






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


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


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase__		,					config=UpperCamelCase__      )
					__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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase__		,					config=UpperCamelCase__      )
					__lowerCAmelCase					=							downstream_dict["""model.linear.weight"""]
					__lowerCAmelCase					=							downstream_dict["""model.linear.bias"""]
					return model


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							WavaVecaForXVector.from_pretrained(UpperCamelCase__		,					config=UpperCamelCase__      )
					__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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							torch.load(UpperCamelCase__		,					map_location='cpu'      )

					__lowerCAmelCase					=							checkpoint["""Downstream"""]

					__lowerCAmelCase					=							WavaVecaConfig.from_pretrained(UpperCamelCase__      )
					__lowerCAmelCase					=							WavaVecaFeatureExtractor.from_pretrained(
					    UpperCamelCase__		,					return_attention_mask=UpperCamelCase__		,					do_normalize=UpperCamelCase__      )

					__lowerCAmelCase					=							hf_config.architectures[0]
					if arch.endswith('ForSequenceClassification'      ):
										__lowerCAmelCase					=							convert_classification(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__      )
					elif arch.endswith('ForAudioFrameClassification'      ):
										__lowerCAmelCase					=							convert_diarization(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__      )
					elif arch.endswith('ForXVector'      ):
										__lowerCAmelCase					=							convert_xvector(UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__      )
					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(UpperCamelCase__      )
					hf_model.save_pretrained(UpperCamelCase__      )


if __name__ == "__main__":
		lowerCamelCase      				=     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.''')
		lowerCamelCase      				=     parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









from graphs.minimum_spanning_tree_kruskal import kruskal


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							9
					__lowerCAmelCase					=							[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					__lowerCAmelCase					=							kruskal(UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					assert sorted(UpperCAmelCase__      ) == sorted(UpperCAmelCase__      )






