






'''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()
SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=	{
    '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',
}
SCREAMING_SNAKE_CASE						=	[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def 							lowercase_				(    __A :       Optional[Any]					,      __A :       Tuple					,      __A :       Dict					,      __A :       Optional[int]					,      __A :       List[str]			)       ->      Dict:



 """simple docstring"""




 for attribute in key.split('''.'''			):
  lowercase					:   Union[str, Any]				  =getattr(__A					,      __A			)

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

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


def 							lowercase_				(    __A :       int					,      __A :       List[str]			)       ->      Optional[Any]:



 """simple docstring"""




 lowercase					:   List[Any]				  =[]
 lowercase					:   int				  =fairseq_model.state_dict()

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

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



 """simple docstring"""




 lowercase					:   Optional[Any]				  =full_name.split('''conv_layers.'''			)[-1]
 lowercase					:   Dict				  =name.split('''.'''			)
 lowercase					:   List[str]				  =int(items[0]			)
 lowercase					:   Union[str, 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.'
   )
   lowercase					:   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.'
   )
   lowercase					:   Tuple				  =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					:   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.'
   )
   lowercase					:   Optional[Any]				  =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 							lowercase_				(    __A :       Any					,      __A :       Tuple					,      __A :       Dict=None			)       ->      List[Any]:



 """simple docstring"""




 lowercase					:   Optional[Any]				  =torch.load(__A			)
 lowercase					:   Any				  =WavLMConfigOrig(checkpoint['''cfg''']			)
 lowercase					:   int				  =WavLMOrig(__A			)
 model.load_state_dict(checkpoint['''model''']			)
 model.eval()

 if config_path is not None:
  lowercase					:   Tuple				  =WavLMConfig.from_pretrained(__A			)
 else:
  lowercase					:   Any				  =WavLMConfig()

 lowercase					:   Dict				  =WavLMModel(__A			)

 recursively_load_weights(__A					,      __A			)

 hf_wavlm.save_pretrained(__A			)


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











'''simple docstring'''



def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 if digit_amount > 0:
  return round(number - int(__A			)					,      __A			)
 return number - int(__A			)


if __name__ == "__main__":
      print(decimal_isolate(1.53, 0))
      print(decimal_isolate(35.345, 1))
      print(decimal_isolate(35.345, 2))
      print(decimal_isolate(35.345, 3))
      print(decimal_isolate(-14.789, 3))
      print(decimal_isolate(0, 2))
      print(decimal_isolate(-14.123, 1))
      print(decimal_isolate(-14.123, 2))
      print(decimal_isolate(-14.123, 3))



