




"""simple docstring"""




import argparse

import torch

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


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







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  str								=       WavaVecaForSequenceClassification.from_pretrained(__UpperCamelCase	,				config=__UpperCamelCase	)
			__lowercase			:  Any								=       downstream_dict['''projector.weight''']
			__lowercase			:  Dict								=       downstream_dict['''projector.bias''']
			__lowercase			:  str								=       downstream_dict['''model.post_net.linear.weight''']
			__lowercase			:  Any								=       downstream_dict['''model.post_net.linear.bias''']
			return model







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  str								=       WavaVecaForAudioFrameClassification.from_pretrained(__UpperCamelCase	,				config=__UpperCamelCase	)
			__lowercase			:  Optional[int]								=       downstream_dict['''model.linear.weight''']
			__lowercase			:  Dict								=       downstream_dict['''model.linear.bias''']
			return model







def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  int								=       WavaVecaForXVector.from_pretrained(__UpperCamelCase	,				config=__UpperCamelCase	)
			__lowercase			:  Optional[int]								=       downstream_dict['''connector.weight''']
			__lowercase			:  List[str]								=       downstream_dict['''connector.bias''']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
						__lowercase			:  Dict								=       downstream_dict[
						    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
						]
						__lowercase			:  Optional[Any]								=       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

			__lowercase			:  str								=       downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
			__lowercase			:  int								=       downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
			__lowercase			:  str								=       downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
			__lowercase			:  Tuple								=       downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
			__lowercase			:  Optional[int]								=       downstream_dict['''objective.W''']
			return model







@torch.no_grad()
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	):
			__lowercase			:  int								=       torch.load(__UpperCamelCase	,				map_location='''cpu'''	)

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

			__lowercase			:  Tuple								=       WavaVecaConfig.from_pretrained(__UpperCamelCase	)
			__lowercase			:  str								=       WavaVecaFeatureExtractor.from_pretrained(
			    __UpperCamelCase	,				return_attention_mask=__UpperCamelCase	,				do_normalize=__UpperCamelCase	)

			__lowercase			:  List[Any]								=       hf_config.architectures[0]
			if arch.endswith('''ForSequenceClassification'''	):
						__lowercase			:  str								=       convert_classification(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
			elif arch.endswith('''ForAudioFrameClassification'''	):
						__lowercase			:  int								=       convert_diarization(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	)
			elif arch.endswith('''ForXVector'''	):
						__lowercase			:  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:
						__lowercase			:  Tuple								=       checkpoint['''Featurizer''']['''weights''']

			hf_feature_extractor.save_pretrained(__UpperCamelCase	)
			hf_model.save_pretrained(__UpperCamelCase	)


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)








from collections.abc import Callable

import numpy as np






def  lowerCamelCase_	(      lowerCAmelCase__	:	Callable			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float			,	lowerCAmelCase__	:	float				)       ->							np.array:

							'''simple docstring'''






							A      						=    int(np.ceil((x_end - xa) / step_size				)				)
							A      						=    np.zeros((n + 1,)				)
							A      						=    ya
							A      						=    xa

							for k in range(lowerCAmelCase__				):
														A      						=    y[k] + step_size * ode_func(lowerCAmelCase__			,	y[k]				)
														A      						=    y[k] + (
														    (step_size / 2) * (ode_func(lowerCAmelCase__			,	y[k]				) + ode_func(x + step_size			,	lowerCAmelCase__				))
														)
														x += step_size

							return y


if __name__ == "__main__":
				import doctest

				doctest.testmod()