





import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
				UpperCAmelCase						:       str        =					[]

				UpperCAmelCase						:       Optional[int]        =					os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
				UpperCAmelCase						:       Optional[Any]        =					glob.glob(pattern)
				# TODO: deal with TF/Flax too
				UpperCAmelCase						:       Dict        =					[
				    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
				]

				for test_file in test_files:
								UpperCAmelCase						:       str        =					get_tester_classes(test_file)
								for tester_class in tester_classes:
												# A few tester classes don't have `parent` parameter in `__init__`.
												# TODO: deal this better
												try:
																UpperCAmelCase						:       Optional[Any]        =					tester_class(parent=None)
												except Exception:
																continue
												if hasattr(tester, '''get_config'''):
																UpperCAmelCase						:       List[Any]        =					tester.get_config()
																for k, v in config.to_dict().items():
																				if isinstance(v, int):
																								UpperCAmelCase						:       str        =					None
																								if k in ["vocab_size"]:
																												UpperCAmelCase						:       Dict        =					1_00
																								elif k in ["max_position_embeddings"]:
																												UpperCAmelCase						:       Tuple        =					1_28
																								elif k in ["hidden_size", "d_model"]:
																												UpperCAmelCase						:       int        =					40
																								elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																												UpperCAmelCase						:       Optional[int]        =					5
																								if target is not None and v > target:
																												failures.append(
																												    F"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
																												    F""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
																												    F""" than {target}."""
																												)

				if len(failures) > 0:
								raise Exception(F"""There were {len(failures)} failures:\n""" + '''\n'''.join(failures))











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)




