



"""simple docstring"""







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

A     =      {
    '''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__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				List[str]:
							for attribute in key.split('.'      ):
														A    =       getattr(A__					,   A__      )

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

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

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__      )			->				Union[str, Any]:
							A    =       []
							A    =       fairseq_model.state_dict()

							A    =       hf_model.feature_extractor

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

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

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				List[str]:
							A    =       HubertConfig()
							A    =       model.config

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

							return config

@torch.no_grad()
def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=None      )			->				Union[str, Any]:
							A    =       distilhubert().model.model

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

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

							recursively_load_weights(A__					,   A__      )

							feature_extractor.save_pretrained(A__      )
							hf_model.save_pretrained(A__      )


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






"""simple docstring"""




import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )

   lowerCamelCase__						       =      np.zeros((rows, 1)						,     dtype=A__ )
   for row in reversed(range(A__ ) ):
      lowerCamelCase__						       =      0
      for col in range(row + 1						,     A__ ):
         total += coefficients[row, col] * x[col]

      lowerCamelCase__						       =      (vector[row] - total) / coefficients[row, row]

   return x



def 						UpperCAmelCase__ (					A__						,     A__ )   ->   NDArray[floataa]:




   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__						       =      np.shape(A__ )
   if rows != columns:
      return np.array(()						,     dtype=A__ )

   # augmented matrix
   lowerCamelCase__						       =      np.concatenate((coefficients, vector)						,     axis=1 )
   lowerCamelCase__						       =      augmented_mat.astype("float64" )

   # scale the matrix leaving it triangular
   for row in range(rows - 1 ):
      lowerCamelCase__						       =      augmented_mat[row, row]
      for col in range(row + 1						,     A__ ):
         lowerCamelCase__						       =      augmented_mat[col, row] / pivot
         augmented_mat[col, :] -= factor * augmented_mat[row, :]

   lowerCamelCase__						       =      retroactive_resolution(
       augmented_mat[:, 0:columns]						,     augmented_mat[:, columns : columns + 1] )

   return x


if __name__ == "__main__":
     import doctest

     doctest.testmod()




