


'''simple docstring'''



import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
A__						:   Optional[Any]				=    logging.get_logger(__name__)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							Tuple ,       UpperCAmelCase_       :							Union[str, Any]				)	->						List[Any]:
     __lowerCamelCase  :      Optional[int]														=						UniSpeechSatForSequenceClassification.from_pretrained(UpperCAmelCase_ ,       config=UpperCAmelCase_				)
     __lowerCamelCase  :      Union[str, Any]														=						downstream_dict['projector.weight']
     __lowerCamelCase  :      List[str]														=						downstream_dict['projector.bias']
     __lowerCamelCase  :      List[str]														=						downstream_dict['model.post_net.linear.weight']
     __lowerCamelCase  :      List[Any]														=						downstream_dict['model.post_net.linear.bias']
     return model



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							List[Any]				)	->						List[str]:
     __lowerCamelCase  :      Optional[Any]														=						UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCAmelCase_ ,       config=UpperCAmelCase_				)
     __lowerCamelCase  :      str														=						downstream_dict['model.linear.weight']
     __lowerCamelCase  :      List[str]														=						downstream_dict['model.linear.bias']
     return model



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple ,       UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							str				)	->						Optional[int]:
     __lowerCamelCase  :      Any														=						UniSpeechSatForXVector.from_pretrained(UpperCAmelCase_ ,       config=UpperCAmelCase_				)
     __lowerCamelCase  :      Dict														=						downstream_dict['connector.weight']
     __lowerCamelCase  :      Dict														=						downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
          __lowerCamelCase  :      Optional[int]														=						downstream_dict[
              F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
          ]
          __lowerCamelCase  :      Optional[int]														=						downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

     __lowerCamelCase  :      Union[str, Any]														=						downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     __lowerCamelCase  :      Tuple														=						downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     __lowerCamelCase  :      Any														=						downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     __lowerCamelCase  :      Tuple														=						downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     __lowerCamelCase  :      Dict														=						downstream_dict['objective.W']
     return model



@torch.no_grad()
def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple ,       UpperCAmelCase_       :							Tuple ,       UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							Optional[int]				)	->						Dict:
     __lowerCamelCase  :      str														=						torch.load(UpperCAmelCase_ ,       map_location='cpu'				)

     __lowerCamelCase  :      Optional[int]														=						checkpoint['Downstream']

     __lowerCamelCase  :      str														=						UniSpeechSatConfig.from_pretrained(UpperCAmelCase_				)
     __lowerCamelCase  :      str														=						WavaVecaFeatureExtractor.from_pretrained(
         UpperCAmelCase_ ,       return_attention_mask=UpperCAmelCase_ ,       do_normalize=UpperCAmelCase_				)

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

     if hf_config.use_weighted_layer_sum:
          __lowerCamelCase  :      Optional[Any]														=						checkpoint['Featurizer']['weights']

     hf_feature_extractor.save_pretrained(UpperCAmelCase_				)
     hf_model.save_pretrained(UpperCAmelCase_				)


if __name__ == "__main__":
      A__						:   str				=    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.""")
      A__						:   Optional[int]				=    parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










'''simple docstring'''



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


A__						:   Tuple				=    logging.get_logger(__name__)

A__						:   Dict				=    {
    """RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
    """RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
    """RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
    """RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
    """RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
    """RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   List[Any]					=     'rwkv'
    lowerCamelCase    :   Any					=     {'max_position_embeddings': 'context_length'}







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=5_02_77     ,	SCREAMING_SNAKE_CASE_=10_24     ,	SCREAMING_SNAKE_CASE_=40_96     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=True     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						vocab_size
         __lowerCamelCase  :      Tuple														=						context_length
         __lowerCamelCase  :      str														=						hidden_size
         __lowerCamelCase  :      List[str]														=						num_hidden_layers
         __lowerCamelCase  :      Any														=						attention_hidden_size if attention_hidden_size is not None else hidden_size
         __lowerCamelCase  :      Optional[int]														=						intermediate_size if intermediate_size is not None else 4 * hidden_size
         __lowerCamelCase  :      Optional[Any]														=						layer_norm_epsilon
         __lowerCamelCase  :      int														=						rescale_every
         __lowerCamelCase  :      Tuple														=						use_cache

         __lowerCamelCase  :      int														=						bos_token_id
         __lowerCamelCase  :      Optional[Any]														=						eos_token_id

         super().__init__(
             tie_word_embeddings=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )






