




'''simple docstring'''



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase_				:     Dict     			=						logging.get_logger(__name__)

UpperCAmelCase_				:     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							:   Tuple      ,		_lowerCamelCase							:   Dict      ,		_lowerCamelCase							:   Union[str, Any]      ,		_lowerCamelCase							:   str      ,		_lowerCamelCase							:   Union[str, Any]		)->      Optional[int]:




     '''simple docstring'''





     for attribute in key.split('''.'''		):
          __snake_case			    =     getattr(_lowerCamelCase      ,		_lowerCamelCase		)

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

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







def 	_UpperCamelCase       (_lowerCamelCase							:   Any      ,		_lowerCamelCase							:   Optional[int]		)->      int:




     '''simple docstring'''





     __snake_case			    =     []
     __snake_case			    =     fairseq_model.state_dict()

     __snake_case			    =     hf_model.feature_extractor

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

                    if key in name:
                         __snake_case			    =     True
                         if "*" in mapped_key:
                              __snake_case			    =     name.split(_lowerCamelCase		)[0].split('''.'''		)[-2]
                              __snake_case			    =     mapped_key.replace('''*'''      ,		_lowerCamelCase		)
                         if "weight_g" in name:
                              __snake_case			    =     '''weight_g'''
                         elif "weight_v" in name:
                              __snake_case			    =     '''weight_v'''
                         elif "weight" in name:
                              __snake_case			    =     '''weight'''
                         elif "bias" in name:
                              __snake_case			    =     '''bias'''
                         else:
                              __snake_case			    =     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							:   Optional[Any]      ,		_lowerCamelCase							:   Any      ,		_lowerCamelCase							:   Union[str, Any]      ,		_lowerCamelCase							:   List[Any]      ,		_lowerCamelCase							:   Any		)->      Union[str, Any]:




     '''simple docstring'''





     __snake_case			    =     full_name.split('''conv_layers.'''		)[-1]
     __snake_case			    =     name.split('''.'''		)
     __snake_case			    =     int(items[0]		)
     __snake_case			    =     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			    =     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			    =     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			    =     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			    =     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		)->      List[Any]:




     '''simple docstring'''





     __snake_case			    =     HubertConfig()
     __snake_case			    =     model.config

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

     return config







@torch.no_grad()
def 	_UpperCamelCase       (_lowerCamelCase							:   Optional[int]      ,		_lowerCamelCase							:   Tuple=None		)->      List[str]:




     '''simple docstring'''





     __snake_case			    =     distilhubert().model.model

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

     __snake_case			    =     WavaVecaFeatureExtractor(
         feature_size=1      ,		sampling_rate=1_60_00      ,		padding_value=0      ,		do_normalize=_lowerCamelCase      ,		return_attention_mask=_lowerCamelCase      ,		)
     __snake_case			    =     HubertModel(_lowerCamelCase		)

     recursively_load_weights(_lowerCamelCase      ,		_lowerCamelCase		)

     feature_extractor.save_pretrained(_lowerCamelCase		)
     hf_model.save_pretrained(_lowerCamelCase		)


if __name__ == "__main__":
     UpperCAmelCase_				:     Optional[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_				:     Optional[int]     			=						parser.parse_args()
     convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)












'''simple docstring'''



from __future__ import annotations







def 	_UpperCamelCase       (_lowerCamelCase							:   int      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   list[int]      ,		_lowerCamelCase							:   int		)->      int:




     '''simple docstring'''





     if counter == 0 or capacity == 0:
          return 0

     # If weight of the nth item is more than Knapsack of capacity,
     #   then this item cannot be included in the optimal solution,
     # else return the maximum of two cases:
     #   (1) nth item included
     #   (2) not included
     if weights[counter - 1] > capacity:
          return knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
     else:
          __snake_case			    =     capacity - weights[counter - 1]
          __snake_case			    =     values[counter - 1] + knapsack(
              _lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          __snake_case			    =     knapsack(_lowerCamelCase      ,		_lowerCamelCase      ,		_lowerCamelCase      ,		counter - 1		)
          return max(_lowerCamelCase      ,		_lowerCamelCase		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






