'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowercase_  		=			{
    '''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__      ,   a__      ,   a__      ,   a__      ,   a__   ):


						'''simple docstring'''






						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 				UpperCamelCase__      (      a__      ,   a__   ):


						'''simple docstring'''






						_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 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__      ,   a__   ):


						'''simple docstring'''






						_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 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						_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 				UpperCamelCase__      (      a__      ,   a__=None   ):


						'''simple docstring'''






						_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=1_6_0_0_0      ,   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__":
							lowercase_  		=			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''')
							lowercase_  		=			parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






'''simple docstring'''

def 				UpperCamelCase__      (      a__ = 1_0_0_0   ):


						'''simple docstring'''






						_lowerCAmelCase					    =[]
						_lowerCAmelCase					    =[]
						_lowerCAmelCase					    =3
						_lowerCAmelCase					    =5
						_lowerCAmelCase					    =1
						while True:
												_lowerCAmelCase					    =z * temp
												if result < n:
																		zmulti.append(a__   )
																		temp += 1
												else:
																		_lowerCAmelCase					    =1
																		break
						while True:
												_lowerCAmelCase					    =x * temp
												if result < n:
																		xmulti.append(a__   )
																		temp += 1
												else:
																		break
						_lowerCAmelCase					    =list(set(xmulti + zmulti   )   )
						return sum(a__   )


if __name__ == "__main__":
							print(F'{solution() = }')





