





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase   :							Optional[Any]     =	logging.get_logger(__name__)
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case     )  ->						Any:


   '''simple docstring'''







   __A				 =       WavLMForSequenceClassification.from_pretrained(snake_case    ,	config=snake_case     )
   __A				 =       downstream_dict['''projector.weight''']
   __A				 =       downstream_dict['''projector.bias''']
   __A				 =       downstream_dict['''model.post_net.linear.weight''']
   __A				 =       downstream_dict['''model.post_net.linear.bias''']
   return model
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case     )  ->						Optional[int]:


   '''simple docstring'''







   __A				 =       WavLMForAudioFrameClassification.from_pretrained(snake_case    ,	config=snake_case     )
   __A				 =       downstream_dict['''model.linear.weight''']
   __A				 =       downstream_dict['''model.linear.bias''']
   return model
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case     )  ->						Any:


   '''simple docstring'''







   __A				 =       WavLMForXVector.from_pretrained(snake_case    ,	config=snake_case     )
   __A				 =       downstream_dict['''connector.weight''']
   __A				 =       downstream_dict['''connector.bias''']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
      __A				 =       downstream_dict[
          F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
      ]
      __A				 =       downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

   __A				 =       downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
   __A				 =       downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
   __A				 =       downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
   __A				 =       downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
   __A				 =       downstream_dict['''objective.W''']
   return model
@torch.no_grad()
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case    ,	snake_case     )  ->						Optional[int]:


   '''simple docstring'''







   __A				 =       torch.load(snake_case    ,	map_location='''cpu'''     )

   __A				 =       checkpoint['''Downstream''']

   __A				 =       WavLMConfig.from_pretrained(snake_case     )
   __A				 =       WavaVecaFeatureExtractor.from_pretrained(
       snake_case    ,	return_attention_mask=snake_case    ,	do_normalize=snake_case     )

   __A				 =       hf_config.architectures[0]
   if arch.endswith('''ForSequenceClassification'''     ):
      __A				 =       convert_classification(snake_case    ,	snake_case    ,	snake_case     )
   elif arch.endswith('''ForAudioFrameClassification'''     ):
      __A				 =       convert_diarization(snake_case    ,	snake_case    ,	snake_case     )
   elif arch.endswith('''ForXVector'''     ):
      __A				 =       convert_xvector(snake_case    ,	snake_case    ,	snake_case     )
   else:
      raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"     )

   if hf_config.use_weighted_layer_sum:
      __A				 =       checkpoint['''Featurizer''']['''weights''']

   hf_feature_extractor.save_pretrained(snake_case     )
   hf_model.save_pretrained(snake_case     )


if __name__ == "__main__":
    _UpperCamelCase   :							List[Any]     =	argparse.ArgumentParser()
    parser.add_argument(
        """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
    )
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
    parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
    parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
    _UpperCamelCase   :							Tuple     =	parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








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


_UpperCamelCase   :							Dict     =	logging.get_logger(__name__)

_UpperCamelCase   :							Optional[int]     =	{
    """MCG-NJU/videomae-base""": """https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json""",
}







class 						_lowerCAmelCase(						_a):

  """simple docstring"""







  lowerCamelCase__      					=   '''videomae'''

  def __init__( self  ,    UpperCAmelCase=2_24  ,    UpperCAmelCase=16  ,    UpperCAmelCase=3  ,    UpperCAmelCase=16  ,    UpperCAmelCase=2  ,    UpperCAmelCase=7_68  ,    UpperCAmelCase=12  ,    UpperCAmelCase=12  ,    UpperCAmelCase=30_72  ,    UpperCAmelCase="gelu"  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.02  ,    UpperCAmelCase=1e-12  ,    UpperCAmelCase=True  ,    UpperCAmelCase=True  ,    UpperCAmelCase=6  ,    UpperCAmelCase=3_84  ,    UpperCAmelCase=4  ,    UpperCAmelCase=15_36  ,    UpperCAmelCase=True  ,    **UpperCAmelCase  ,    )-> Any:
     super().__init__(**UpperCAmelCase     )

     __A				 =       image_size
     __A				 =       patch_size
     __A				 =       num_channels
     __A				 =       num_frames
     __A				 =       tubelet_size

     __A				 =       hidden_size
     __A				 =       num_hidden_layers
     __A				 =       num_attention_heads
     __A				 =       intermediate_size
     __A				 =       hidden_act
     __A				 =       hidden_dropout_prob
     __A				 =       attention_probs_dropout_prob
     __A				 =       initializer_range
     __A				 =       layer_norm_eps
     __A				 =       qkv_bias
     __A				 =       use_mean_pooling

     __A				 =       decoder_num_attention_heads
     __A				 =       decoder_hidden_size
     __A				 =       decoder_num_hidden_layers
     __A				 =       decoder_intermediate_size
     __A				 =       norm_pix_loss

