



"""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()
_lowerCAmelCase    : Optional[int]							=							logging.get_logger(__name__)

_lowerCAmelCase    : List[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",
}
_lowerCAmelCase    : Union[str, Any]							=							[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						List[Any]    )  ->       Optional[int]:





     '''simple docstring'''

     for attribute in key.split("."    ):
          _UpperCAmelCase      :     List[str]        =   getattr(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

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

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Tuple:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   []
     _UpperCAmelCase      :     Optional[Any]        =   fairseq_model.state_dict()

     _UpperCAmelCase      :     Tuple        =   hf_model.feature_extractor

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

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

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

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						Optional[int]    )  ->       str:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   full_name.split("conv_layers."    )[-1]
     _UpperCAmelCase      :     str        =   name.split("."    )
     _UpperCAmelCase      :     Dict        =   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[Any]        =   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      :     Dict        =   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      :     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.'
               )
               _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(SCREAMING_SNAKE_CASE__    )

@torch.no_grad()
def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[int]						,  SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						Dict=None    )  ->       str:





     '''simple docstring'''

     _UpperCAmelCase      :     Any        =   torch.load(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     List[str]        =   WavLMConfigOrig(checkpoint["cfg"]    )
     _UpperCAmelCase      :     str        =   WavLMOrig(SCREAMING_SNAKE_CASE__    )
     model.load_state_dict(checkpoint["model"]    )
     model.eval()

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

     _UpperCAmelCase      :     Optional[Any]        =   WavLMModel(SCREAMING_SNAKE_CASE__    )

     recursively_load_weights(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

     hf_wavlm.save_pretrained(SCREAMING_SNAKE_CASE__    )


if __name__ == "__main__":
 _lowerCAmelCase    : List[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")
 _lowerCAmelCase    : List[str]							=							parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_lowerCAmelCase    : Union[str, Any]							=							{}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Optional[Any]							=							["GPTSw3Tokenizer"]


if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_swa import GPTSwaTokenizer

else:
 import sys

 _lowerCAmelCase    : List[Any]							=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
