





import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				snake_case_						:   int           =  UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase			,    config=_UpperCAmelCase			)
				snake_case_						:   Any           =  downstream_dict['projector.weight']
				snake_case_						:   Tuple           =  downstream_dict['projector.bias']
				snake_case_						:   str           =  downstream_dict['model.post_net.linear.weight']
				snake_case_						:   Dict           =  downstream_dict['model.post_net.linear.bias']
				return model






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,    config=_UpperCAmelCase			)
				snake_case_						:   List[Any]           =  downstream_dict['model.linear.weight']
				snake_case_						:   Optional[int]           =  downstream_dict['model.linear.bias']
				return model






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   Any           =  UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase			,    config=_UpperCAmelCase			)
				snake_case_						:   List[Any]           =  downstream_dict['connector.weight']
				snake_case_						:   Optional[Any]           =  downstream_dict['connector.bias']
				for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
								snake_case_						:   Optional[Any]           =  downstream_dict[
								    f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
								]
								snake_case_						:   Dict           =  downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

				snake_case_						:   List[str]           =  downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
				snake_case_						:   List[Any]           =  downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
				snake_case_						:   str           =  downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
				snake_case_						:   int           =  downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
				snake_case_						:   List[str]           =  downstream_dict['objective.W']
				return model






@torch.no_grad()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				snake_case_						:   Any           =  torch.load(_UpperCAmelCase			,    map_location='''cpu'''			)

				snake_case_						:   Union[str, Any]           =  checkpoint['Downstream']

				snake_case_						:   int           =  UniSpeechSatConfig.from_pretrained(_UpperCAmelCase			)
				snake_case_						:   Dict           =  WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase			,    return_attention_mask=_UpperCAmelCase			,    do_normalize=_UpperCAmelCase			)

				snake_case_						:   Union[str, Any]           =  hf_config.architectures[0]
				if arch.endswith('''ForSequenceClassification'''			):
								snake_case_						:   Optional[int]           =  convert_classification(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			)
				elif arch.endswith('''ForAudioFrameClassification'''			):
								snake_case_						:   Any           =  convert_diarization(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			)
				elif arch.endswith('''ForXVector'''			):
								snake_case_						:   Union[str, Any]           =  convert_xvector(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			)
				else:
								raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''			)

				if hf_config.use_weighted_layer_sum:
								snake_case_						:   Tuple           =  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)







import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						tuple:






				"""simple docstring"""


				return (data["data"], data["target"])






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						XGBClassifier:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  XGBClassifier()
				classifier.fit(_UpperCamelCase			,    _UpperCamelCase			)
				return classifier






def 					lowerCamelCase_						(    )		->						None:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  load_iris()
				snake_case_      ,							snake_case_						:   str           =  data_handling(_UpperCamelCase			)
				snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Dict           =  train_test_split(
				    _UpperCamelCase			,    _UpperCamelCase			,    test_size=0.25			)

				snake_case_						:   List[str]           =  iris['''target_names''']

				# Create an XGBoost Classifier from the training data
				snake_case_						:   int           =  xgboost(_UpperCamelCase			,    _UpperCamelCase			)

				# Display the confusion matrix of the classifier with both training and test sets
				ConfusionMatrixDisplay.from_estimator(
				    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    display_labels=_UpperCamelCase			,    cmap='''Blues'''			,    normalize='''true'''			,    )
				plt.title('''Normalized Confusion Matrix - IRIS Dataset'''			)
				plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()
