import argparse

import torch

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


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

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			List[Any]:
							lowerCAmelCase				=	WavLMForSequenceClassification.from_pretrained(snake_case__     ,						config=snake_case__							)
							lowerCAmelCase				=	downstream_dict['''projector.weight''']
							lowerCAmelCase				=	downstream_dict['''projector.bias''']
							lowerCAmelCase				=	downstream_dict['''model.post_net.linear.weight''']
							lowerCAmelCase				=	downstream_dict['''model.post_net.linear.bias''']
							return model

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			str:
							lowerCAmelCase				=	WavLMForAudioFrameClassification.from_pretrained(snake_case__     ,						config=snake_case__							)
							lowerCAmelCase				=	downstream_dict['''model.linear.weight''']
							lowerCAmelCase				=	downstream_dict['''model.linear.bias''']
							return model

def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			int:
							lowerCAmelCase				=	WavLMForXVector.from_pretrained(snake_case__     ,						config=snake_case__							)
							lowerCAmelCase				=	downstream_dict['''connector.weight''']
							lowerCAmelCase				=	downstream_dict['''connector.bias''']
							for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
														lowerCAmelCase				=	downstream_dict[
														    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
														]
														lowerCAmelCase				=	downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

							lowerCAmelCase				=	downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
							lowerCAmelCase				=	downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
							lowerCAmelCase				=	downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
							lowerCAmelCase				=	downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
							lowerCAmelCase				=	downstream_dict['''objective.W''']
							return model

@torch.no_grad()
def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)  ->			List[str]:
							lowerCAmelCase				=	torch.load(snake_case__     ,						map_location='''cpu'''							)

							lowerCAmelCase				=	checkpoint['''Downstream''']

							lowerCAmelCase				=	WavLMConfig.from_pretrained(snake_case__							)
							lowerCAmelCase				=	WavaVecaFeatureExtractor.from_pretrained(
							    snake_case__     ,						return_attention_mask=snake_case__     ,						do_normalize=snake_case__							)

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

							if hf_config.use_weighted_layer_sum:
														lowerCAmelCase				=	checkpoint['''Featurizer''']['''weights''']

							hf_feature_extractor.save_pretrained(snake_case__							)
							hf_model.save_pretrained(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__    :       List[Any]						=			parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
	lowercase__    :       List[Any]						=			[]

	lowercase__    :       Optional[Any]						=			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
	lowercase__    :       Optional[int]						=			glob.glob(pattern)
	# TODO: deal with TF/Flax too
	lowercase__    :       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:
		lowercase__    :       Tuple						=			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:
				lowercase__    :       Union[str, Any]						=			tester_class(parent=None)
			except Exception:
				continue
			if hasattr(tester, '''get_config'''):
				lowercase__    :       Union[str, Any]						=			tester.get_config()
				for k, v in config.to_dict().items():
					if isinstance(v, int):
						lowercase__    :       int						=			None
						if k in ["vocab_size"]:
							lowercase__    :       Optional[int]						=			1_0_0
						elif k in ["max_position_embeddings"]:
							lowercase__    :       Union[str, Any]						=			1_2_8
						elif k in ["hidden_size", "d_model"]:
							lowercase__    :       Optional[Any]						=			4_0
						elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
							lowercase__    :       Dict						=			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))



