingAndNoAttention(
			    last_hidden_state=_SCREAMING_SNAKE_CASE       ,		pooler_output=_SCREAMING_SNAKE_CASE       ,		hidden_states=_SCREAMING_SNAKE_CASE       ,		)




@add_start_docstrings(
    '\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '       ,     _lowerCamelCase       ,     )
class  A__       (	_lowerCamelCase):

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE ):
			super().__init__(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						Optional[int]									=      config.num_labels
			__lowerCAmelCase			:						Union[str, Any]									=      MobileNetVaModel(_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						int									=      self.mobilenet_va.layer[-1].convolution.out_channels

			# Classifier head
			__lowerCAmelCase			:						Optional[Any]									=      nn.Dropout(config.classifier_dropout_prob       ,		inplace=_SCREAMING_SNAKE_CASE )
			__lowerCAmelCase			:						Any									=      nn.Linear(_SCREAMING_SNAKE_CASE       ,		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(_SCREAMING_SNAKE_CASE )
		@add_code_sample_docstrings(
		    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,		output_type=_SCREAMING_SNAKE_CASE       ,		config_class=_CONFIG_FOR_DOC       ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,		)
		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		_SCREAMING_SNAKE_CASE = None       ,		):
			__lowerCAmelCase			:						Optional[Any]									=      return_dict if return_dict is not None else self.config.use_return_dict

			__lowerCAmelCase			:						int									=      self.mobilenet_va(_SCREAMING_SNAKE_CASE       ,		output_hidden_states=_SCREAMING_SNAKE_CASE       ,		return_dict=_SCREAMING_SNAKE_CASE )

			__lowerCAmelCase			:						str									=      outputs.pooler_output if return_dict else outputs[1]

			__lowerCAmelCase			:						Dict									=      self.classifier(self.dropout(_SCREAMING_SNAKE_CASE ) )

			__lowerCAmelCase			:						Optional[int]									=      None
			if labels is not None:
				if self.config.problem_type is None:
					if self.num_labels == 1:
						__lowerCAmelCase			:						Optional[int]									=      'regression'
					elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
						__lowerCAmelCase			:						Optional[int]									=      'single_label_classification'
					else:
						__lowerCAmelCase			:						int									=      'multi_label_classification'

				if self.config.problem_type == "regression":
					__lowerCAmelCase			:						Tuple									=      MSELoss()
					if self.num_labels == 1:
						__lowerCAmelCase			:						Tuple									=      loss_fct(logits.squeeze()       ,		labels.squeeze() )
					else:
						__lowerCAmelCase			:						Dict									=      loss_fct(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
				elif self.config.problem_type == "single_label_classification":
					__lowerCAmelCase			:						Tuple									=      CrossEntropyLoss()
					__lowerCAmelCase			:						Dict									=      loss_fct(logits.view(-1       ,		self.num_labels )       ,		labels.view(-1 ) )
				elif self.config.problem_type == "multi_label_classification":
					__lowerCAmelCase			:						Tuple									=      BCEWithLogitsLoss()
					__lowerCAmelCase			:						List[Any]									=      loss_fct(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )

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

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

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

UpperCAmelCase__      :    Dict    							= {
    'MIT/ast-finetuned-audioset-10-10-0.4593': (
        'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
    ),
}



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[int]					       =   '''audio-spectrogram-transformer'''



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=7_68   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=12   ,       SCREAMING_SNAKE_CASE__=30_72   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=1E-12   ,       SCREAMING_SNAKE_CASE__=16   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=10   ,       SCREAMING_SNAKE_CASE__=10   ,       SCREAMING_SNAKE_CASE__=10_24   ,       SCREAMING_SNAKE_CASE__=1_28   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		Tuple:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       str           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       int           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       Tuple           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       Any           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       int           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       int           =   layer_norm_eps
											SCREAMING_SNAKE_CASE__						:       Dict           =   patch_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   qkv_bias
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   frequency_stride
											SCREAMING_SNAKE_CASE__						:       Any           =   time_stride
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   max_length
											SCREAMING_SNAKE_CASE__						:       Any           =   num_mel_bins




