






import torch

from accelerate import Accelerator, DistributedType





class 						_A:


				"""simple docstring"""


				def __enter__(      self					):
									if self.enabled:
														__A     :						Optional[Any]          =    self.model.no_sync()
														self.model_sync_obj.__enter__()

									return self



				def __exit__(      self					, _A					, _A					, _A					):
									if self.enabled:
														# Average all models on exit
														self._sync_and_avg_model_params()
														self.model_sync_obj.__exit__(_A					, _A					, _A					)



				def __init__(      self					, _A					, _A					, _A					, _A = True					):
									if accelerator.distributed_type not in [
									    DistributedType.NO,
									    DistributedType.MULTI_CPU,
									    DistributedType.MULTI_GPU,
									]:
														raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'					)
									__A     :						List[str]          =    enabled and accelerator.distributed_type != DistributedType.NO
									__A     :						Tuple          =    0
									if self.enabled:
														__A     :						List[Any]          =    accelerator
														__A     :						Optional[int]          =    model
														__A     :						int          =    local_sgd_steps



				def  UpperCAmelCase_		(      self					):
									self.num_steps += 1
									if not self.enabled:
														return

									if self.num_steps % self.local_sgd_steps == 0:
														self._sync_and_avg_model_params()





				def  UpperCAmelCase_		(      self					):
									self.accelerator.wait_for_everyone()
									with self.accelerator.autocast():
														for param in self.model.parameters():
																			__A     :						Tuple          =    self.accelerator.reduce(param.data					, reduction='mean'					)











import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase						:       Tuple        =					logging.get_logger(__name__)

UpperCAmelCase						:       List[Any]        =					['''UtteranceLevel''']







@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a  )				->				List[str]:
					__A     :						Any          =    torch.load(a ,						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     :						Dict          =    checkpoint['Downstream']

					__A     :						Optional[Any]          =    HubertConfig.from_pretrained(a  )
					__A     :						Tuple          =    HubertForSequenceClassification.from_pretrained(a ,						config=a  )
					__A     :						str          =    WavaVecaFeatureExtractor.from_pretrained(
					    a ,						return_attention_mask=a ,						do_normalize=a  )

					if hf_congfig.use_weighted_layer_sum:
										__A     :						Optional[Any]          =    checkpoint['Featurizer']['weights']

					__A     :						Dict          =    downstream_dict['projector.weight']
					__A     :						int          =    downstream_dict['projector.bias']
					__A     :						List[Any]          =    downstream_dict['model.post_net.linear.weight']
					__A     :						int          =    downstream_dict['model.post_net.linear.bias']

					hf_feature_extractor.save_pretrained(a  )
					hf_model.save_pretrained(a  )


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




