


"""simple docstring"""





import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
snake_case__		:     List[str]    				=				logging.get_logger(__name__)

snake_case__		:     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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
snake_case__		:     Union[str, Any]    				=				[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def      _snake_case						(						_snake_case				:			int ,   _snake_case				:			List[str] ,   _snake_case				:			Optional[Any] ,   _snake_case				:			int ,   _snake_case				:			Tuple			):




      for attribute in key.split('''.'''			):
            lowerCAmelCase					:	List[Any]       =   getattr(__a ,   __a			)

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

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


def      _snake_case						(						_snake_case				:			Dict ,   _snake_case				:			Union[str, Any]			):
      lowerCAmelCase					:	Any       =   []
      lowerCAmelCase					:	int       =   fairseq_model.state_dict()

      lowerCAmelCase					:	Any       =   hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            lowerCAmelCase					:	Optional[int]       =   False
            if "conv_layers" in name:
                  load_conv_layer(
                      __a ,   __a ,   __a ,   __a ,   hf_model.config.feat_extract_norm == '''group''' ,   )
                  lowerCAmelCase					:	int       =   True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split('''w2v_model.'''			)[-1] == name.split('''.'''			)[0]:
                              lowerCAmelCase					:	List[Any]       =   True
                              if "*" in mapped_key:
                                    lowerCAmelCase					:	Optional[Any]       =   name.split(__a			)[0].split('''.'''			)[-2]
                                    lowerCAmelCase					:	int       =   mapped_key.replace('''*''' ,   __a			)
                              if "weight_g" in name:
                                    lowerCAmelCase					:	Any       =   '''weight_g'''
                              elif "weight_v" in name:
                                    lowerCAmelCase					:	Tuple       =   '''weight_v'''
                              elif "bias" in name and "relative_attention_bias" not in name:
                                    lowerCAmelCase					:	int       =   '''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    lowerCAmelCase					:	Any       =   '''weight'''
                              else:
                                    lowerCAmelCase					:	List[Any]       =   None

                              set_recursively(__a ,   __a ,   __a ,   __a ,   __a			)
                        continue
            if not is_used:
                  unused_weights.append(__a			)

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


def      _snake_case						(						_snake_case				:			Any ,   _snake_case				:			str ,   _snake_case				:			Tuple ,   _snake_case				:			Optional[Any] ,   _snake_case				:			Optional[Any]			):
      lowerCAmelCase					:	List[str]       =   full_name.split('''conv_layers.'''			)[-1]
      lowerCAmelCase					:	str       =   name.split('''.'''			)
      lowerCAmelCase					:	Union[str, Any]       =   int(items[0]			)
      lowerCAmelCase					:	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.'''
                  )
                  lowerCAmelCase					:	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.'''
                  )
                  lowerCAmelCase					:	List[Any]       =   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					:	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.'''
                  )
                  lowerCAmelCase					:	List[str]       =   value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
      else:
            unused_weights.append(__a			)


@torch.no_grad()
def      _snake_case						(						_snake_case				:			List[Any] ,   _snake_case				:			Any ,   _snake_case				:			Union[str, Any]=None			):
      lowerCAmelCase					:	Optional[int]       =   torch.load(__a			)
      lowerCAmelCase					:	int       =   WavLMConfigOrig(checkpoint['''cfg''']			)
      lowerCAmelCase					:	Union[str, Any]       =   WavLMOrig(__a			)
      model.load_state_dict(checkpoint['''model''']			)
      model.eval()

      if config_path is not None:
            lowerCAmelCase					:	Optional[int]       =   WavLMConfig.from_pretrained(__a			)
      else:
            lowerCAmelCase					:	Optional[int]       =   WavLMConfig()

      lowerCAmelCase					:	List[Any]       =   WavLMModel(__a			)

      recursively_load_weights(__a ,   __a			)

      hf_wavlm.save_pretrained(__a			)


if __name__ == "__main__":
 snake_case__		:     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('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 snake_case__		:     Any    				=				parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






"""simple docstring"""





def      _snake_case						(						_snake_case				:			int ,   _snake_case				:			int			):
      return base * power(_snake_case ,   (exponent - 1)			) if exponent else 1


if __name__ == "__main__":
 print('''Raise base to the power of exponent using recursion...''')
 snake_case__		:     Union[str, Any]    				=				int(input('''Enter the base: ''').strip())
 snake_case__		:     Optional[Any]    				=				int(input('''Enter the exponent: ''').strip())
 snake_case__		:     Any    				=				power(base, abs(exponent))
 if exponent < 0:  # power() does not properly deal w/ negative exponents
  snake_case__		:     Dict    				=				1 / result
 print(f"""{base} to the power of {exponent} is {result}""")


