

'''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__			    =			logging.get_logger(__name__)

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




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_	):





 for attribute in key.split('.'	):
  UpperCAmelCase       :	Optional[Any]											= getattr(_snake_case  ,       _snake_case	)

 if weight_type is not None:
  UpperCAmelCase       :	Tuple											= getattr(_snake_case  ,       _snake_case	).shape
 else:
  UpperCAmelCase       :	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":
  UpperCAmelCase       :	int											= value
 elif weight_type == "weight_g":
  UpperCAmelCase       :	List[str]											= value
 elif weight_type == "weight_v":
  UpperCAmelCase       :	str											= value
 elif weight_type == "bias":
  UpperCAmelCase       :	Any											= value
 else:
  UpperCAmelCase       :	Dict											= value

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




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_	):
 UpperCAmelCase       :	List[Any]											= []
 UpperCAmelCase       :	str											= fairseq_model.state_dict()

 UpperCAmelCase       :	Optional[Any]											= hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCAmelCase       :	List[str]											= False
  if "conv_layers" in name:
   load_conv_layer(
       _snake_case  ,       _snake_case  ,       _snake_case  ,       _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       :	str											= True
     if "*" in mapped_key:
      UpperCAmelCase       :	Union[str, Any]											= name.split(_snake_case	)[0].split('.'	)[-2]
      UpperCAmelCase       :	str											= mapped_key.replace('*'  ,       _snake_case	)
     if "weight_g" in name:
      UpperCAmelCase       :	Dict											= '''weight_g'''
     elif "weight_v" in name:
      UpperCAmelCase       :	List[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       :	List[Any]											= '''weight'''
     else:
      UpperCAmelCase       :	str											= 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       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_	):
 UpperCAmelCase       :	str											= full_name.split('conv_layers.'	)[-1]
 UpperCAmelCase       :	Union[str, Any]											= name.split('.'	)
 UpperCAmelCase       :	Optional[Any]											= int(items[0]	)
 UpperCAmelCase       :	List[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."""
   )
   UpperCAmelCase       :	Optional[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       :	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."
   )
   UpperCAmelCase       :	Optional[int]											= 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       :	Union[str, 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       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_=None	):
 UpperCAmelCase       :	Optional[Any]											= torch.load(_snake_case	)
 UpperCAmelCase       :	Any											= WavLMConfigOrig(checkpoint['cfg']	)
 UpperCAmelCase       :	Tuple											= WavLMOrig(_snake_case	)
 model.load_state_dict(checkpoint['model']	)
 model.eval()

 if config_path is not None:
  UpperCAmelCase       :	List[str]											= WavLMConfig.from_pretrained(_snake_case	)
 else:
  UpperCAmelCase       :	Optional[Any]											= WavLMConfig()

 UpperCAmelCase       :	int											= WavLMModel(_snake_case	)

 recursively_load_weights(_snake_case  ,       _snake_case	)

 hf_wavlm.save_pretrained(_snake_case	)


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








"""simple docstring"""
import numpy as np






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      int     ,   _snake_case						:      Union[str, Any]     )     ->Dict:
	"""simple docstring"""





	__snake_case	:						Union[str, Any]						=				int(np.ceil((x_end - xa) / h     )     )
	__snake_case	:						Dict						=				np.zeros((n + 1,)     )
	__snake_case	:						List[Any]						=				ya
	__snake_case	:						int						=				xa

	for k in range(_snake_case     ):
		__snake_case	:						Any						=				f(_snake_case     ,   y[k]     )
		__snake_case	:						List[Any]						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						int						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						Optional[int]						=				f(x + h     ,   y[k] + h * ka     )
		__snake_case	:						Optional[int]						=				y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
		x += h

	return y


if __name__ == "__main__":
			import doctest

			doctest.testmod()





