




import argparse

import torch

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


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





def 							A__   (snake_case      :		Dict		,  snake_case      :		Optional[int]		,  snake_case      :		Optional[int]							)    ->	Any:
					__UpperCamelCase				:  Any								=			WavLMForSequenceClassification.from_pretrained(snake_case		,  config=snake_case							)
					__UpperCamelCase				:  int								=			downstream_dict["""projector.weight"""]
					__UpperCamelCase				:  Union[str, Any]								=			downstream_dict["""projector.bias"""]
					__UpperCamelCase				:  List[Any]								=			downstream_dict["""model.post_net.linear.weight"""]
					__UpperCamelCase				:  Any								=			downstream_dict["""model.post_net.linear.bias"""]
					return model





def 							A__   (snake_case      :		Optional[Any]		,  snake_case      :		List[str]		,  snake_case      :		Optional[Any]							)    ->	Union[str, Any]:
					__UpperCamelCase				:  int								=			WavLMForAudioFrameClassification.from_pretrained(snake_case		,  config=snake_case							)
					__UpperCamelCase				:  int								=			downstream_dict["""model.linear.weight"""]
					__UpperCamelCase				:  Dict								=			downstream_dict["""model.linear.bias"""]
					return model





def 							A__   (snake_case      :		Union[str, Any]		,  snake_case      :		Dict		,  snake_case      :		str							)    ->	Any:
					__UpperCamelCase				:  Tuple								=			WavLMForXVector.from_pretrained(snake_case		,  config=snake_case							)
					__UpperCamelCase				:  Any								=			downstream_dict["""connector.weight"""]
					__UpperCamelCase				:  Dict								=			downstream_dict["""connector.bias"""]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
										__UpperCamelCase				:  int								=			downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										__UpperCamelCase				:  Optional[int]								=			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

					__UpperCamelCase				:  Any								=			downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
					__UpperCamelCase				:  List[str]								=			downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
					__UpperCamelCase				:  Union[str, Any]								=			downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
					__UpperCamelCase				:  Optional[Any]								=			downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
					__UpperCamelCase				:  Optional[int]								=			downstream_dict["""objective.W"""]
					return model





@torch.no_grad()
def 							A__   (snake_case      :		Any		,  snake_case      :		Any		,  snake_case      :		Optional[Any]		,  snake_case      :		Any							)    ->	Tuple:
					__UpperCamelCase				:  str								=			torch.load(snake_case		,  map_location="""cpu"""							)

					__UpperCamelCase				:  Tuple								=			checkpoint["""Downstream"""]

					__UpperCamelCase				:  Optional[int]								=			WavLMConfig.from_pretrained(snake_case							)
					__UpperCamelCase				:  int								=			WavaVecaFeatureExtractor.from_pretrained(
					    snake_case		,  return_attention_mask=snake_case		,  do_normalize=snake_case							)

					__UpperCamelCase				:  str								=			hf_config.architectures[0]
					if arch.endswith("""ForSequenceClassification"""							):
										__UpperCamelCase				:  Tuple								=			convert_classification(snake_case		,  snake_case		,  snake_case							)
					elif arch.endswith("""ForAudioFrameClassification"""							):
										__UpperCamelCase				:  Dict								=			convert_diarization(snake_case		,  snake_case		,  snake_case							)
					elif arch.endswith("""ForXVector"""							):
										__UpperCamelCase				:  str								=			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:
										__UpperCamelCase				:  str								=			checkpoint["""Featurizer"""]["""weights"""]

					hf_feature_extractor.save_pretrained(snake_case							)
					hf_model.save_pretrained(snake_case							)


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 os





def 							A__   ()    ->	int:
					__UpperCamelCase				:  int								=			os.path.dirname(os.path.realpath(snake_case							)							)
					__UpperCamelCase				:  List[str]								=			os.path.join(snake_case		,  """triangle.txt"""							)

					with open(snake_case							) as in_file:
										__UpperCamelCase				:  Optional[Any]								=			[[int(snake_case							) for i in line.split()] for line in in_file]

					while len(snake_case							) != 1:
										__UpperCamelCase				:  Union[str, Any]								=			triangle.pop()
										__UpperCamelCase				:  List[str]								=			triangle[-1]
										for j in range(len(snake_case							) - 1							):
															curr_row[j] += max(last_row[j]		,  last_row[j + 1]							)
					return triangle[0][0]


if __name__ == "__main__":
	print(solution())






