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()
UpperCAmelCase_           =				logging.get_logger(__name__)

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




def    lowerCAmelCase_  (		lowercase:				Any	,       lowercase:				Optional[Any]	,       lowercase:				str	,       lowercase:				Any	,       lowercase:				Optional[int]		)     ->					Tuple:







 '''simple docstring'''
 for attribute in key.split('''.'''		):
  _UpperCamelCase:  Optional[int]    						=			getattr(lowercase	,       lowercase		)

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

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




def    lowerCAmelCase_  (		lowercase:				Tuple	,       lowercase:				List[Any]		)     ->					int:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			[]
 _UpperCamelCase:  str    						=			fairseq_model.state_dict()

 _UpperCamelCase:  Any    						=			hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  _UpperCamelCase:  Union[str, Any]    						=			False
  if "conv_layers" in name:
   load_conv_layer(
       lowercase	,       lowercase	,       lowercase	,       lowercase	,       hf_model.config.feat_extract_norm == '''group'''	,       )
   _UpperCamelCase:  Dict    						=			True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
     _UpperCamelCase:  Any    						=			True
     if "*" in mapped_key:
      _UpperCamelCase:  Any    						=			name.split(lowercase		)[0].split('''.'''		)[-2]
      _UpperCamelCase:  Optional[Any]    						=			mapped_key.replace('''*'''	,       lowercase		)
     if "weight_g" in name:
      _UpperCamelCase:  Dict    						=			'''weight_g'''
     elif "weight_v" in name:
      _UpperCamelCase:  List[str]    						=			'''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:  Tuple    						=			'''weight'''
     else:
      _UpperCamelCase:  int    						=			None

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

 logger.warning(F"""Unused weights: {unused_weights}"""		)




def    lowerCAmelCase_  (		lowercase:				Optional[int]	,       lowercase:				List[str]	,       lowercase:				Tuple	,       lowercase:				int	,       lowercase:				Any		)     ->					List[str]:







 '''simple docstring'''
 _UpperCamelCase:  int    						=			full_name.split('''conv_layers.'''		)[-1]
 _UpperCamelCase:  Tuple    						=			name.split('''.'''		)
 _UpperCamelCase:  Optional[int]    						=			int(items[0]		)
 _UpperCamelCase:  Dict    						=			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:  str    						=			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:  List[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."""
   )
   _UpperCamelCase:  Optional[int]    						=			value
   logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
 else:
  unused_weights.append(lowercase		)




@torch.no_grad()
def    lowerCAmelCase_  (		lowercase:				List[str]	,       lowercase:				Optional[Any]	,       lowercase:				List[str]=None		)     ->					Optional[int]:







 '''simple docstring'''
 # load the pre-trained checkpoints
 _UpperCamelCase:  List[Any]    						=			torch.load(lowercase		)
 _UpperCamelCase:  List[Any]    						=			WavLMConfigOrig(checkpoint['''cfg''']		)
 _UpperCamelCase:  Tuple    						=			WavLMOrig(lowercase		)
 model.load_state_dict(checkpoint['''model''']		)
 model.eval()

 if config_path is not None:
  _UpperCamelCase:  int    						=			WavLMConfig.from_pretrained(lowercase		)
 else:
  _UpperCamelCase:  Tuple    						=			WavLMConfig()

 _UpperCamelCase:  Dict    						=			WavLMModel(lowercase		)

 recursively_load_weights(lowercase	,       lowercase		)

 hf_wavlm.save_pretrained(lowercase		)


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




def    lowerCAmelCase_  (		lowercase:				int	,       lowercase:				float	,       lowercase:				bool = False		)     ->					dict:







 '''simple docstring'''
 _UpperCamelCase:  dict    						=			{i: [] for i in range(lowercase		)}

 # if probability is greater or equal than 1, then generate a complete graph
 if probability >= 1:
  return complete_graph(lowercase		)
 # if probability is lower or equal than 0, then return a graph without edges
 if probability <= 0:
  return graph

 # for each couple of nodes, add an edge from u to v
 # if the number randomly generated is greater than probability probability
 for i in range(lowercase		):
  for j in range(i + 1	,       lowercase		):
   if random.random() < probability:
    graph[i].append(lowercase		)
    if not directed:
     # if the graph is undirected, add an edge in from j to i, either
     graph[j].append(lowercase		)
 return graph




def    lowerCAmelCase_  (		lowercase:				int		)     ->					dict:







 '''simple docstring'''
 return {
     i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
 }


if __name__ == "__main__":
  import doctest

  doctest.testmod()