





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()
__lowerCamelCase :  Optional[Any]										=  logging.get_logger(__name__)

__lowerCamelCase :  str										=  {
    """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""",
}
__lowerCamelCase :  str										=  [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Dict:
 for attribute in key.split("."  ):
  UpperCamelCase			:      Tuple 			=     getattr(_lowerCAmelCase							,     _lowerCAmelCase  )

 if weight_type is not None:
  UpperCamelCase			:      Dict 			=     getattr(_lowerCAmelCase							,     _lowerCAmelCase  ).shape
 else:
  UpperCamelCase			:      Optional[int] 			=     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			:      Optional[Any] 			=     value
 elif weight_type == "weight_g":
  UpperCamelCase			:      Dict 			=     value
 elif weight_type == "weight_v":
  UpperCamelCase			:      Union[str, Any] 			=     value
 elif weight_type == "bias":
  UpperCamelCase			:      Optional[Any] 			=     value
 else:
  UpperCamelCase			:      List[str] 			=     value

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



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase  )		->				Tuple:
 UpperCamelCase			:      Optional[int] 			=     []
 UpperCamelCase			:      Tuple 			=     fairseq_model.state_dict()

 UpperCamelCase			:      Any 			=     hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCamelCase			:      Optional[int] 			=     False
  if "conv_layers" in name:
   load_conv_layer(
       _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     hf_model.config.feat_extract_norm == "group"							,     )
   UpperCamelCase			:      Dict 			=     True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split("w2v_model."  )[-1] == name.split("."  )[0]:
     UpperCamelCase			:      Any 			=     True
     if "*" in mapped_key:
      UpperCamelCase			:      Any 			=     name.split(_lowerCAmelCase  )[0].split("."  )[-2]
      UpperCamelCase			:      Optional[Any] 			=     mapped_key.replace("*"							,     _lowerCAmelCase  )
     if "weight_g" in name:
      UpperCamelCase			:      Optional[Any] 			=     "weight_g"
     elif "weight_v" in name:
      UpperCamelCase			:      Any 			=     "weight_v"
     elif "bias" in name and "relative_attention_bias" not in name:
      UpperCamelCase			:      Optional[int] 			=     "bias"
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      UpperCamelCase			:      Tuple 			=     "weight"
     else:
      UpperCamelCase			:      Dict 			=     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 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Tuple:
 UpperCamelCase			:      str 			=     full_name.split("conv_layers."  )[-1]
 UpperCamelCase			:      Optional[int] 			=     name.split("."  )
 UpperCamelCase			:      str 			=     int(items[0]  )
 UpperCamelCase			:      Optional[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."""
   )
   UpperCamelCase			:      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			:      int 			=     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			:      str 			=     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			:      Optional[Any] 			=     value
   logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
 else:
  unused_weights.append(_lowerCAmelCase  )



@torch.no_grad()
def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase=None  )		->				List[str]:
 # load the pre-trained checkpoints
 UpperCamelCase			:      Optional[Any] 			=     torch.load(_lowerCAmelCase  )
 UpperCamelCase			:      Optional[Any] 			=     WavLMConfigOrig(checkpoint["cfg"]  )
 UpperCamelCase			:      Dict 			=     WavLMOrig(_lowerCAmelCase  )
 model.load_state_dict(checkpoint["model"]  )
 model.eval()

 if config_path is not None:
  UpperCamelCase			:      Optional[int] 			=     WavLMConfig.from_pretrained(_lowerCAmelCase  )
 else:
  UpperCamelCase			:      Tuple 			=     WavLMConfig()

 UpperCamelCase			:      List[Any] 			=     WavLMModel(_lowerCAmelCase  )

 recursively_load_weights(_lowerCAmelCase							,     _lowerCAmelCase  )

 hf_wavlm.save_pretrained(_lowerCAmelCase  )


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







import math

import tensorflow as tf
from packaging import version



def 	A_       (  _lowerCAmelCase  )		->				Any:
 UpperCamelCase			:      List[Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      Any 			=     0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0  )							,     x.dtype  )  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				Dict:
 UpperCamelCase			:      Union[str, Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(math.pi							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      int 			=     0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi  ) * (x + coeff * tf.pow(_lowerCAmelCase							,     3  ))  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      str 			=     tf.convert_to_tensor(_lowerCAmelCase  )

 return x * tf.tanh(tf.math.softplus(_lowerCAmelCase  )  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      Tuple 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.7_978_845_608							,     x.dtype  )

 return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)  ))



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 UpperCamelCase			:      Any 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(1.702							,     x.dtype  )
 return x * tf.math.sigmoid(coeff * x  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 return tf.clip_by_value(_gelu(_lowerCAmelCase  )							,     -10							,     10  )



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase=-1  )		->				str:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     tf.split(_lowerCAmelCase							,     2							,     axis=_lowerCAmelCase  )
 return a * tf.math.sigmoid(_lowerCAmelCase  )


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):



    def 	A_       (  _lowerCAmelCase  )		->				Any:
     return tf.keras.activations.gelu(_lowerCAmelCase							,     approximate=_lowerCAmelCase  )

    __lowerCamelCase :  Optional[int]										=  tf.keras.activations.gelu


    __lowerCamelCase :  int										=  approximate_gelu_wrap
else:
    __lowerCamelCase :  List[Any]										=  _gelu
    __lowerCamelCase :  Optional[Any]										=  _gelu_new


__lowerCamelCase :  Any										=  {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()  )}"""  )
