



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()
a_								=       logging.get_logger(__name__)

a_								=       {
    """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""",
}
a_								=       [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]





def 		a__      (		_UpperCamelCase      :			Any			,_UpperCamelCase      :			List[str]			,_UpperCamelCase      :			str			,_UpperCamelCase      :			List[str]			,_UpperCamelCase      :			Any				):
   for attribute in key.split('''.'''				):
      __lowerCamelCase    				=     getattr(_UpperCamelCase			,_UpperCamelCase				)

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

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





def 		a__      (		_UpperCamelCase      :			int			,_UpperCamelCase      :			Optional[Any]				):
   __lowerCamelCase    				=     []
   __lowerCamelCase    				=     fairseq_model.state_dict()

   __lowerCamelCase    				=     hf_model.feature_extractor

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

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

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





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





@torch.no_grad()
def 		a__      (		_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Union[str, Any]=None				):
   # load the pre-trained checkpoints
   __lowerCamelCase    				=     torch.load(_UpperCamelCase				)
   __lowerCamelCase    				=     WavLMConfigOrig(checkpoint['''cfg''']				)
   __lowerCamelCase    				=     WavLMOrig(_UpperCamelCase				)
   model.load_state_dict(checkpoint['''model''']				)
   model.eval()

   if config_path is not None:
      __lowerCamelCase    				=     WavLMConfig.from_pretrained(_UpperCamelCase				)
   else:
      __lowerCamelCase    				=     WavLMConfig()

   __lowerCamelCase    				=     WavLMModel(_UpperCamelCase				)

   recursively_load_weights(_UpperCamelCase			,_UpperCamelCase				)

   hf_wavlm.save_pretrained(_UpperCamelCase				)


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











a_								=       {
    0: """0""",
    1: """1""",
    2: """2""",
    3: """3""",
    4: """4""",
    5: """5""",
    6: """6""",
    7: """7""",
    8: """8""",
    9: """9""",
    10: """a""",
    11: """b""",
    12: """c""",
    13: """d""",
    14: """e""",
    15: """f""",
}





def 		a__      (		_UpperCamelCase      :			float				):
   assert type(_UpperCamelCase				) in (int, float) and decimal == int(_UpperCamelCase				)
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   __lowerCamelCase    				=     ''''''
   __lowerCamelCase    				=     False
   if decimal < 0:
      __lowerCamelCase    				=     True
      decimal *= -1
   while decimal > 0:
      __lowerCamelCase				,__lowerCamelCase    				=     divmod(_UpperCamelCase			,16				)
      __lowerCamelCase    				=     values[remainder] + hexadecimal
   __lowerCamelCase    				=     '''0x''' + hexadecimal
   if negative:
      __lowerCamelCase    				=     '''-''' + hexadecimal
   return hexadecimal


if __name__ == "__main__":
  import doctest

  doctest.testmod()






