'''simple docstring'''





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCamelCase    :			int						     =  logging.get_logger(__name__)

UpperCamelCase    :			Dict						     =  {
    """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 SCREAMING_SNAKE_CASE__   (							snake_case    :							Union[str, Any]							,     snake_case    :							Union[str, Any]							,     snake_case    :							List[Any]							,     snake_case    :							str							,     snake_case    :							Any   )					->							str:







     """simple docstring"""



     for attribute in key.split('.'   ):
          a :				Union[str, Any]				=      getattr(lowerCAmelCase_							,     lowerCAmelCase_   )

     if weight_type is not None:
          a :				List[Any]				=      getattr(lowerCAmelCase_							,     lowerCAmelCase_   ).shape
     else:
          a :				Dict				=      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 :				Tuple				=      value
     elif weight_type == "weight_g":
          a :				List[Any]				=      value
     elif weight_type == "weight_v":
          a :				Optional[int]				=      value
     elif weight_type == "bias":
          a :				int				=      value
     else:
          a :				Optional[int]				=      value

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






def SCREAMING_SNAKE_CASE__   (							snake_case    :							List[Any]							,     snake_case    :							int   )					->							List[str]:







     """simple docstring"""
     a :				int				=      []
     a :				Tuple				=      fairseq_model.state_dict()

     a :				Any				=      hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          a :				str				=      False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     lowerCAmelCase_							,     hf_model.config.feat_extract_norm == 'group'							,     )
               a :				List[str]				=      True
          else:
               for key, mapped_key in MAPPING.items():
                    a :				int				=      mapped_key

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







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






def SCREAMING_SNAKE_CASE__   (							snake_case    :							int   )					->							int:







     """simple docstring"""
     a :				Dict				=      HubertConfig()
     a :				Optional[Any]				=      model.config

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

     return config






@torch.no_grad()
def SCREAMING_SNAKE_CASE__   (							snake_case    :							Tuple							,     snake_case    :							Dict=None   )					->							int:







     """simple docstring"""
     a :				Optional[int]				=      distilhubert().model.model

     if config_path is not None:
          a :				Optional[Any]				=      HubertConfig.from_pretrained(lowerCAmelCase_   )
     else:
          a :				Tuple				=      convert_config(lowerCAmelCase_   )
     a :				Tuple				=      model.eval()

     a :				str				=      WavaVecaFeatureExtractor(
         feature_size=1							,     sampling_rate=16_000							,     padding_value=0							,     do_normalize=lowerCAmelCase_							,     return_attention_mask=lowerCAmelCase_							,     )
     a :				Union[str, Any]				=      HubertModel(lowerCAmelCase_   )

     recursively_load_weights(lowerCAmelCase_							,     lowerCAmelCase_   )

     feature_extractor.save_pretrained(lowerCAmelCase_   )
     hf_model.save_pretrained(lowerCAmelCase_   )


if __name__ == "__main__":
      UpperCamelCase    :			Union[str, 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""")
      UpperCamelCase    :			Tuple						     =  parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






def        __a     (    lowerCAmelCase_   :					int				)      -> int:






						'''simple docstring'''



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



def        __a     (    lowerCAmelCase_   :					int				)      -> int:






						'''simple docstring'''



						if not isinstance(lowerCAmelCase_			,lowerCAmelCase_				):
												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				    =						int(input('''Enter a positive integer: ''').strip() or 0)
	print(f'factorial{n} is {factorial(n)}')




