




'''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()
lowerCAmelCase_      :  List[str]				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  List[Any]				     =  {
    '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',
}
lowerCAmelCase_      :  int				     =  [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					Dict ,    UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					List[str]				):




     """simple docstring"""


     for attribute in key.split("""."""				):
          a_     :							Union[str, Any]     						=  getattr(lowerCamelCase__ ,    lowerCamelCase__				)

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

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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					int				):




     """simple docstring"""


     a_     :							Tuple     						=  []
     a_     :							Optional[Any]     						=  fairseq_model.state_dict()

     a_     :							List[Any]     						=  hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          a_     :							Tuple     						=  False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCamelCase__ ,    lowerCamelCase__ ,    lowerCamelCase__ ,    lowerCamelCase__ ,    hf_model.config.feat_extract_norm == """group""" ,    )
               a_     :							Tuple     						=  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_     :							List[Any]     						=  name.split(lowerCamelCase__				)[0].split("""."""				)[-2]
                              a_     :							str     						=  mapped_key.replace("""*""" ,    lowerCamelCase__				)
                         if "weight_g" in name:
                              a_     :							Any     						=  """weight_g"""
                         elif "weight_v" in name:
                              a_     :							Optional[int]     						=  """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_     :							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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					str ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					Optional[Any]				):




     """simple docstring"""


     a_     :							Dict     						=  full_name.split("""conv_layers."""				)[-1]
     a_     :							Union[str, Any]     						=  name.split("""."""				)
     a_     :							Dict     						=  int(items[0]				)
     a_     :							Optional[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."
               )
               a_     :							List[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."
               )
               a_     :							List[str]     						=  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_     :							Optional[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."
               )
               a_     :							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(lowerCamelCase__				)



@torch.no_grad()
def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					str=None				):




     """simple docstring"""


     a_     :							List[str]     						=  torch.load(lowerCamelCase__				)
     a_     :							int     						=  WavLMConfigOrig(checkpoint["""cfg"""]				)
     a_     :							Any     						=  WavLMOrig(lowerCamelCase__				)
     model.load_state_dict(checkpoint["""model"""]				)
     model.eval()

     if config_path is not None:
          a_     :							int     						=  WavLMConfig.from_pretrained(lowerCamelCase__				)
     else:
          a_     :							Optional[int]     						=  WavLMConfig()

     a_     :							int     						=  WavLMModel(lowerCamelCase__				)

     recursively_load_weights(lowerCamelCase__ ,    lowerCamelCase__				)

     hf_wavlm.save_pretrained(lowerCamelCase__				)


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




"""simple docstring"""


import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Path       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		):
 if config_name_or_path is None:
  lowercase__       :       Tuple				=						"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

 if generator_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						question_encoder_name_or_path

 lowercase__       :       Any				=						RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

 # Save model.
 lowercase__       :       Union[str, Any]				=						RagConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						AutoConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       Dict				=						AutoConfig.from_pretrained(lowerCamelCase__  )

 lowercase__       :       Union[str, Any]				=						gen_config
 lowercase__       :       Any				=						question_encoder_config

 lowercase__       :       str				=						model_class.from_pretrained_question_encoder_generator(
     lowerCamelCase__       ,		lowerCamelCase__       ,		config=lowerCamelCase__  )
 rag_model.save_pretrained(lowerCamelCase__  )

 # Sanity check.
 model_class.from_pretrained(lowerCamelCase__  )

 # Save tokenizers.
 lowercase__       :       Optional[Any]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""  )
 lowercase__       :       Optional[int]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""  )


if __name__ == "__main__":
   __snake_case    					=					argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   __snake_case    					=					parser.parse_args()

   __snake_case    					=					Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )