'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:					Tuple  =logging.get_logger(__name__)





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Optional[Any] ,   snake_case_	:			List[Any]      )   ->				Dict:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			Optional[int] ,   snake_case_	:			List[Any] ,   snake_case_	:			Optional[int]      )   ->				List[Any]:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			int ,   snake_case_	:			Tuple      )   ->				Any:


     '''simple docstring'''




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

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





@torch.no_grad()
def      lowerCAmelCase_    ( snake_case_	:			List[Any] ,   snake_case_	:			str ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[Any]      )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       torch.load(snake_case_ ,   map_location="cpu"      )

     UpperCAmelCase_	   =       checkpoint["Downstream"]

     UpperCAmelCase_	   =       WavaVecaConfig.from_pretrained(snake_case_      )
     UpperCAmelCase_	   =       WavaVecaFeatureExtractor.from_pretrained(
         snake_case_ ,   return_attention_mask=snake_case_ ,   do_normalize=snake_case_      )

     UpperCAmelCase_	   =       hf_config.architectures[0]
     if arch.endswith("ForSequenceClassification"      ):
          UpperCAmelCase_	   =       convert_classification(snake_case_ ,   snake_case_ ,   snake_case_      )
     elif arch.endswith("ForAudioFrameClassification"      ):
          UpperCAmelCase_	   =       convert_diarization(snake_case_ ,   snake_case_ ,   snake_case_      )
     elif arch.endswith("ForXVector"      ):
          UpperCAmelCase_	   =       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:
          UpperCAmelCase_	   =       checkpoint["Featurizer"]["weights"]

     hf_feature_extractor.save_pretrained(snake_case_      )
     hf_model.save_pretrained(snake_case_      )


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





'''simple docstring'''





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       FileLock(str(tmpdir / "foo.lock"      )      )
     UpperCAmelCase_	   =       FileLock(str(tmpdir / "foo.lock"      )      )
     UpperCAmelCase_	   =       0.01
     with locka.acquire():
          with pytest.raises(snake_case_      ):
               UpperCAmelCase_	   =       time.time()
               locka.acquire(snake_case_      )
               assert time.time() - _start > timeout






def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       "a" * 10_00 + ".lock"
     UpperCAmelCase_	   =       FileLock(str(tmpdir / filename      )      )
     assert locka._lock_file.endswith(".lock"      )
     assert not locka._lock_file.endswith(snake_case_      )
     assert len(os.path.basename(locka._lock_file      )      ) <= 2_55
     UpperCAmelCase_	   =       FileLock(tmpdir / filename      )
     with locka.acquire():
          with pytest.raises(snake_case_      ):
               locka.acquire(0      )




