



'''simple docstring'''





import argparse

import torch

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


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




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						UniSpeechSatForSequenceClassification.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											=						UniSpeechSatForAudioFrameClassification.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											=						UniSpeechSatForXVector.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											=						UniSpeechSatConfig.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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor






class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def __init__(self				,     A				,     A=7				,     A=3				,     A=18				,     A=30				,     A=400				,     A=True				,     A=None				,     A=True				,     A=None				,     )					->  Any:


      """simple docstring"""



      _a											=						size if size is not None else {'''shortest_edge''': 20}
      _a											=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      _a											=						parent
      _a											=						batch_size
      _a											=						num_channels
      _a											=						image_size
      _a											=						min_resolution
      _a											=						max_resolution
      _a											=						do_resize
      _a											=						size
      _a											=						do_center_crop
      _a											=						crop_size



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


      """simple docstring"""



      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }






@require_torch
@require_vision
class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Tuple    = MobileNetVaImageProcessor if is_vision_available() else None



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						MobileNetVaImageProcessingTester(self )



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(A				,     '''do_resize''' ) )
      self.assertTrue(hasattr(A				,     '''size''' ) )
      self.assertTrue(hasattr(A				,     '''do_center_crop''' ) )
      self.assertTrue(hasattr(A				,     '''crop_size''' ) )



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size				,     {'''shortest_edge''': 20} )
      self.assertEqual(image_processor.crop_size				,     {'''height''': 18, '''width''': 18} )

      _a											=						self.image_processing_class.from_dict(self.image_processor_dict				,     size=42				,     crop_size=84 )
      self.assertEqual(image_processor.size				,     {'''shortest_edge''': 42} )
      self.assertEqual(image_processor.crop_size				,     {'''height''': 84, '''width''': 84} )



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


      """simple docstring"""



      pass



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     Image.Image )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     numpify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     np.ndarray )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )



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


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     torchify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     torch.Tensor )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )



