





'''simple docstring'''




from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike


class 					SCREAMING_SNAKE_CASE       (a__				):


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

          '''simple docstring'''






          __A		:					int						=			path_or_paths
          __A		:					List[str]						=			split if split or isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else 'train'
          __A		:					Any						=			features
          __A		:					Dict						=			cache_dir
          __A		:					List[str]						=			keep_in_memory
          __A		:					Union[str, Any]						=			streaming
          __A		:					Tuple						=			num_proc
          __A		:					Union[str, Any]						=			kwargs


     @abstractmethod
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass




class 					SCREAMING_SNAKE_CASE       (a__				):


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

          '''simple docstring'''






          __A		:					Dict						=			features
          __A		:					Any						=			cache_dir
          __A		:					str						=			keep_in_memory
          __A		:					Optional[Any]						=			streaming
          __A		:					List[str]						=			num_proc
          __A		:					List[Any]						=			kwargs


     @abstractmethod
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass






'''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()
lowercase__       : Union[str, Any]         =       logging.get_logger(__name__)

lowercase__       : int         =       {
    '''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''',
}
lowercase__       : Dict         =       [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					Tuple					,				__snake_case    :					Any					,				__snake_case    :					List[str]     )	->	Union[str, Any]:
     for attribute in key.split('.'     ):
          __A		:					int						=			getattr(__snake_case					,				__snake_case     )

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

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






def 			_lowerCAmelCase     (    __snake_case    :					Any					,				__snake_case    :					List[str]     )	->	List[Any]:
     __A		:					Optional[Any]						=			[]
     __A		:					Any						=			fairseq_model.state_dict()

     __A		:					Union[str, Any]						=			hf_model.feature_extractor

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

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

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






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






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					List[str]					,				__snake_case    :					Tuple=None     )	->	Any:
     # load the pre-trained checkpoints
     __A		:					List[str]						=			torch.load(__snake_case     )
     __A		:					Dict						=			WavLMConfigOrig(checkpoint['cfg']     )
     __A		:					Optional[int]						=			WavLMOrig(__snake_case     )
     model.load_state_dict(checkpoint['model']     )
     model.eval()

     if config_path is not None:
          __A		:					List[Any]						=			WavLMConfig.from_pretrained(__snake_case     )
     else:
          __A		:					Dict						=			WavLMConfig()

     __A		:					Optional[Any]						=			WavLMModel(__snake_case     )

     recursively_load_weights(__snake_case					,				__snake_case     )

     hf_wavlm.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Tuple         =       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''')
  lowercase__       : Any         =       parser.parse_args()
  convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)