




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__       :      Optional[int]  =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :      Union[str, 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',
}
SCREAMING_SNAKE_CASE__       :      Optional[int]  =   [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Optional[int]:
       for attribute in key.split("."    ):
              lowerCamelCase   :      Optional[Any]			 =					getattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

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

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

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       lowerCamelCase   :      Optional[Any]			 =					[]
       lowerCamelCase   :      Tuple			 =					fairseq_model.state_dict()

       lowerCamelCase   :      int			 =					hf_model.feature_extractor

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

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

       logger.warning(f'''Unused weights: {unused_weights}'''    )

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Tuple:
       lowerCamelCase   :      List[Any]			 =					full_name.split("conv_layers."    )[-1]
       lowerCamelCase   :      Union[str, Any]			 =					name.split("."    )
       lowerCamelCase   :      Optional[Any]			 =					int(items[0]    )
       lowerCamelCase   :      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.'''
                     )
                     lowerCamelCase   :      Tuple			 =					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.'''
                     )
                     lowerCamelCase   :      Optional[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."
                     )
                     lowerCamelCase   :      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.'''
                     )
                     lowerCamelCase   :      List[Any]			 =					value
                     logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''    )
       else:
              unused_weights.append(_SCREAMING_SNAKE_CASE    )

@torch.no_grad()
def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=None    )	-> Tuple:
       # load the pre-trained checkpoints
       lowerCamelCase   :      Optional[int]			 =					torch.load(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					WavLMConfigOrig(checkpoint["cfg"]    )
       lowerCamelCase   :      Any			 =					WavLMOrig(_SCREAMING_SNAKE_CASE    )
       model.load_state_dict(checkpoint["model"]    )
       model.eval()

       if config_path is not None:
              lowerCamelCase   :      Any			 =					WavLMConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       else:
              lowerCamelCase   :      Dict			 =					WavLMConfig()

       lowerCamelCase   :      Optional[int]			 =					WavLMModel(_SCREAMING_SNAKE_CASE    )

       recursively_load_weights(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       hf_wavlm.save_pretrained(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   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__       :      Union[str, Any]  =   parser.parse_args()
       convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)












def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       return x if y == 0 else greatest_common_divisor(_SCREAMING_SNAKE_CASE ,x % y    )

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> int:
       return (x * y) // greatest_common_divisor(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

def      A      (      _SCREAMING_SNAKE_CASE = 20    )	-> int:
       lowerCamelCase   :      List[Any]			 =					1
       for i in range(1 ,n + 1    ):
              lowerCamelCase   :      List[str]			 =					lcm(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       return g


if __name__ == "__main__":
       print(f'''{solution() = }''')






