code: 
  
  
  '''simple docstring'''
  
  import argparse
  
  import torch
  
  from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  __lowercase:		Tuple							=     logging.get_logger(__name__)
  
  __lowercase:		str							=     ["UtteranceLevel"]
  
  
  
  @torch.no_grad()
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				Any    ,		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				Any   )		->					Union[str, Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						torch.load(snake_case_    ,		map_location="cpu"   )
     if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
        raise NotImplementedError(F'The supported s3prl models are {SUPPORTED_MODELS}'   )
  
     UpperCamelCase__ 						=						checkpoint['''Downstream''']
  
     UpperCamelCase__ 						=						HubertConfig.from_pretrained(snake_case_   )
     UpperCamelCase__ 						=						HubertForSequenceClassification.from_pretrained(snake_case_    ,		config=snake_case_   )
     UpperCamelCase__ 						=						WavaVecaFeatureExtractor.from_pretrained(
         snake_case_    ,		return_attention_mask=snake_case_    ,		do_normalize=snake_case_   )
  
     if hf_congfig.use_weighted_layer_sum:
        UpperCamelCase__ 						=						checkpoint['''Featurizer''']['''weights''']
  
     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''']
  
     hf_feature_extractor.save_pretrained(snake_case_   )
     hf_model.save_pretrained(snake_case_   )
  
  
  if __name__ == "__main__":
         __lowercase:		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.")
         __lowercase:		List[Any]							=     parser.parse_args()
         convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
style_context: 
  
  
  '''simple docstring'''
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  
  
  
  
  
  
  @require_vision
  class  UpperCAmelCase (       unittest.TestCase):
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						tempfile.mkdtemp()
  
      UpperCamelCase__ 						=						CLIPImageProcessor()
      UpperCamelCase__ 						=						BertTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-BertModel",					model_input_names=["input_ids", "attention_mask"]					)
  
      UpperCamelCase__ 						=						GitProcessor(a_,					a_					)
  
      processor.save_pretrained(self.tmpdirname					)
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).tokenizer
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).image_processor
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      shutil.rmtree(self.tmpdirname					)
  
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta					)]
  
      UpperCamelCase__ 						=						[Image.fromarray(np.moveaxis(a_,					0,					-1					)					) for x in image_inputs]
  
      return image_inputs
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()					)
      processor.save_pretrained(self.tmpdirname					)
  
      UpperCamelCase__ 						=						self.get_tokenizer(bos_token="(BOS)",					eos_token="(EOS)"					)
      UpperCamelCase__ 						=						self.get_image_processor(do_normalize=a_,					padding_value=1.0					)
  
      UpperCamelCase__ 						=						GitProcessor.from_pretrained(
          self.tmpdirname,					bos_token="(BOS)",					eos_token="(EOS)",					do_normalize=a_,					padding_value=1.0					)
  
      self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()					)
      self.assertIsInstance(processor.tokenizer,					a_					)
  
      self.assertEqual(processor.image_processor.to_json_string(),					image_processor_add_kwargs.to_json_string()					)
      self.assertIsInstance(processor.image_processor,					a_					)
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						image_processor(a_,					return_tensors="np"					)
      UpperCamelCase__ 						=						processor(images=a_,					return_tensors="np"					)
  
      for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
  
      UpperCamelCase__ 						=						processor(text=a_					)
  
      UpperCamelCase__ 						=						tokenizer(a_,					return_token_type_ids=a_					)
  
      for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key],					encoded_processor[key]					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)
  
      # test if it raises when no input is passed
      with pytest.raises(a_					):
         processor()
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
      UpperCamelCase__ 						=						processor.batch_decode(a_					)
      UpperCamelCase__ 						=						tokenizer.batch_decode(a_					)
  
      self.assertListEqual(a_,					a_					)
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)