
"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__lowerCAmelCase							:  List[str] 				=		logging.get_logger(__name__)

__lowerCAmelCase							:  int 				=		['''UtteranceLevel''']

@torch.no_grad()
def __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    )       ->    Union[str, Any]:


		"""simple docstring"""





		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__":
					__lowerCAmelCase							:  Any 				=		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.''')
					__lowerCAmelCase							:  Optional[Any] 				=		parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






"""simple docstring"""




import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowerCAmelCase							:  Dict 				=		logging.getLogger()

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		a__   		=     argparse.ArgumentParser()
		parser.add_argument('''-f'''    )
		a__   		=     parser.parse_args()
		return args.f




class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







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

			'''simple docstring'''






			a__   		=     logging.StreamHandler(sys.stdout			)
			logger.addHandler(__magic_name__			)

			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					a__   		=     run_glue_with_pabee.main()
					for value in result.values():
							self.assertGreaterEqual(__magic_name__       ,    0.75			)




