
'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase__      :		List[str]       							=  logging.get_logger(__name__)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				UniSpeechSatForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_    ,      config=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""projector.weight"""]
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""projector.bias"""]
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""model.post_net.linear.weight"""]
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""model.post_net.linear.bias"""]
   return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				UniSpeechSatForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE_    ,      config=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""model.linear.weight"""]
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""model.linear.bias"""]
   return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				UniSpeechSatForXVector.from_pretrained(SCREAMING_SNAKE_CASE_    ,      config=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""connector.weight"""]
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""connector.bias"""]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
      _SCREAMING_SNAKE_CASE      =				downstream_dict[
          F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
      ]
      _SCREAMING_SNAKE_CASE      =				downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

   _SCREAMING_SNAKE_CASE      =				downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
   _SCREAMING_SNAKE_CASE      =				downstream_dict["""objective.W"""]
   return model
@torch.no_grad()
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				torch.load(SCREAMING_SNAKE_CASE_    ,      map_location="""cpu"""						)

   _SCREAMING_SNAKE_CASE      =				checkpoint["""Downstream"""]

   _SCREAMING_SNAKE_CASE      =				UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				WavaVecaFeatureExtractor.from_pretrained(
       SCREAMING_SNAKE_CASE_    ,      return_attention_mask=SCREAMING_SNAKE_CASE_    ,      do_normalize=SCREAMING_SNAKE_CASE_						)

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

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

   hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_						)
   hf_model.save_pretrained(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
     UpperCamelCase__      :		Tuple       							=  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__      :		int       							=  parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








'''simple docstring'''



import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def    lowerCAmelCase_	(				)     ->     List[Any]:




   """simple docstring"""
   with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT						):
      with pytest.raises(SCREAMING_SNAKE_CASE_						):
         requests.request("""GET"""    ,      """https://huggingface.co"""						)
      with pytest.raises(requests.exceptions.ConnectTimeout						):
         requests.request("""GET"""    ,      """https://huggingface.co"""    ,      timeout=1.0						)
@pytest.mark.integration
def    lowerCAmelCase_	(				)     ->     int:




   """simple docstring"""
   with offline(OfflineSimulationMode.CONNECTION_FAILS						):
      with pytest.raises(requests.exceptions.ConnectionError						):
         requests.request("""GET"""    ,      """https://huggingface.co"""						)







def    lowerCAmelCase_	(				)     ->     Optional[Any]:




   """simple docstring"""
   with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1						):
      with pytest.raises(SCREAMING_SNAKE_CASE_						):
         http_head("""https://huggingface.co"""						)






