

'''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         =						logging.get_logger(__name__)

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




def 						UpperCamelCase	(			a		,						a		,						a		,						a		,						a			)				->     Tuple:






     '''simple docstring'''
     for attribute in key.split('''.'''			):
          __magic_name__						       =    getattr(a		,						a			)

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

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




def 						UpperCamelCase	(			a		,						a			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    []
     __magic_name__						       =    fairseq_model.state_dict()

     __magic_name__						       =    hf_model.feature_extractor

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






     '''simple docstring'''
     __magic_name__						       =    full_name.split('''conv_layers.'''			)[-1]
     __magic_name__						       =    name.split('''.'''			)
     __magic_name__						       =    int(items[0]			)
     __magic_name__						       =    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.'''
               )
               __magic_name__						       =    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.'''
               )
               __magic_name__						       =    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."
               )
               __magic_name__						       =    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.'''
               )
               __magic_name__						       =    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 						UpperCamelCase	(			a		,						a		,						a=None			)				->     Tuple:






     '''simple docstring'''
     # load the pre-trained checkpoints
     __magic_name__						       =    torch.load(a			)
     __magic_name__						       =    WavLMConfigOrig(checkpoint['''cfg''']			)
     __magic_name__						       =    WavLMOrig(a			)
     model.load_state_dict(checkpoint['''model''']			)
     model.eval()

     if config_path is not None:
          __magic_name__						       =    WavLMConfig.from_pretrained(a			)
     else:
          __magic_name__						       =    WavLMConfig()

     __magic_name__						       =    WavLMModel(a			)

     recursively_load_weights(a		,						a			)

     hf_wavlm.save_pretrained(a			)


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









'''simple docstring'''




from pathlib import Path

import fire
from tqdm import tqdm




def 						UpperCamelCase	(			a="ro"		,						a="en"		,						a="wmt16"		,						a=None			)				->     None:






     '''simple docstring'''
     try:
          import datasets
     except (ModuleNotFoundError, ImportError):
          raise ImportError('''run pip install datasets'''			)
     __magic_name__						       =    F'''{src_lang}-{tgt_lang}'''
     print(F'''Converting {dataset}-{pair}'''			)
     __magic_name__						       =    datasets.load_dataset(a		,						a			)
     if save_dir is None:
          __magic_name__						       =    F'''{dataset}-{pair}'''
     __magic_name__						       =    Path(a			)
     save_dir.mkdir(exist_ok=a			)

     for split in ds.keys():
          print(F'''Splitting {split} with {ds[split].num_rows} records'''			)

          # to save to val.source, val.target like summary datasets
          __magic_name__						       =    '''val''' if split == '''validation''' else split
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.source'''			)
          __magic_name__						       =    save_dir.joinpath(F'''{fn}.target'''			)
          __magic_name__						       =    src_path.open('''w+'''			)
          __magic_name__						       =    tgt_path.open('''w+'''			)

          # reader is the bottleneck so writing one record at a time doesn't slow things down
          for x in tqdm(ds[split]			):
               __magic_name__						       =    x['''translation''']
               src_fp.write(ex[src_lang] + '''\n'''			)
               tgt_fp.write(ex[tgt_lang] + '''\n'''			)

     print(F'''Saved {dataset} dataset to {save_dir}'''			)


if __name__ == "__main__":
      fire.Fire(download_wmt_dataset)






