




import argparse

import torch

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


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





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> List[Any]:
	a		       =							WavLMForSequenceClassification.from_pretrained(__lowerCamelCase							,    config=__lowerCamelCase					)
	a		       =							downstream_dict["""projector.weight"""]
	a		       =							downstream_dict["""projector.bias"""]
	a		       =							downstream_dict["""model.post_net.linear.weight"""]
	a		       =							downstream_dict["""model.post_net.linear.bias"""]
	return model





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Optional[Any]:
	a		       =							WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase							,    config=__lowerCamelCase					)
	a		       =							downstream_dict["""model.linear.weight"""]
	a		       =							downstream_dict["""model.linear.bias"""]
	return model





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

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





@torch.no_grad()
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Tuple:
	a		       =							torch.load(__lowerCamelCase							,    map_location="""cpu"""					)

	a		       =							checkpoint["""Downstream"""]

	a		       =							WavLMConfig.from_pretrained(__lowerCamelCase					)
	a		       =							WavaVecaFeatureExtractor.from_pretrained(
	    __lowerCamelCase							,    return_attention_mask=__lowerCamelCase							,    do_normalize=__lowerCamelCase					)

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

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

	hf_feature_extractor.save_pretrained(__lowerCamelCase					)
	hf_model.save_pretrained(__lowerCamelCase					)


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      : Union[str, Any]					      =   parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







from itertools import combinations, product





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	a		       =							xa * xa + ya * ya
	a		       =							xa * xa + ya * ya
	a		       =							(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)





def 	__A	(			__lowerCamelCase = 50					)   -> int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1					)							,    repeat=2					)							,    2					)
	    if is_right(*__lowerCamelCase							,    *__lowerCamelCase					)					)


if __name__ == "__main__":
		print(F'{solution() = }')

