
import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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




def 					lowerCamelCase__					(    a__	:	Optional[Any]						,   a__	:	Dict						,   a__	:	Union[str, Any]  )		->						Dict:
       UpperCamelCase_											=							WavLMForSequenceClassification.from_pretrained(a__						,   config=a__  )
       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"""]
       return model




def 					lowerCamelCase__					(    a__	:	List[Any]						,   a__	:	Union[str, Any]						,   a__	:	List[str]  )		->						List[Any]:
       UpperCamelCase_											=							WavLMForAudioFrameClassification.from_pretrained(a__						,   config=a__  )
       UpperCamelCase_											=							downstream_dict["""model.linear.weight"""]
       UpperCamelCase_											=							downstream_dict["""model.linear.bias"""]
       return model




def 					lowerCamelCase__					(    a__	:	Dict						,   a__	:	str						,   a__	:	List[Any]  )		->						List[str]:
       UpperCamelCase_											=							WavLMForXVector.from_pretrained(a__						,   config=a__  )
       UpperCamelCase_											=							downstream_dict["""connector.weight"""]
       UpperCamelCase_											=							downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
              UpperCamelCase_											=							downstream_dict[
                  f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              UpperCamelCase_											=							downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

       UpperCamelCase_											=							downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       UpperCamelCase_											=							downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       UpperCamelCase_											=							downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       UpperCamelCase_											=							downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       UpperCamelCase_											=							downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	int						,   a__	:	Any						,   a__	:	List[Any]						,   a__	:	List[Any]  )		->						str:
       UpperCamelCase_											=							torch.load(a__						,   map_location="""cpu"""  )

       UpperCamelCase_											=							checkpoint["""Downstream"""]

       UpperCamelCase_											=							WavLMConfig.from_pretrained(a__  )
       UpperCamelCase_											=							WavaVecaFeatureExtractor.from_pretrained(
           a__						,   return_attention_mask=a__						,   do_normalize=a__  )

       UpperCamelCase_											=							hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""  ):
              UpperCamelCase_											=							convert_classification(a__						,   a__						,   a__  )
       elif arch.endswith("""ForAudioFrameClassification"""  ):
              UpperCamelCase_											=							convert_diarization(a__						,   a__						,   a__  )
       elif arch.endswith("""ForXVector"""  ):
              UpperCamelCase_											=							convert_xvector(a__						,   a__						,   a__  )
       else:
              raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''  )

       if hf_config.use_weighted_layer_sum:
              UpperCamelCase_											=							checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(a__  )
       hf_model.save_pretrained(a__  )


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





import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   start_method="""spawn"""  )


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      _A							=       rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



