


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a					:				Any       =      logging.get_logger(__name__)

a					:				Optional[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  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->Any:


 '''simple docstring'''
 for attribute in key.split('''.'''							):
  snake_case :			str							=   getattr(__lowerCamelCase			,  __lowerCamelCase							)

 if weight_type is not None:
  snake_case :			str							=   getattr(__lowerCamelCase			,  __lowerCamelCase							).shape
 else:
  snake_case :			Tuple							=   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":
  snake_case :			Union[str, Any]							=   value
 elif weight_type == "weight_g":
  snake_case :			List[str]							=   value
 elif weight_type == "weight_v":
  snake_case :			List[str]							=   value
 elif weight_type == "bias":
  snake_case :			str							=   value
 else:
  snake_case :			Union[str, Any]							=   value

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






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase							)		->Optional[int]:


 '''simple docstring'''
 snake_case :			Dict							=   []
 snake_case :			Optional[int]							=   fairseq_model.state_dict()

 snake_case :			Optional[int]							=   hf_model.feature_extractor

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

    if key in name:
     snake_case :			str							=   True
     if "*" in mapped_key:
      snake_case :			Dict							=   name.split(__lowerCamelCase							)[0].split('''.'''							)[-2]
      snake_case :			List[str]							=   mapped_key.replace('''*'''			,  __lowerCamelCase							)
     if "weight_g" in name:
      snake_case :			List[Any]							=   '''weight_g'''
     elif "weight_v" in name:
      snake_case :			Any							=   '''weight_v'''
     elif "weight" in name:
      snake_case :			Tuple							=   '''weight'''
     elif "bias" in name:
      snake_case :			Dict							=   '''bias'''
     else:
      snake_case :			List[Any]							=   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  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->Optional[int]:


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






def  _UpperCAmelCase						( __lowerCamelCase							)		->Any:


 '''simple docstring'''
 snake_case :			Optional[int]							=   HubertConfig()
 snake_case :			List[str]							=   model.config

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

 return config






@torch.no_grad()
def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase=None							)		->Union[str, Any]:


 '''simple docstring'''
 snake_case :			Optional[Any]							=   distilhubert().model.model

 if config_path is not None:
  snake_case :			Union[str, Any]							=   HubertConfig.from_pretrained(__lowerCamelCase							)
 else:
  snake_case :			Optional[int]							=   convert_config(__lowerCamelCase							)
 snake_case :			Any							=   model.eval()

 snake_case :			Dict							=   WavaVecaFeatureExtractor(
     feature_size=1			,  sampling_rate=16000			,  padding_value=0			,  do_normalize=__lowerCamelCase			,  return_attention_mask=__lowerCamelCase			,  )
 snake_case :			Union[str, Any]							=   HubertModel(__lowerCamelCase							)

 recursively_load_weights(__lowerCamelCase			,  __lowerCamelCase							)

 feature_extractor.save_pretrained(__lowerCamelCase							)
 hf_model.save_pretrained(__lowerCamelCase							)


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










from typing import Any

import numpy as np






def  _UpperCAmelCase						( __lowerCamelCase							)		->bool:


 '''simple docstring'''
 assert np.shape(__lowerCamelCase							)[0] == np.shape(__lowerCamelCase							)[1]

 # If matrix not symmetric, exit right away.
 if np.allclose(__lowerCamelCase			,  matrix.T							) is False:
  return False

 # Get eigenvalues and eignevectors for a symmetric matrix.
 snake_case	,       snake_case :			int							=   np.linalg.eigh(__lowerCamelCase							)

 # Check sign of all eigenvalues.
 # np.all returns a value of type np.bool_
 return bool(np.all(eigen_values > 0							)							)






def  _UpperCAmelCase						( __lowerCamelCase							)		->Any:


 '''simple docstring'''
 snake_case :			Dict							=   np.random.randn(__lowerCamelCase			,  __lowerCamelCase							)
 snake_case :			List[Any]							=   np.dot(__lowerCamelCase			,  random_matrix.T							)
 assert _is_matrix_spd(__lowerCamelCase							)
 return spd_matrix






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase = 1000			,  __lowerCamelCase = 1E-8			,  )		->Any:


 '''simple docstring'''
 assert np.shape(__lowerCamelCase							)[0] == np.shape(__lowerCamelCase							)[1]
 assert np.shape(__lowerCamelCase							)[0] == np.shape(__lowerCamelCase							)[0]
 assert _is_matrix_spd(__lowerCamelCase							)

 # Initialize solution guess, residual, search direction.
 snake_case :			Any							=   np.zeros((np.shape(__lowerCamelCase							)[0], 1)							)
 snake_case :			Optional[int]							=   np.copy(__lowerCamelCase							)
 snake_case :			Optional[Any]							=   np.copy(__lowerCamelCase							)

 # Set initial errors in solution guess and residual.
 snake_case :			Optional[Any]							=   1E9
 snake_case :			List[Any]							=   1E9
 snake_case :			List[str]							=   1E9

 # Set iteration counter to threshold number of iterations.
 snake_case :			List[str]							=   0

 while error > tol:
  # Save this value so we only calculate the matrix-vector product once.
  snake_case :			str							=   np.dot(__lowerCamelCase			,  __lowerCamelCase							)

  # The main algorithm.

  # Update search direction magnitude.
  snake_case :			List[str]							=   np.dot(ra.T			,  __lowerCamelCase							) / np.dot(pa.T			,  __lowerCamelCase							)
  # Update solution guess.
  snake_case :			Optional[int]							=   xa + alpha * pa
  # Calculate new residual.
  snake_case :			str							=   ra - alpha * w
  # Calculate new Krylov subspace scale.
  snake_case :			List[str]							=   np.dot(r.T			,  __lowerCamelCase							) / np.dot(ra.T			,  __lowerCamelCase							)
  # Calculate new A conjuage search direction.
  snake_case :			List[Any]							=   r + beta * pa

  # Calculate errors.
  snake_case :			List[Any]							=   np.linalg.norm(r - ra							)
  snake_case :			Any							=   np.linalg.norm(x - xa							)
  snake_case :			str							=   np.maximum(__lowerCamelCase			,  __lowerCamelCase							)

  # Update variables.
  snake_case :			Tuple							=   np.copy(__lowerCamelCase							)
  snake_case :			Tuple							=   np.copy(__lowerCamelCase							)
  snake_case :			List[str]							=   np.copy(__lowerCamelCase							)

  # Update number of iterations.
  iterations += 1
  if iterations > max_iterations:
   break

 return x






def  _UpperCAmelCase						( )		->None:


 '''simple docstring'''
 snake_case :			str							=   3
 snake_case :			Dict							=   _create_spd_matrix(__lowerCamelCase							)
 snake_case :			str							=   np.random.randn(__lowerCamelCase			,  1							)
 snake_case :			str							=   np.dot(__lowerCamelCase			,  __lowerCamelCase							)

 # Numpy solution.
 snake_case :			Any							=   np.linalg.solve(__lowerCamelCase			,  __lowerCamelCase							)

 # Our implementation.
 snake_case :			List[str]							=   conjugate_gradient(__lowerCamelCase			,  __lowerCamelCase							)

 # Ensure both solutions are close to x_true (and therefore one another).
 assert np.linalg.norm(x_numpy - x_true							) <= 1E-6
 assert np.linalg.norm(x_conjugate_gradient - x_true							) <= 1E-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_conjugate_gradient()






