



'''simple docstring'''





import argparse

import torch

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


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




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						WavaVecaForSequenceClassification.from_pretrained(__A						,			config=__A)
     _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 lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						WavaVecaForAudioFrameClassification.from_pretrained(__A						,			config=__A)
     _a											=						downstream_dict['''model.linear.weight''']
     _a											=						downstream_dict['''model.linear.bias''']
     return model




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						WavaVecaForXVector.from_pretrained(__A						,			config=__A)
     _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 lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



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

     _a											=						checkpoint['''Downstream''']

     _a											=						WavaVecaConfig.from_pretrained(__A)
     _a											=						WavaVecaFeatureExtractor.from_pretrained(
         __A						,			return_attention_mask=__A						,			do_normalize=__A)

     _a											=						hf_config.architectures[0]
     if arch.endswith('''ForSequenceClassification'''):
          _a											=						convert_classification(__A						,			__A						,			__A)
     elif arch.endswith('''ForAudioFrameClassification'''):
          _a											=						convert_diarization(__A						,			__A						,			__A)
     elif arch.endswith('''ForXVector'''):
          _a											=						convert_xvector(__A						,			__A						,			__A)
     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(__A)
     hf_model.save_pretrained(__A)


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








'''simple docstring'''





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


lowercase_	 =						get_tests_dir("fixtures")






class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						mock.Mock()
      _a											=						500
      _a											=						{}
      _a											=						HTTPError
      _a											=						{}

      # Download this model to make sure it's in the cache.
      _a											=						ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
      # Under the mock environment we get a 500 error when trying to reach the model.
      with mock.patch('''requests.Session.request'''				,     return_value=A ) as mock_head:
           _a											=						ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
           # This check we did call the fake head request
           mock_head.assert_called()



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						ViTImageProcessor.from_pretrained(
          '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      with self.assertRaises(A ):
           # config is in subfolder, the following should not work without specifying the subfolder
           _a											=						AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )

      _a											=						AutoImageProcessor.from_pretrained(
          '''hf-internal-testing/stable-diffusion-all-variants'''				,     subfolder='''feature_extractor''' )

      self.assertIsNotNone(A )






@is_staging_test
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 @classmethod
 def       a__						(cls )					->  Any:


      """simple docstring"""



      _a											=						TOKEN
      HfFolder.save_token(A )



 @classmethod
 def       a__						(cls )					->  Union[str, Any]:


      """simple docstring"""



      try:
           delete_repo(token=cls._token				,     repo_id='''test-image-processor''' )
      except HTTPError:
           pass

      try:
           delete_repo(token=cls._token				,     repo_id='''valid_org/test-image-processor-org''' )
      except HTTPError:
           pass

      try:
           delete_repo(token=cls._token				,     repo_id='''test-dynamic-image-processor''' )
      except HTTPError:
           pass



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						ViTImageProcessor.from_pretrained(A )
      image_processor.push_to_hub('''test-image-processor'''				,     use_auth_token=self._token )

      _a											=						ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor''' )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A				,     getattr(A				,     A ) )

      # Reset repo
      delete_repo(token=self._token				,     repo_id='''test-image-processor''' )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(
               A				,     repo_id='''test-image-processor'''				,     push_to_hub=A				,     use_auth_token=self._token )

      _a											=						ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor''' )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A				,     getattr(A				,     A ) )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						ViTImageProcessor.from_pretrained(A )
      image_processor.push_to_hub('''valid_org/test-image-processor'''				,     use_auth_token=self._token )

      _a											=						ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A				,     getattr(A				,     A ) )

      # Reset repo
      delete_repo(token=self._token				,     repo_id='''valid_org/test-image-processor''' )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(
               A				,     repo_id='''valid_org/test-image-processor-org'''				,     push_to_hub=A				,     use_auth_token=self._token )

      _a											=						ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A				,     getattr(A				,     A ) )



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      CustomImageProcessor.register_for_auto_class()
      _a											=						CustomImageProcessor.from_pretrained(A )

      image_processor.push_to_hub('''test-dynamic-image-processor'''				,     use_auth_token=self._token )

      # This has added the proper auto_map field to the config
      self.assertDictEqual(
          image_processor.auto_map				,     {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}				,     )

      _a											=						AutoImageProcessor.from_pretrained(
          f'''{USER}/test-dynamic-image-processor'''				,     trust_remote_code=A )
      # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
      self.assertEqual(new_image_processor.__class__.__name__				,     '''CustomImageProcessor''' )



