






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__      :	str       	=      logging.get_logger(__name__)

snake_case__      :	Union[str, Any]       	=      {
    """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__      :	str       	=      [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):
   for attribute in key.split('.'):
      UpperCamelCase_									=     getattr(__lowercase    ,      __lowercase)

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

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






def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     []
   UpperCamelCase_									=     fairseq_model.state_dict()

   UpperCamelCase_									=     hf_model.feature_extractor

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

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

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






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     full_name.split('conv_layers.')[-1]
   UpperCamelCase_									=     name.split('.')
   UpperCamelCase_									=     int(items[0])
   UpperCamelCase_									=     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_									=     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_									=     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_									=     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_									=     value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
   else:
      unused_weights.append(__lowercase)






@torch.no_grad()
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase=None):
   # load the pre-trained checkpoints
   UpperCamelCase_									=     torch.load(__lowercase)
   UpperCamelCase_									=     WavLMConfigOrig(checkpoint['cfg'])
   UpperCamelCase_									=     WavLMOrig(__lowercase)
   model.load_state_dict(checkpoint['model'])
   model.eval()

   if config_path is not None:
      UpperCamelCase_									=     WavLMConfig.from_pretrained(__lowercase)
   else:
      UpperCamelCase_									=     WavLMConfig()

   UpperCamelCase_									=     WavLMModel(__lowercase)

   recursively_load_weights(__lowercase    ,      __lowercase)

   hf_wavlm.save_pretrained(__lowercase)


if __name__ == "__main__":
 snake_case__      :	str       	=      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__      :	str       	=      parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)













def     _snake_case    (__lowercase = 1000):
   UpperCamelCase_		,      UpperCamelCase_									=     1, 1
   UpperCamelCase_									=     2
   while True:
      UpperCamelCase_									=     0
      UpperCamelCase_									=     fa + fa
      UpperCamelCase_		,      UpperCamelCase_									=     fa, f
      index += 1
      for _ in str(__lowercase):
         i += 1
      if i == n:
         break
   return index


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))





