

class 						lowercase     :


		"""simple docstring"""




		def __init__(					self						,							__snake_case):
			_UpperCamelCase :		Any								=    data
			_UpperCamelCase :		List[str]								=    None
			_UpperCamelCase :		int								=    None






		def __str__(					self):
			return f'''{self.data}'''







class 						lowercase     :


		"""simple docstring"""




		def __init__(					self):
			_UpperCamelCase :		List[str]								=    None
			_UpperCamelCase :		Optional[Any]								=    None


		def __iter__(					self):
			_UpperCamelCase :		List[str]								=    self.head
			while node:
				yield node.data
				_UpperCamelCase :		Optional[Any]								=    node.next


		def __str__(					self):
			return "->".join([str(__snake_case) for item in self])


		def __len__(					self):
			return sum(1 for _ in self)


		def 		A__       (					self						,							__snake_case):
			self.insert_at_nth(0						,							__snake_case)


		def 		A__       (					self						,							__snake_case):
			self.insert_at_nth(len(self)						,							__snake_case)


		def 		A__       (					self						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Union[str, Any]								=    len(self)

			if not 0 <= index <= length:
				raise IndexError('list index out of range')
			_UpperCamelCase :		Tuple								=    Node(__snake_case)
			if self.head is None:
				_UpperCamelCase :		Optional[Any]								=    new_node
			elif index == 0:
				_UpperCamelCase :		Optional[int]								=    new_node
				_UpperCamelCase :		int								=    self.head
				_UpperCamelCase :		List[Any]								=    new_node
			elif index == length:
				_UpperCamelCase :		Optional[int]								=    new_node
				_UpperCamelCase :		Tuple								=    self.tail
				_UpperCamelCase :		List[Any]								=    new_node
			else:
				_UpperCamelCase :		Tuple								=    self.head
				for _ in range(0						,							__snake_case):
					_UpperCamelCase :		Union[str, Any]								=    temp.next
				_UpperCamelCase :		Optional[Any]								=    new_node
				_UpperCamelCase :		str								=    temp.previous
				_UpperCamelCase :		int								=    temp
				_UpperCamelCase :		List[Any]								=    new_node


		def 		A__       (					self):
			return self.delete_at_nth(0)


		def 		A__       (					self):
			return self.delete_at_nth(len(self) - 1)


		def 		A__       (					self						,							__snake_case):
			_UpperCamelCase :		Tuple								=    len(self)

			if not 0 <= index <= length - 1:
				raise IndexError('list index out of range')
			_UpperCamelCase :		int								=    self.head  # default first node
			if length == 1:
				_UpperCamelCase :		str								=    None
			elif index == 0:
				_UpperCamelCase :		List[str]								=    self.head.next
				_UpperCamelCase :		List[Any]								=    None
			elif index == length - 1:
				_UpperCamelCase :		int								=    self.tail
				_UpperCamelCase :		Dict								=    self.tail.previous
				_UpperCamelCase :		str								=    None
			else:
				_UpperCamelCase :		int								=    self.head
				for _ in range(0						,							__snake_case):
					_UpperCamelCase :		str								=    temp.next
				_UpperCamelCase :		List[Any]								=    temp
				_UpperCamelCase :		Optional[Any]								=    temp.previous
				_UpperCamelCase :		Tuple								=    temp.next
			return delete_node.data


		def 		A__       (					self						,							__snake_case):
			_UpperCamelCase :		Optional[Any]								=    self.head

			while current.data != data:  # Find the position to delete
				if current.next:
					_UpperCamelCase :		Optional[int]								=    current.next
				else:  # We have reached the end an no value matches
					raise ValueError('No data matching given value')

			if current == self.head:
				self.delete_head()

			elif current == self.tail:
				self.delete_tail()

			else:  # Before: 1 <--> 2(current) <--> 3
				_UpperCamelCase :		List[str]								=    current.next  # 1 --> 3
				_UpperCamelCase :		Optional[Any]								=    current.previous  # 1 <--> 3
			return data






		def 		A__       (					self):
			return len(self) == 0






def        lowerCamelCase_  (				)    ->					None:




	'''simple docstring'''




	_UpperCamelCase :		Optional[Any]								=    DoublyLinkedList()
	assert linked_list.is_empty() is True
	assert str(UpperCAmelCase_						) == ""

	try:
		linked_list.delete_head()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	try:
		linked_list.delete_tail()
		raise AssertionError  # This should not happen.
	except IndexError:
		assert True  # This should happen.

	for i in range(1_0						):
		assert len(UpperCAmelCase_						) == i
		linked_list.insert_at_nth(UpperCAmelCase_				,							i + 1						)
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(1				,							1_1						)						)

	linked_list.insert_at_head(0						)
	linked_list.insert_at_tail(1_1						)
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(0				,							1_2						)						)

	assert linked_list.delete_head() == 0
	assert linked_list.delete_at_nth(9						) == 1_0
	assert linked_list.delete_tail() == 1_1
	assert len(UpperCAmelCase_						) == 9
	assert str(UpperCAmelCase_						) == "->".join(str(UpperCAmelCase_						) for i in range(1				,							1_0						)						)


if __name__ == "__main__":
							from doctest import testmod

							testmod()







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCAmelCase__             =		{
    """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        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[Any]						)    ->					Any:




	'''simple docstring'''




	for attribute in key.split('.'						):
		_UpperCamelCase :		Any								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						)

	if weight_type is not None:
		_UpperCamelCase :		List[str]								=    getattr(UpperCAmelCase_				,							UpperCAmelCase_						).shape
	else:
		_UpperCamelCase :		Optional[int]								=    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 :		Tuple								=    value
	elif weight_type == "weight_g":
		_UpperCamelCase :		Optional[Any]								=    value
	elif weight_type == "weight_v":
		_UpperCamelCase :		Tuple								=    value
	elif weight_type == "bias":
		_UpperCamelCase :		Any								=    value
	else:
		_UpperCamelCase :		Tuple								=    value

	logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    []
	_UpperCamelCase :		List[Any]								=    fairseq_model.state_dict()

	_UpperCamelCase :		int								=    hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_UpperCamelCase :		Dict								=    False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							hf_model.config.feat_extract_norm == 'group'				,							)
			_UpperCamelCase :		Union[str, Any]								=    True
		else:
			for key, mapped_key in MAPPING.items():
				_UpperCamelCase :		Tuple								=    mapped_key

				if key in name:
					_UpperCamelCase :		List[Any]								=    True
					if "*" in mapped_key:
						_UpperCamelCase :		List[Any]								=    name.split(UpperCAmelCase_						)[0].split('.'						)[-2]
						_UpperCamelCase :		Optional[Any]								=    mapped_key.replace('*'				,							UpperCAmelCase_						)
					if "weight_g" in name:
						_UpperCamelCase :		List[Any]								=    'weight_g'
					elif "weight_v" in name:
						_UpperCamelCase :		Tuple								=    'weight_v'
					elif "weight" in name:
						_UpperCamelCase :		Optional[Any]								=    'weight'
					elif "bias" in name:
						_UpperCamelCase :		List[Any]								=    'bias'
					else:
						_UpperCamelCase :		str								=    None
					set_recursively(UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_						)
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_						)

	logger.warning(F'''Unused weights: {unused_weights}'''						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Union[str, Any]				,							UpperCAmelCase_				:  List[Any]				,							UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  int						)    ->					Dict:




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    full_name.split('conv_layers.'						)[-1]
	_UpperCamelCase :		str								=    name.split('.'						)
	_UpperCamelCase :		List[Any]								=    int(items[0]						)
	_UpperCamelCase :		Tuple								=    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 :		Any								=    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 :		List[str]								=    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 :		str								=    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 :		Dict								=    value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
	else:
		unused_weights.append(UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Tuple						)    ->					List[str]:




	'''simple docstring'''




	_UpperCamelCase :		Union[str, Any]								=    HubertConfig()
	_UpperCamelCase :		List[str]								=    model.config

	_UpperCamelCase :		Any								=    fs_config.activation_dropout
	_UpperCamelCase :		Tuple								=    False
	_UpperCamelCase :		Tuple								=    fs_config.attention_dropout
	_UpperCamelCase :		Dict								=    False
	_UpperCamelCase :		Optional[Any]								=    eval(fs_config.extractor_conv_feature_layers						)
	_UpperCamelCase :		Dict								=    [x[0] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[1] for x in conv_layers]
	_UpperCamelCase :		Any								=    [x[2] for x in conv_layers]
	_UpperCamelCase :		Dict								=    'gelu'
	_UpperCamelCase :		List[Any]								=    'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_UpperCamelCase :		List[Any]								=    False
	_UpperCamelCase :		List[Any]								=    0.0
	_UpperCamelCase :		Optional[int]								=    0.0
	_UpperCamelCase :		Optional[int]								=    fs_config.activation_fn
	_UpperCamelCase :		Optional[int]								=    fs_config.dropout
	_UpperCamelCase :		Union[str, Any]								=    fs_config.encoder_embed_dim
	_UpperCamelCase :		Tuple								=    0.0_2
	_UpperCamelCase :		int								=    fs_config.encoder_ffn_embed_dim
	_UpperCamelCase :		int								=    1e-5
	_UpperCamelCase :		Optional[Any]								=    0.0
	_UpperCamelCase :		List[str]								=    fs_config.encoder_attention_heads
	_UpperCamelCase :		List[str]								=    fs_config.conv_pos_groups
	_UpperCamelCase :		Union[str, Any]								=    fs_config.conv_pos
	_UpperCamelCase :		int								=    len(UpperCAmelCase_						)
	_UpperCamelCase :		Tuple								=    fs_config.encoder_layers

	return config






@torch.no_grad()
def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict=None						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Tuple								=    distilhubert().model.model

	if config_path is not None:
		_UpperCamelCase :		Optional[Any]								=    HubertConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Optional[int]								=    convert_config(UpperCAmelCase_						)
	_UpperCamelCase :		List[Any]								=    model.eval()

	_UpperCamelCase :		List[Any]								=    WavaVecaFeatureExtractor(
	    feature_size=1				,							sampling_rate=1_6_0_0_0				,							padding_value=0				,							do_normalize=UpperCAmelCase_				,							return_attention_mask=UpperCAmelCase_				,							)
	_UpperCamelCase :		Optional[Any]								=    HubertModel(UpperCAmelCase_						)

	recursively_load_weights(UpperCAmelCase_				,							UpperCAmelCase_						)

	feature_extractor.save_pretrained(UpperCAmelCase_						)
	hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
							lowerCAmelCase__             =		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""")
							lowerCAmelCase__             =		parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)




