




import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase								=					logging.get_logger(__name__)



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     lowerCAmelCase__          =			WavaVecaForSequenceClassification.from_pretrained(lowerCAmelCase_       ,    config=lowerCAmelCase_      )
     lowerCAmelCase__          =			downstream_dict["projector.weight"]
     lowerCAmelCase__          =			downstream_dict["projector.bias"]
     lowerCAmelCase__          =			downstream_dict["model.post_net.linear.weight"]
     lowerCAmelCase__          =			downstream_dict["model.post_net.linear.bias"]
     return model



def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			WavaVecaForAudioFrameClassification.from_pretrained(lowerCAmelCase_       ,    config=lowerCAmelCase_      )
     lowerCAmelCase__          =			downstream_dict["model.linear.weight"]
     lowerCAmelCase__          =			downstream_dict["model.linear.bias"]
     return model



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

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

     lowerCAmelCase__          =			downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
     lowerCAmelCase__          =			downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
     lowerCAmelCase__          =			downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
     lowerCAmelCase__          =			downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
     lowerCAmelCase__          =			downstream_dict["objective.W"]
     return model



@torch.no_grad()
def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Any       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__          =			torch.load(lowerCAmelCase_       ,    map_location="cpu"      )

     lowerCAmelCase__          =			checkpoint["Downstream"]

     lowerCAmelCase__          =			WavaVecaConfig.from_pretrained(lowerCAmelCase_      )
     lowerCAmelCase__          =			WavaVecaFeatureExtractor.from_pretrained(
         lowerCAmelCase_       ,    return_attention_mask=lowerCAmelCase_       ,    do_normalize=lowerCAmelCase_      )

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

     if hf_config.use_weighted_layer_sum:
          lowerCAmelCase__          =			checkpoint["Featurizer"]["weights"]

     hf_feature_extractor.save_pretrained(lowerCAmelCase_      )
     hf_model.save_pretrained(lowerCAmelCase_      )


if __name__ == "__main__":
     UpperCamelCase								=					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								=					parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






import string



def 							_A       (						lowerCAmelCase_					: str      ):
     """simple docstring"""

     for key in range(len(string.ascii_uppercase      )      ):
          lowerCAmelCase__          =			""
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    lowerCAmelCase__          =			string.ascii_uppercase.find(lowerCAmelCase_      )
                    lowerCAmelCase__          =			num - key
                    if num < 0:
                         lowerCAmelCase__          =			num + len(string.ascii_uppercase      )
                    lowerCAmelCase__          =			translated + string.ascii_uppercase[num]
               else:
                    lowerCAmelCase__          =			translated + symbol
          print(F'Decryption using Key #{key}: {translated}'      )



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			input("Encrypted message: "      )
     lowerCAmelCase__          =			message.upper()
     decrypt(lowerCAmelCase_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()
