'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_A      :				Tuple 				=    logging.get_logger(__name__)

_A      :				Tuple 				=    {
    '''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_						( snake_case_	:	List[str]						,		snake_case_	:	str						,		snake_case_	:	str						,		snake_case_	:	Optional[Any]						,		snake_case_	:	Dict				) ->					Union[str, Any]:






 '''simple docstring'''

 for attribute in key.split("""."""				):
  __lowerCAmelCase    							=						getattr(snake_case_						,		snake_case_				)

 if weight_type is not None:
  __lowerCAmelCase    							=						getattr(snake_case_						,		snake_case_				).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_						( snake_case_	:	Dict						,		snake_case_	:	Dict				) ->					Dict:






 '''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(
       snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		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(snake_case_				)[0].split("""."""				)[-2]
      __lowerCAmelCase    							=						mapped_key.replace("""*"""						,		snake_case_				)
     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(snake_case_						,		snake_case_						,		snake_case_						,		snake_case_						,		snake_case_				)
    continue
  if not is_used:
   unused_weights.append(snake_case_				)

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







def 					UpperCamelCase_						( snake_case_	:	Any						,		snake_case_	:	List[str]						,		snake_case_	:	int						,		snake_case_	:	int						,		snake_case_	:	Union[str, Any]				) ->					str:






 '''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(snake_case_				)







def 					UpperCamelCase_						( snake_case_	:	Optional[Any]				) ->					Dict:






 '''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    							=						0.0_2
 __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(snake_case_				)
 __lowerCAmelCase    							=						fs_config.encoder_layers

 return config







@torch.no_grad()
def 					UpperCamelCase_						( snake_case_	:	Dict						,		snake_case_	:	Any=None				) ->					Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase    							=						distilhubert().model.model

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

 __lowerCAmelCase    							=						WavaVecaFeatureExtractor(
     feature_size=1						,		sampling_rate=1_60_00						,		padding_value=0						,		do_normalize=snake_case_						,		return_attention_mask=snake_case_						,		)
 __lowerCAmelCase    							=						HubertModel(snake_case_				)

 recursively_load_weights(snake_case_						,		snake_case_				)

 feature_extractor.save_pretrained(snake_case_				)
 hf_model.save_pretrained(snake_case_				)


if __name__ == "__main__":
 _A      :				Tuple 				=    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      :				int 				=    parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''

def 					UpperCamelCase_						( snake_case_	:	int				) ->					int:






 '''simple docstring'''

 if number != int(snake_case_				):
  raise ValueError("""factorial() only accepts integral values"""				)
 if number < 0:
  raise ValueError("""factorial() not defined for negative values"""				)
 __lowerCAmelCase    							=						1
 for i in range(1						,		number + 1				):
  value *= i
 return value







def 					UpperCamelCase_						( snake_case_	:	int				) ->					int:






 '''simple docstring'''

 if not isinstance(snake_case_						,		snake_case_				):
  raise ValueError("""factorial() only accepts integral values"""				)
 if n < 0:
  raise ValueError("""factorial() not defined for negative values"""				)
 return 1 if n == 0 or n == 1 else n * factorial(n - 1				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _A      :				List[Any] 				=    int(input('''Enter a positive integer: ''').strip() or 0)
 print(f'factorial{n} is {factorial(n)}')






