




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowercase__   :    List[Any] 					=      logging.get_logger(__name__)

lowercase__   :    List[Any] 					=      ["UtteranceLevel"]

@torch.no_grad()
def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase)    ->  Union[str, Any]:
     a											=   torch.load(__UpperCamelCase	,      map_location="cpu")
     if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}''')

     a											=   checkpoint["Downstream"]

     a											=   HubertConfig.from_pretrained(__UpperCamelCase)
     a											=   HubertForSequenceClassification.from_pretrained(__UpperCamelCase	,      config=__UpperCamelCase)
     a											=   WavaVecaFeatureExtractor.from_pretrained(
         __UpperCamelCase	,      return_attention_mask=__UpperCamelCase	,      do_normalize=__UpperCamelCase)

     if hf_congfig.use_weighted_layer_sum:
          a											=   checkpoint["Featurizer"]["weights"]

     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"]

     hf_feature_extractor.save_pretrained(__UpperCamelCase)
     hf_model.save_pretrained(__UpperCamelCase)


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__   :    Any 					=      parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







import unittest

from transformers.image_processing_utils import get_size_dict




class     a__ (      unittest.TestCase   ):







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



          '''simple docstring'''



          a											=   {"wrong_key": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"height": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"width": 224, "shortest_edge": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          # Test a dict with the correct keys is returned as is
          a											=   {"height": 224, "width": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	A				)

          a											=   {"shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          a											=   {"longest_edge": 224, "shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"longest_edge": 224, "shortest_edge": 224}				)

          # Test a single int value which  represents (size, size)
          a											=   get_size_dict(224				)
          self.assertEqual(A						,	{"height": 224, "width": 224}				)

          # Test a single int value which represents the shortest edge
          a											=   get_size_dict(224						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          # Test a tuple of ints which represents (height, width)
          a											=   get_size_dict((150, 200)				)
          self.assertEqual(A						,	{"height": 150, "width": 200}				)

          # Test a tuple of ints which represents (width, height)
          a											=   get_size_dict((150, 200)						,	height_width_order=A				)
          self.assertEqual(A						,	{"height": 200, "width": 150}				)

          # Test an int representing the shortest edge and max_size which represents the longest edge
          a											=   get_size_dict(224						,	max_size=256						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224, "longest_edge": 256}				)

          # Test int with default_to_square=True and max_size fails
          with self.assertRaises(A				):
               get_size_dict(224						,	max_size=256						,	default_to_square=A				)

