


from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


__UpperCamelCase      						=    logging.get_logger(__name__)

# General docstring
__UpperCamelCase      						=    'ResNetConfig'

# Base docstring
__UpperCamelCase      						=    'microsoft/resnet-50'
__UpperCamelCase      						=    [1, 2_0_4_8, 7, 7]

# Image classification docstring
__UpperCamelCase      						=    'microsoft/resnet-50'
__UpperCamelCase      						=    'tiger cat'

__UpperCamelCase      						=    [
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







class     lowerCamelCase__		(							tf.keras.layers.Layer ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case = 3 ,  snake_case = 1 ,  snake_case = "relu" ,  **snake_case       ):

						'''simple docstring'''


						super().__init__(**_UpperCAmelCase       )
						UpperCamelCase__   =    kernel_size // 2
						UpperCamelCase__   =    tf.keras.layers.ConvaD(
						    _UpperCAmelCase ,  kernel_size=_UpperCAmelCase ,  strides=_UpperCAmelCase ,  padding="valid" ,  use_bias=_UpperCAmelCase ,  name="convolution"       )
						# Use same default momentum and epsilon as PyTorch equivalent
						UpperCamelCase__   =    tf.keras.layers.BatchNormalization(epsilon=1E-5 ,  momentum=0.9 ,  name="normalization"       )
						UpperCamelCase__   =    ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear"       )




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    (self.pad_value, self.pad_value)
						UpperCamelCase__   =    tf.pad(_UpperCAmelCase ,  [(0, 0), height_pad, width_pad, (0, 0)]       )
						UpperCamelCase__   =    self.conv(_UpperCAmelCase       )
						return hidden_state







			def 			snake_case__			(							self ,  snake_case ,  snake_case = False       ):

						'''simple docstring'''


						UpperCamelCase__   =    self.convolution(_UpperCAmelCase       )
						UpperCamelCase__   =    self.normalization(_UpperCAmelCase ,  training=_UpperCAmelCase       )
						UpperCamelCase__   =    self.activation(_UpperCAmelCase       )
						return hidden_state







class     lowerCamelCase__		(							tf.keras.layers.Layer ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  **snake_case       ):

						'''simple docstring'''


						super().__init__(**_UpperCAmelCase       )
						UpperCamelCase__   =    TFResNetConvLayer(
						    config.embedding_size ,  kernel_size=7 ,  stride=2 ,  activation=config.hidden_act ,  name="embedder" ,  )
						UpperCamelCase__   =    tf.keras.layers.MaxPoolaD(pool_size=3 ,  strides=2 ,  padding="valid" ,  name="pooler"       )
						UpperCamelCase__   =    config.num_channels







			def 			snake_case__			(							self ,  snake_case ,  snake_case = False       ):

						'''simple docstring'''


						UpperCamelCase__   =    shape_list(_UpperCAmelCase       )
						if tf.executing_eagerly() and 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."       )
						UpperCamelCase__   =    pixel_values
						UpperCamelCase__   =    self.embedder(_UpperCAmelCase       )
						UpperCamelCase__   =    tf.pad(_UpperCAmelCase ,  [[0, 0], [1, 1], [1, 1], [0, 0]]       )
						UpperCamelCase__   =    self.pooler(_UpperCAmelCase       )
						return hidden_state







class     lowerCamelCase__		(							tf.keras.layers.Layer ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case = 2 ,  **snake_case       ):

						'''simple docstring'''


						super().__init__(**_UpperCAmelCase       )
						UpperCamelCase__   =    tf.keras.layers.ConvaD(
						    _UpperCAmelCase ,  kernel_size=1 ,  strides=_UpperCAmelCase ,  use_bias=_UpperCAmelCase ,  name="convolution"       )
						# Use same default momentum and epsilon as PyTorch equivalent
						UpperCamelCase__   =    tf.keras.layers.BatchNormalization(epsilon=1E-5 ,  momentum=0.9 ,  name="normalization"       )







			def 			snake_case__			(							self ,  snake_case ,  snake_case = False       ):

						'''simple docstring'''


						UpperCamelCase__   =    x
						UpperCamelCase__   =    self.convolution(_UpperCAmelCase       )
						UpperCamelCase__   =    self.normalization(_UpperCAmelCase ,  training=_UpperCAmelCase       )
						return hidden_state







class     lowerCamelCase__		(							tf.keras.layers.Layer ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case ,  snake_case = 1 ,  snake_case = "relu" ,  **snake_case       ):

						'''simple docstring'''


						super().__init__(**_UpperCAmelCase       )
						UpperCamelCase__   =    in_channels != out_channels or stride != 1
						UpperCamelCase__   =    TFResNetConvLayer(_UpperCAmelCase ,  stride=_UpperCAmelCase ,  name="layer.0"       )
						UpperCamelCase__   =    TFResNetConvLayer(_UpperCAmelCase ,  activation=_UpperCAmelCase ,  name="layer.1"       )
						UpperCamelCase__   =    (
						    TFResNetShortCut(_UpperCAmelCase ,  stride=_UpperCAmelCase ,  name="shortcut"       )
						    if should_apply_shortcut
						    else tf.keras.layers.Activation("linear" ,  name="shortcut"       )
						)
						UpperCamelCase__   =    ACTaFN[activation]







			def 			snake_case__			(							self ,  snake_case ,  snake_case = False       ):

						'''simple docstring'''


						UpperCamelCase__   =    hidden_state
						UpperCamelCase__   =    self.conva(_UpperCAmelCase ,  training=_UpperCAmelCase       )
						UpperCamelCase__   =    self.conva(_UpperCAmelCase ,  training=_UpperCAmelCase       )
						UpperCamelCase__   =    self.shortcut(_UpperCAmelCase ,  training=_UpperCAmelCase       )
						hidden_state += residual
						UpperCamelCase__   =    self.activation(_UpperCAmelCase       )
						return hidden_state







class     lowerCamelCase__		(							tf.keras.layers.Layer ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case ,  snake_case = 1 ,  snake_case = "relu" ,  snake_case = 4 ,  **snake_case ,  ):

						'''simple docstring'''


						super().__init__(**_UpperCAmelCase       )
						UpperCamelCase__   =    in_channels != out_channels or stride != 1
						UpperCamelCase__   =    out_channels // reduction
						UpperCamelCase__   =    TFResNetConvLayer(_UpperCAmelCase ,  kernel_size=1 ,  name="layer.0"       )
						UpperCamelCase__   =    TFResNetConvLayer(_UpperCAmelCase ,  stride=_UpperCAmelCase ,  name="layer.1"       )
						UpperCamelCase__   =    TFResNetConvLayer(_UpperCAmelCase ,  kernel_size=1 ,  activation=_UpperCAmelCase ,  name="layer.2"       )
						UpperCamelCase__   =    (
						    TFResNetShortCut(_UpperCAmelCase ,  stride=_UpperCAmelCase ,  name="shortcut"       )
						    if should_apply_shortcut
						    else tf.keras.layers.Activation("linear" ,  name="shortcut"       )
						)
						UpperCamelCase__   =    ACTaFN[activation]







			def 			snake_case__			(							self ,  snake_case ,  snake_case = False       ):

						'''simple docstring'''


						UpperCamelCase__   =    hidden_state
						UpperCamelCase__   =    self.conva(_UpperCAmelCase ,  training=_UpperCAmelCase       )
						UpperCamelCase__   =    self.conva(_UpperCAmelCase ,  training=_UpperCAmelCase       )
						UpperCamelCase__   =    self.conva(_UpperCAmelCase ,  training=_UpperCAmelCase       )
						UpperCamelCase__   =    self.shortcut(_UpperCAmelCase ,  training=_UpperCAmelCase       )
						hidden_state += residual
						UpperCamelCase__   =    self.activation(_UpperCAmelCase       )
						return hidden_state







class     lowerCamelCase__		(							tf.keras.layers.Layer ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  snake_case ,  snake_case ,  snake_case = 2 ,  snake_case = 2 ,  **snake_case       ):

						'''simple docstring'''


						super().__init__(**_UpperCAmelCase       )

						UpperCamelCase__   =    TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

						UpperCamelCase__   =    [layer(_UpperCAmelCase ,  _UpperCAmelCase ,  stride=_UpperCAmelCase ,  activation=config.hidden_act ,  name="layers.0"       )]
						layers += [
						    layer(_UpperCAmelCase ,  _UpperCAmelCase ,  activation=config.hidden_act ,  name=F'''layers.{i + 1}'''       )
						    for i in range(depth - 1       )
						]
						UpperCamelCase__   =    layers







			def 			snake_case__			(							self ,  snake_case ,  snake_case = False       ):

						'''simple docstring'''


						for layer in self.stage_layers:
									UpperCamelCase__   =    layer(_UpperCAmelCase ,  training=_UpperCAmelCase       )
						return hidden_state







class     lowerCamelCase__		(							tf.keras.layers.Layer ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  **snake_case       ):

						'''simple docstring'''


						super().__init__(**_UpperCAmelCase       )
						# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
						UpperCamelCase__   =    [
						    TFResNetStage(
						        _UpperCAmelCase ,  config.embedding_size ,  config.hidden_sizes[0] ,  stride=2 if config.downsample_in_first_stage else 1 ,  depth=config.depths[0] ,  name="stages.0" ,  )
						]
						for i, (in_channels, out_channels, depth) in enumerate(
						    zip(config.hidden_sizes ,  config.hidden_sizes[1:] ,  config.depths[1:]       )       ):
									self.stages.append(TFResNetStage(_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  depth=_UpperCAmelCase ,  name=F'''stages.{i + 1}'''       )       )







			def 			snake_case__			(							self ,  snake_case ,  snake_case = False ,  snake_case = True ,  snake_case = False ,  ):

						'''simple docstring'''


						UpperCamelCase__   =    () if output_hidden_states else None

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

									UpperCamelCase__   =    stage_module(_UpperCAmelCase ,  training=_UpperCAmelCase       )

						if output_hidden_states:
									UpperCamelCase__   =    hidden_states + (hidden_state,)

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

						return TFBaseModelOutputWithNoAttention(last_hidden_state=_UpperCAmelCase ,  hidden_states=_UpperCAmelCase       )







class     lowerCamelCase__		(							SCREAMING_SNAKE_CASE__ ):






			"""simple docstring"""






			_UpperCamelCase						:   Optional[int]					=						ResNetConfig
			_UpperCamelCase						:   Optional[Any]					=						'resnet'
			_UpperCamelCase						:   List[str]					=						'pixel_values'




			@property
			def 			snake_case__			(							self       ):

						'''simple docstring'''


						return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) ,  dtype=tf.floataa       )}



__UpperCamelCase      						=    R'\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'


__UpperCamelCase      						=    R'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'







@keras_serializable
class     lowerCamelCase__		(							tf.keras.layers.Layer ):






			"""simple docstring"""






			_UpperCamelCase						:   List[Any]					=						ResNetConfig




			def __init__(							self ,  snake_case ,  **snake_case       ):

						'''simple docstring'''


						super().__init__(**_UpperCAmelCase       )
						UpperCamelCase__   =    config
						UpperCamelCase__   =    TFResNetEmbeddings(_UpperCAmelCase ,  name="embedder"       )
						UpperCamelCase__   =    TFResNetEncoder(_UpperCAmelCase ,  name="encoder"       )
						UpperCamelCase__   =    tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCAmelCase       )







			@unpack_inputs
			def 			snake_case__			(							self ,  snake_case ,  snake_case = None ,  snake_case = None ,  snake_case = False ,  ):

						'''simple docstring'''


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

						# TF 2.0 image layers can't use NCHW format when running on CPU.
						# We transpose to NHWC format and then transpose back after the full forward pass.
						# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
						UpperCamelCase__   =    tf.transpose(_UpperCAmelCase ,  perm=[0, 2, 3, 1]       )
						UpperCamelCase__   =    self.embedder(_UpperCAmelCase ,  training=_UpperCAmelCase       )

						UpperCamelCase__   =    self.encoder(
						    _UpperCAmelCase ,  output_hidden_states=_UpperCAmelCase ,  return_dict=_UpperCAmelCase ,  training=_UpperCAmelCase       )

						UpperCamelCase__   =    encoder_outputs[0]

						UpperCamelCase__   =    self.pooler(_UpperCAmelCase       )

						# Transpose all the outputs to the NCHW format
						# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
						UpperCamelCase__   =    tf.transpose(_UpperCAmelCase ,  (0, 3, 1, 2)       )
						UpperCamelCase__   =    tf.transpose(_UpperCAmelCase ,  (0, 3, 1, 2)       )
						UpperCamelCase__   =    ()
						for hidden_state in encoder_outputs[1:]:
									UpperCamelCase__   =    hidden_states + tuple(tf.transpose(_UpperCAmelCase ,  (0, 3, 1, 2)       ) for h in hidden_state       )

						if not return_dict:
									return (last_hidden_state, pooled_output) + hidden_states

						UpperCamelCase__   =    hidden_states if output_hidden_states else None

						return TFBaseModelOutputWithPoolingAndNoAttention(
						    last_hidden_state=_UpperCAmelCase ,  pooler_output=_UpperCAmelCase ,  hidden_states=_UpperCAmelCase ,  )







@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'   ,		SCREAMING_SNAKE_CASE__   ,		)
class     lowerCamelCase__		(							SCREAMING_SNAKE_CASE__ ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  **snake_case       ):

						'''simple docstring'''


						super().__init__(_UpperCAmelCase ,  **_UpperCAmelCase       )
						UpperCamelCase__   =    TFResNetMainLayer(config=_UpperCAmelCase ,  name="resnet"       )







			@add_start_docstrings_to_model_forward(_UpperCAmelCase       )
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC ,  output_type=_UpperCAmelCase ,  config_class=_CONFIG_FOR_DOC ,  modality="vision" ,  expected_output=_EXPECTED_OUTPUT_SHAPE ,  )
			@unpack_inputs
			def 			snake_case__			(							self ,  snake_case ,  snake_case = None ,  snake_case = None ,  snake_case = False ,  ):

						'''simple docstring'''


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

						UpperCamelCase__   =    self.resnet(
						    pixel_values=_UpperCAmelCase ,  output_hidden_states=_UpperCAmelCase ,  return_dict=_UpperCAmelCase ,  training=_UpperCAmelCase ,  )
						return resnet_outputs







@add_start_docstrings(
    '\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '   ,		SCREAMING_SNAKE_CASE__   ,		)
class     lowerCamelCase__		(							SCREAMING_SNAKE_CASE__   ,		SCREAMING_SNAKE_CASE__ ):






			"""simple docstring"""






			def __init__(							self ,  snake_case ,  **snake_case       ):

						'''simple docstring'''


						super().__init__(_UpperCAmelCase ,  **_UpperCAmelCase       )
						UpperCamelCase__   =    config.num_labels
						UpperCamelCase__   =    TFResNetMainLayer(_UpperCAmelCase ,  name="resnet"       )
						# classification head
						UpperCamelCase__   =    (
						    tf.keras.layers.Dense(config.num_labels ,  name="classifier.1"       )
						    if config.num_labels > 0
						    else tf.keras.layers.Activation("linear" ,  name="classifier.1"       )
						)




			def 			snake_case__			(							self ,  snake_case       ):

						'''simple docstring'''


						UpperCamelCase__   =    tf.keras.layers.Flatten()(_UpperCAmelCase       )
						UpperCamelCase__   =    self.classifier_layer(_UpperCAmelCase       )
						return logits







			@add_start_docstrings_to_model_forward(_UpperCAmelCase       )
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT ,  output_type=_UpperCAmelCase ,  config_class=_CONFIG_FOR_DOC ,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,  )
			@unpack_inputs
			def 			snake_case__			(							self ,  snake_case = None ,  snake_case = None ,  snake_case = None ,  snake_case = None ,  snake_case = False ,  ):

						'''simple docstring'''


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

						UpperCamelCase__   =    self.resnet(
						    _UpperCAmelCase ,  output_hidden_states=_UpperCAmelCase ,  return_dict=_UpperCAmelCase ,  training=_UpperCAmelCase       )

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

						UpperCamelCase__   =    self.classifier(_UpperCAmelCase       )

						UpperCamelCase__   =    None if labels is None else self.hf_compute_loss(_UpperCAmelCase ,  _UpperCAmelCase       )

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

						return TFImageClassifierOutputWithNoAttention(loss=_UpperCAmelCase ,  logits=_UpperCAmelCase ,  hidden_states=outputs.hidden_states       )








import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__UpperCamelCase      						=    logging.get_logger(__name__)

__UpperCamelCase      						=    {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'mask_emb': 'masked_spec_embed',
}
def     UpperCamelCase_(			_A				:int			,       _A				:Dict			,       _A				:int			,       _A				:List[str]			,       _A				:Optional[int]					)->						Any:
			for attribute in key.split("."					):
						UpperCamelCase__   =    getattr(_A			,       _A					)

			if weight_type is not None:
						UpperCamelCase__   =    getattr(_A			,       _A					).shape
			else:
						UpperCamelCase__   =    hf_pointer.shape

			assert hf_shape == value.shape, (
			    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
			    F''' {value.shape} for {full_name}'''
			)

			if weight_type == "weight":
						UpperCamelCase__   =    value
			elif weight_type == "weight_g":
						UpperCamelCase__   =    value
			elif weight_type == "weight_v":
						UpperCamelCase__   =    value
			elif weight_type == "bias":
						UpperCamelCase__   =    value
			else:
						UpperCamelCase__   =    value

			logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''					)
def     UpperCamelCase_(			_A				:Optional[Any]			,       _A				:Dict					)->						List[Any]:
			UpperCamelCase__   =    []
			UpperCamelCase__   =    fairseq_model.state_dict()

			UpperCamelCase__   =    hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						UpperCamelCase__   =    False
						if "conv_layers" in name:
									load_conv_layer(
									    _A			,       _A			,       _A			,       _A			,       hf_model.config.feat_extract_norm == "group"			,       )
									UpperCamelCase__   =    True
						else:
									for key, mapped_key in MAPPING.items():
												UpperCamelCase__   =    mapped_key

												if key in name:
															UpperCamelCase__   =    True
															if "*" in mapped_key:
																		UpperCamelCase__   =    name.split(_A					)[0].split("."					)[-2]
																		UpperCamelCase__   =    mapped_key.replace("*"			,       _A					)
															if "weight_g" in name:
																		UpperCamelCase__   =    "weight_g"
															elif "weight_v" in name:
																		UpperCamelCase__   =    "weight_v"
															elif "weight" in name:
																		UpperCamelCase__   =    "weight"
															elif "bias" in name:
																		UpperCamelCase__   =    "bias"
															else:
																		UpperCamelCase__   =    None
															set_recursively(_A			,       _A			,       _A			,       _A			,       _A					)
												continue
						if not is_used:
									unused_weights.append(_A					)

			logger.warning(F'''Unused weights: {unused_weights}'''					)
def     UpperCamelCase_(			_A				:Any			,       _A				:List[Any]			,       _A				:Union[str, Any]			,       _A				:Dict			,       _A				:Union[str, Any]					)->						Optional[Any]:
			UpperCamelCase__   =    full_name.split("conv_layers."					)[-1]
			UpperCamelCase__   =    name.split("."					)
			UpperCamelCase__   =    int(items[0]					)
			UpperCamelCase__   =    int(items[1]					)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    F'''{full_name} has size {value.shape}, but'''
									    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
									)
									UpperCamelCase__   =    value
									logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    F'''{full_name} has size {value.shape}, but'''
									    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
									)
									UpperCamelCase__   =    value
									logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
									    " found."
									)
									UpperCamelCase__   =    value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    F'''{full_name} has size {value.shape}, but'''
									    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
									)
									UpperCamelCase__   =    value
									logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
			else:
						unused_weights.append(_A					)
def     UpperCamelCase_(			_A				:Dict					)->						Union[str, Any]:
			UpperCamelCase__   =    HubertConfig()
			UpperCamelCase__   =    model.config

			UpperCamelCase__   =    fs_config.activation_dropout
			UpperCamelCase__   =    False
			UpperCamelCase__   =    fs_config.attention_dropout
			UpperCamelCase__   =    False
			UpperCamelCase__   =    eval(fs_config.extractor_conv_feature_layers					)
			UpperCamelCase__   =    [x[0] for x in conv_layers]
			UpperCamelCase__   =    [x[1] for x in conv_layers]
			UpperCamelCase__   =    [x[2] for x in conv_layers]
			UpperCamelCase__   =    "gelu"
			UpperCamelCase__   =    "layer" if fs_config.extractor_mode == "layer_norm" else "group"
			UpperCamelCase__   =    False
			UpperCamelCase__   =    0.0
			UpperCamelCase__   =    0.0
			UpperCamelCase__   =    fs_config.activation_fn
			UpperCamelCase__   =    fs_config.dropout
			UpperCamelCase__   =    fs_config.encoder_embed_dim
			UpperCamelCase__   =    0.0_2
			UpperCamelCase__   =    fs_config.encoder_ffn_embed_dim
			UpperCamelCase__   =    1e-5
			UpperCamelCase__   =    0.0
			UpperCamelCase__   =    fs_config.encoder_attention_heads
			UpperCamelCase__   =    fs_config.conv_pos_groups
			UpperCamelCase__   =    fs_config.conv_pos
			UpperCamelCase__   =    len(_A					)
			UpperCamelCase__   =    fs_config.encoder_layers

			return config
@torch.no_grad()
def     UpperCamelCase_(			_A				:str			,       _A				:Union[str, Any]=None					)->						str:
			UpperCamelCase__   =    distilhubert().model.model

			if config_path is not None:
						UpperCamelCase__   =    HubertConfig.from_pretrained(_A					)
			else:
						UpperCamelCase__   =    convert_config(_A					)
			UpperCamelCase__   =    model.eval()

			UpperCamelCase__   =    WavaVecaFeatureExtractor(
			    feature_size=1			,       sampling_rate=1_60_00			,       padding_value=0			,       do_normalize=_A			,       return_attention_mask=_A			,       )
			UpperCamelCase__   =    HubertModel(_A					)

			recursively_load_weights(_A			,       _A					)

			feature_extractor.save_pretrained(_A					)
			hf_model.save_pretrained(_A					)


if __name__ == "__main__":
						__UpperCamelCase      						=    argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						__UpperCamelCase      						=    parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)


