"""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_:					int     =					logging.get_logger(__name__)

lowerCAmelCase_:					List[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_:					Tuple     =					[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     for attribute in key.split("."      ):
          lowercase__				 =							getattr(A    ,				A      )

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

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







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							[]
     lowercase__				 =							fairseq_model.state_dict()

     lowercase__				 =							hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase__				 =							False
          if "conv_layers" in name:
               load_conv_layer(
                   A    ,				A    ,				A    ,				A    ,				hf_model.config.feat_extract_norm == "group"    ,				)
               lowercase__				 =							True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                         lowercase__				 =							True
                         if "*" in mapped_key:
                              lowercase__				 =							name.split(A      )[0].split("."      )[-2]
                              lowercase__				 =							mapped_key.replace("*"    ,				A      )
                         if "weight_g" in name:
                              lowercase__				 =							"weight_g"
                         elif "weight_v" in name:
                              lowercase__				 =							"weight_v"
                         elif "bias" in name and "relative_attention_bias" not in name:
                              lowercase__				 =							"bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowercase__				 =							"weight"
                         else:
                              lowercase__				 =							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 					__a   (		A    ,				A    ,				A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							full_name.split("conv_layers."      )[-1]
     lowercase__				 =							name.split("."      )
     lowercase__				 =							int(items[0]      )
     lowercase__				 =							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.'''
               )
               lowercase__				 =							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.'''
               )
               lowercase__				 =							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."
               )
               lowercase__				 =							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.'''
               )
               lowercase__				 =							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 					__a   (		A    ,				A    ,				A=None      ):






     '''simple docstring'''



     lowercase__				 =							torch.load(A      )
     lowercase__				 =							WavLMConfigOrig(checkpoint["cfg"]      )
     lowercase__				 =							WavLMOrig(A      )
     model.load_state_dict(checkpoint["model"]      )
     model.eval()

     if config_path is not None:
          lowercase__				 =							WavLMConfig.from_pretrained(A      )
     else:
          lowercase__				 =							WavLMConfig()

     lowercase__				 =							WavLMModel(A      )

     recursively_load_weights(A    ,				A      )

     hf_wavlm.save_pretrained(A      )


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_:					Dict     =					parser.parse_args()
 convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





"""simple docstring"""


from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				_UpperCAmelCase = False,				_UpperCAmelCase = False,				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            _UpperCAmelCase,				split=_UpperCAmelCase,				features=_UpperCAmelCase,				cache_dir=_UpperCAmelCase,				keep_in_memory=_UpperCAmelCase,				streaming=_UpperCAmelCase,				num_proc=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							path_or_paths if isinstance(_UpperCAmelCase,				_UpperCAmelCase  ) else {self.split: path_or_paths}
        lowercase__				 =							Text(
            cache_dir=_UpperCAmelCase,				data_files=_UpperCAmelCase,				features=_UpperCAmelCase,				**_UpperCAmelCase,				)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        if self.streaming:
             lowercase__				 =							self.builder.as_streaming_dataset(split=self.split  )
        # Build regular (map-style) dataset
        else:
             lowercase__				 =							None
             lowercase__				 =							None
             lowercase__				 =							None
             lowercase__				 =							None

             self.builder.download_and_prepare(
                 download_config=_UpperCAmelCase,				download_mode=_UpperCAmelCase,				verification_mode=_UpperCAmelCase,				base_path=_UpperCAmelCase,				num_proc=self.num_proc,				)
             lowercase__				 =							self.builder.as_dataset(
                 split=self.split,				verification_mode=_UpperCAmelCase,				in_memory=self.keep_in_memory  )
        return dataset




