import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

__snake_case			      =    {
    '''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						)->  Dict:






      '''simple docstring'''





      for attribute in key.split('''.'''						):
            UpperCAmelCase    : Optional[Any]      =getattr(__lowerCAmelCase		,					__lowerCAmelCase						)

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

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





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase						)->  str:






      '''simple docstring'''





      UpperCAmelCase    : Tuple      =[]
      UpperCAmelCase    : Any      =fairseq_model.state_dict()

      UpperCAmelCase    : Dict      =hf_model.feature_extractor

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

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






      '''simple docstring'''





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





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase    : int      =HubertConfig()
      UpperCAmelCase    : List[str]      =model.config

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

      return config





@torch.no_grad()
def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=None						)->  Dict:






      '''simple docstring'''





      UpperCAmelCase    : List[str]      =distilhubert().model.model

      if config_path is not None:
            UpperCAmelCase    : Dict      =HubertConfig.from_pretrained(__lowerCAmelCase						)
      else:
            UpperCAmelCase    : Any      =convert_config(__lowerCAmelCase						)
      UpperCAmelCase    : str      =model.eval()

      UpperCAmelCase    : str      =WavaVecaFeatureExtractor(
          feature_size=1		,					sampling_rate=1_60_00		,					padding_value=0		,					do_normalize=__lowerCAmelCase		,					return_attention_mask=__lowerCAmelCase		,					)
      UpperCAmelCase    : Dict      =HubertModel(__lowerCAmelCase						)

      recursively_load_weights(__lowerCAmelCase		,					__lowerCAmelCase						)

      feature_extractor.save_pretrained(__lowerCAmelCase						)
      hf_model.save_pretrained(__lowerCAmelCase						)


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

__snake_case			      =    [[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 			__snake_case   :
 def __init__(  self		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =name
       UpperCAmelCase    : Tuple      =[]
       UpperCAmelCase    : str      =[]
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       self.inbound.append(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       self.outbound.append(snake_case__						)






 def __repr__(  self						)	->							Union[str, Any]:
       '''simple docstring'''





       return f'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=3		,					__lowerCAmelCase=0.85						)->  Any:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      ={}
      for node in nodes:
            UpperCAmelCase    : str      =1

      UpperCAmelCase    : str      ={}
      for node in nodes:
            UpperCAmelCase    : int      =len(node.outbound						)

      for i in range(__lowerCAmelCase						):
            print(f'''======= Iteration {i + 1} ======='''						)
            for _, node in enumerate(__lowerCAmelCase						):
                  UpperCAmelCase    : Optional[int]      =(1 - d) + d * sum(
                      ranks[ib] / outbounds[ib] for ib in node.inbound						)
            print(__lowerCAmelCase						)





def   lowerCAmelCase_ (				)->  Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =list(input('''Enter Names of the Nodes: '''						).split()						)

      UpperCAmelCase    : str      =[Node(__lowerCAmelCase						) for name in names]

      for ri, row in enumerate(__lowerCAmelCase						):
            for ci, col in enumerate(__lowerCAmelCase						):
                  if col == 1:
                        nodes[ci].add_inbound(names[ri]						)
                        nodes[ri].add_outbound(names[ci]						)

      print('''======= Nodes ======='''						)
      for node in nodes:
            print(__lowerCAmelCase						)

      page_rank(__lowerCAmelCase						)


if __name__ == "__main__":
     main()
