





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowercase     :			Dict     						=	logging.get_logger(__name__)

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


@torch.no_grad()
def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    torch.load(SCREAMING_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(SCREAMING_SNAKE_CASE__       )
       _UpperCamelCase														=    HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__     ,							config=SCREAMING_SNAKE_CASE__       )
       _UpperCamelCase														=    WavaVecaFeatureExtractor.from_pretrained(
           SCREAMING_SNAKE_CASE__     ,							return_attention_mask=SCREAMING_SNAKE_CASE__     ,							do_normalize=SCREAMING_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(SCREAMING_SNAKE_CASE__       )
       hf_model.save_pretrained(SCREAMING_SNAKE_CASE__       )


if __name__ == "__main__":
       lowercase     :			List[str]     						=	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     :			str     						=	parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A									=		50_0000

_A			,	_A									=		os.path.split(__file__)
_A									=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.select(range(0     ,	len(SCREAMING_SNAKE_CASE__							)     ,	2							)							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.sort('numbers'							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.shuffle()

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.train_test_split(0.1							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset     ,	SCREAMING_SNAKE_CASE__     :   int=10							):
     for shard_id in range(SCREAMING_SNAKE_CASE__							):
          __UpperCamelCase       				=dataset.shard(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				={'num examples': SPEED_TEST_N_EXAMPLES}
     __UpperCamelCase       				=(select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('generating dataset'							)
          __UpperCamelCase       				=datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
          __UpperCamelCase       				=generate_example_dataset(
              os.path.join(SCREAMING_SNAKE_CASE__     ,	'dataset.arrow'							)     ,	SCREAMING_SNAKE_CASE__     ,	num_examples=SCREAMING_SNAKE_CASE__							)
          print('Functions'							)
          for func in functions:
               print(func.__name__							)
               __UpperCamelCase       				=func(SCREAMING_SNAKE_CASE__							)

     with open(SCREAMING_SNAKE_CASE__     ,	'wb'							) as f:
          f.write(json.dumps(SCREAMING_SNAKE_CASE__							).encode('utf-8'							)							)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()





