


'''simple docstring'''






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 					_snake_case   (     A ,    A ,    A ,    A ,    A       )						->    str:
							for attribute in key.split('''.'''       ):
														lowerCAmelCase__          =	getattr(A ,    A       )

							if weight_type is not None:
														lowerCAmelCase__          =	getattr(A ,    A       ).shape
							else:
														lowerCAmelCase__          =	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":
														lowerCAmelCase__          =	value
							elif weight_type == "weight_g":
														lowerCAmelCase__          =	value
							elif weight_type == "weight_v":
														lowerCAmelCase__          =	value
							elif weight_type == "bias":
														lowerCAmelCase__          =	value
							else:
														lowerCAmelCase__          =	value

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


def 					_snake_case   (     A ,    A       )						->    Tuple:
							lowerCAmelCase__          =	[]
							lowerCAmelCase__          =	fairseq_model.state_dict()

							lowerCAmelCase__          =	hf_model.feature_extractor

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

																												if key in name:
																																			lowerCAmelCase__          =	True
																																			if "*" in mapped_key:
																																										lowerCAmelCase__          =	name.split(A       )[0].split('''.'''       )[-2]
																																										lowerCAmelCase__          =	mapped_key.replace('''*''' ,    A       )
																																			if "weight_g" in name:
																																										lowerCAmelCase__          =	'''weight_g'''
																																			elif "weight_v" in name:
																																										lowerCAmelCase__          =	'''weight_v'''
																																			elif "weight" in name:
																																										lowerCAmelCase__          =	'''weight'''
																																			elif "bias" in name:
																																										lowerCAmelCase__          =	'''bias'''
																																			else:
																																										lowerCAmelCase__          =	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 					_snake_case   (     A ,    A ,    A ,    A ,    A       )						->    Optional[int]:
							lowerCAmelCase__          =	full_name.split('''conv_layers.'''       )[-1]
							lowerCAmelCase__          =	name.split('''.'''       )
							lowerCAmelCase__          =	int(items[0]       )
							lowerCAmelCase__          =	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."""
																					)
																					lowerCAmelCase__          =	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."""
																					)
																					lowerCAmelCase__          =	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."
																					)
																					lowerCAmelCase__          =	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."""
																					)
																					lowerCAmelCase__          =	value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
							else:
														unused_weights.append(A       )


def 					_snake_case   (     A       )						->    Union[str, Any]:
							lowerCAmelCase__          =	HubertConfig()
							lowerCAmelCase__          =	model.config

							lowerCAmelCase__          =	fs_config.activation_dropout
							lowerCAmelCase__          =	False
							lowerCAmelCase__          =	fs_config.attention_dropout
							lowerCAmelCase__          =	False
							lowerCAmelCase__          =	eval(fs_config.extractor_conv_feature_layers       )
							lowerCAmelCase__          =	[x[0] for x in conv_layers]
							lowerCAmelCase__          =	[x[1] for x in conv_layers]
							lowerCAmelCase__          =	[x[2] for x in conv_layers]
							lowerCAmelCase__          =	'''gelu'''
							lowerCAmelCase__          =	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
							lowerCAmelCase__          =	False
							lowerCAmelCase__          =	0.0
							lowerCAmelCase__          =	0.0
							lowerCAmelCase__          =	fs_config.activation_fn
							lowerCAmelCase__          =	fs_config.dropout
							lowerCAmelCase__          =	fs_config.encoder_embed_dim
							lowerCAmelCase__          =	0.02
							lowerCAmelCase__          =	fs_config.encoder_ffn_embed_dim
							lowerCAmelCase__          =	1E-5
							lowerCAmelCase__          =	0.0
							lowerCAmelCase__          =	fs_config.encoder_attention_heads
							lowerCAmelCase__          =	fs_config.conv_pos_groups
							lowerCAmelCase__          =	fs_config.conv_pos
							lowerCAmelCase__          =	len(A       )
							lowerCAmelCase__          =	fs_config.encoder_layers

							return config


@torch.no_grad()
def 					_snake_case   (     A ,    A=None       )						->    int:
							lowerCAmelCase__          =	distilhubert().model.model

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

							lowerCAmelCase__          =	WavaVecaFeatureExtractor(
							    feature_size=1 ,    sampling_rate=16000 ,    padding_value=0 ,    do_normalize=A ,    return_attention_mask=A ,    )
							lowerCAmelCase__          =	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)



'''simple docstring'''






def 					_snake_case   (     A       )						->    str:
							return " ".join(sorted(set(sentence.split()       )       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()