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()
a_    					=       logging.get_logger(__name__)

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





def    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Dict	):
 for attribute in key.split('.'	):
  UpperCamelCase_					:						Optional[Any] =					getattr(lowerCamelCase  ,  lowerCamelCase	)

 if weight_type is not None:
  UpperCamelCase_					:						Dict =					getattr(lowerCamelCase  ,  lowerCamelCase	).shape
 else:
  UpperCamelCase_					:						int =					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_					:						Dict =					value
 elif weight_type == "weight_g":
  UpperCamelCase_					:						Any =					value
 elif weight_type == "weight_v":
  UpperCamelCase_					:						List[Any] =					value
 elif weight_type == "bias":
  UpperCamelCase_					:						Any =					value
 else:
  UpperCamelCase_					:						Union[str, Any] =					value

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





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     List[str]	):
 UpperCamelCase_					:						Optional[Any] =					[]
 UpperCamelCase_					:						int =					fairseq_model.state_dict()

 UpperCamelCase_					:						Dict =					hf_model.feature_extractor

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

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

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





def    __lowercase     (						lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Optional[int]	):
 UpperCamelCase_					:						int =					full_name.split('conv_layers.'	)[-1]
 UpperCamelCase_					:						str =					name.split('.'	)
 UpperCamelCase_					:						Optional[int] =					int(items[0]	)
 UpperCamelCase_					:						int =					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_					:						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."
   )
   UpperCamelCase_					:						str =					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(lowerCamelCase	)





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     List[str]=None	):
 # load the pre-trained checkpoints
 UpperCamelCase_					:						List[Any] =					torch.load(lowerCamelCase	)
 UpperCamelCase_					:						str =					WavLMConfigOrig(checkpoint['cfg']	)
 UpperCamelCase_					:						Union[str, Any] =					WavLMOrig(lowerCamelCase	)
 model.load_state_dict(checkpoint['model']	)
 model.eval()

 if config_path is not None:
  UpperCamelCase_					:						Optional[int] =					WavLMConfig.from_pretrained(lowerCamelCase	)
 else:
  UpperCamelCase_					:						Dict =					WavLMConfig()

 UpperCamelCase_					:						List[str] =					WavLMModel(lowerCamelCase	)

 recursively_load_weights(lowerCamelCase  ,  lowerCamelCase	)

 hf_wavlm.save_pretrained(lowerCamelCase	)


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




from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_    					=       logging.get_logger(__name__)

a_    					=       {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   'megatron-bert'

 def __init__( self	:							List[str]   ,      snake_case	:							Tuple=2_9_0_5_6   ,      snake_case	:							Dict=1_0_2_4   ,      snake_case	:							Dict=2_4   ,      snake_case	:							Union[str, Any]=1_6   ,      snake_case	:							Optional[int]=4_0_9_6   ,      snake_case	:							Optional[int]="gelu"   ,      snake_case	:							Any=0.1   ,      snake_case	:							Tuple=0.1   ,      snake_case	:							Optional[int]=5_1_2   ,      snake_case	:							List[Any]=2   ,      snake_case	:							Tuple=0.02   ,      snake_case	:							Optional[Any]=1e-12   ,      snake_case	:							str=0   ,      snake_case	:							Optional[int]="absolute"   ,      snake_case	:							Union[str, Any]=True   ,      **snake_case	:							Any   ,      )   ->						List[Any]:

  """simple docstring"""





  super().__init__(pad_token_id=snake_case   ,      **snake_case			)

  UpperCamelCase_					:						Optional[Any] =					vocab_size
  UpperCamelCase_					:						Any =					hidden_size
  UpperCamelCase_					:						Union[str, Any] =					num_hidden_layers
  UpperCamelCase_					:						List[Any] =					num_attention_heads
  UpperCamelCase_					:						str =					hidden_act
  UpperCamelCase_					:						List[str] =					intermediate_size
  UpperCamelCase_					:						List[Any] =					hidden_dropout_prob
  UpperCamelCase_					:						Any =					attention_probs_dropout_prob
  UpperCamelCase_					:						Tuple =					max_position_embeddings
  UpperCamelCase_					:						Dict =					type_vocab_size
  UpperCamelCase_					:						Optional[int] =					initializer_range
  UpperCamelCase_					:						Optional[Any] =					layer_norm_eps
  UpperCamelCase_					:						Dict =					position_embedding_type
  UpperCamelCase_					:						List[str] =					use_cache



