


'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCAmelCase   :int								=    {
    '''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    (    lowerCAmelCase       :			str	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			List[Any]	):





	"""simple docstring"""







	for attribute in key.split('.'	):
		__magic_name__ :					int  			=  getattr(lowerCAmelCase	,	lowerCAmelCase	)

	if weight_type is not None:
		__magic_name__ :					Optional[Any]  			=  getattr(lowerCAmelCase	,	lowerCAmelCase	).shape
	else:
		__magic_name__ :					str  			=  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":
		__magic_name__ :					int  			=  value
	elif weight_type == "weight_g":
		__magic_name__ :					Union[str, Any]  			=  value
	elif weight_type == "weight_v":
		__magic_name__ :					str  			=  value
	elif weight_type == "bias":
		__magic_name__ :					Optional[int]  			=  value
	else:
		__magic_name__ :					int  			=  value

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





def lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	,	lowerCAmelCase       :			Union[str, Any]	):





	"""simple docstring"""







	__magic_name__ :					Dict  			=  []
	__magic_name__ :					Union[str, Any]  			=  fairseq_model.state_dict()

	__magic_name__ :					Optional[int]  			=  hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		__magic_name__ :					Dict  			=  False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	hf_model.config.feat_extract_norm == 'group'	,	)
			__magic_name__ :					str  			=  True
		else:
			for key, mapped_key in MAPPING.items():
				__magic_name__ :					List[str]  			=  mapped_key

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

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





def lowerCamelCase    (    lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			str	):





	"""simple docstring"""







	__magic_name__ :					int  			=  full_name.split('conv_layers.'	)[-1]
	__magic_name__ :					int  			=  name.split('.'	)
	__magic_name__ :					Tuple  			=  int(items[0]	)
	__magic_name__ :					str  			=  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.'
			)
			__magic_name__ :					Union[str, 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.'
			)
			__magic_name__ :					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."
			)
			__magic_name__ :					Dict  			=  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.'
			)
			__magic_name__ :					Any  			=  value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
	else:
		unused_weights.append(lowerCAmelCase	)





def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):





	"""simple docstring"""







	__magic_name__ :					List[str]  			=  HubertConfig()
	__magic_name__ :					Dict  			=  model.config

	__magic_name__ :					Union[str, Any]  			=  fs_config.activation_dropout
	__magic_name__ :					Any  			=  False
	__magic_name__ :					str  			=  fs_config.attention_dropout
	__magic_name__ :					List[str]  			=  False
	__magic_name__ :					Optional[Any]  			=  eval(fs_config.extractor_conv_feature_layers	)
	__magic_name__ :					Tuple  			=  [x[0] for x in conv_layers]
	__magic_name__ :					Tuple  			=  [x[1] for x in conv_layers]
	__magic_name__ :					Optional[Any]  			=  [x[2] for x in conv_layers]
	__magic_name__ :					Any  			=  'gelu'
	__magic_name__ :					Any  			=  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	__magic_name__ :					List[str]  			=  False
	__magic_name__ :					List[str]  			=  0.0
	__magic_name__ :					int  			=  0.0
	__magic_name__ :					List[str]  			=  fs_config.activation_fn
	__magic_name__ :					List[Any]  			=  fs_config.dropout
	__magic_name__ :					str  			=  fs_config.encoder_embed_dim
	__magic_name__ :					Optional[int]  			=  0.02
	__magic_name__ :					Union[str, Any]  			=  fs_config.encoder_ffn_embed_dim
	__magic_name__ :					Optional[int]  			=  1e-5
	__magic_name__ :					Tuple  			=  0.0
	__magic_name__ :					int  			=  fs_config.encoder_attention_heads
	__magic_name__ :					Tuple  			=  fs_config.conv_pos_groups
	__magic_name__ :					Optional[int]  			=  fs_config.conv_pos
	__magic_name__ :					Optional[Any]  			=  len(lowerCAmelCase	)
	__magic_name__ :					int  			=  fs_config.encoder_layers

	return config





@torch.no_grad()
def lowerCamelCase    (    lowerCAmelCase       :			Any	,	lowerCAmelCase       :			List[str]=None	):





	"""simple docstring"""







	__magic_name__ :					Any  			=  distilhubert().model.model

	if config_path is not None:
		__magic_name__ :					Any  			=  HubertConfig.from_pretrained(lowerCAmelCase	)
	else:
		__magic_name__ :					Union[str, Any]  			=  convert_config(lowerCAmelCase	)
	__magic_name__ :					Dict  			=  model.eval()

	__magic_name__ :					Optional[Any]  			=  WavaVecaFeatureExtractor(
	    feature_size=1	,	sampling_rate=1_6000	,	padding_value=0	,	do_normalize=lowerCAmelCase	,	return_attention_mask=lowerCAmelCase	,	)
	__magic_name__ :					Tuple  			=  HubertModel(lowerCAmelCase	)

	recursively_load_weights(lowerCAmelCase	,	lowerCAmelCase	)

	feature_extractor.save_pretrained(lowerCAmelCase	)
	hf_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
							lowerCAmelCase   :Any								=    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   :List[str]								=    parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)



'''simple docstring'''

import math





def lowerCamelCase    (    lowerCAmelCase       :			int	):





	"""simple docstring"""







	if not isinstance(lowerCAmelCase	,	lowerCAmelCase	):
		__magic_name__ :					str  			=  f'Input value of [number={number}] must be an integer'
		raise TypeError(lowerCAmelCase	)
	if number < 1:
		__magic_name__ :					Tuple  			=  f'Input value of [number={number}] must be a positive integer'
		raise ValueError(lowerCAmelCase	)
	__magic_name__ :					Tuple  			=  [number]
	while number != 1:
		if number % 2 == 0:
			__magic_name__ :					Optional[int]  			=  math.floor(math.sqrt(lowerCAmelCase	)	)
		else:
			__magic_name__ :					Union[str, Any]  			=  math.floor(
			    math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	)	)
		sequence.append(lowerCAmelCase	)
	return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()