



import argparse

import torch

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


logging.set_verbosity_info()
__snake_case     :Optional[Any]			       =logging.get_logger(__name__)






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	Optional[Any]				)       ->							str:

							'''simple docstring'''






							A      						=    WavaVecaForSequenceClassification.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  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[str]				)       ->							Tuple:

							'''simple docstring'''






							A      						=    WavaVecaForAudioFrameClassification.from_pretrained(lowerCAmelCase__			,	config=lowerCAmelCase__				)
							A      						=    downstream_dict['model.linear.weight']
							A      						=    downstream_dict['model.linear.bias']
							return model






def  lowerCamelCase_	(      lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	Dict				)       ->							Optional[Any]:

							'''simple docstring'''






							A      						=    WavaVecaForXVector.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  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[Any]			,	lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	str				)       ->							Tuple:

							'''simple docstring'''






							A      						=    torch.load(lowerCAmelCase__			,	map_location='cpu'				)

							A      						=    checkpoint['Downstream']

							A      						=    WavaVecaConfig.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__":
				__snake_case     :Tuple			       =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.')
				__snake_case     :str			       =parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



"""simple docstring"""


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			return math.sqrt(sum(pow(a - b       ,					2 ) for a, b in zip(_UpperCamelCase       ,					_UpperCamelCase ) ) )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			if dataset.ndim != value_array.ndim:
						__lowerCAmelCase							       =     (
						    "Wrong input data's dimensions... "
						    f"dataset : {dataset.ndim}, value_array : {value_array.ndim}"
						)
						raise ValueError(_UpperCamelCase )

			try:
						if dataset.shape[1] != value_array.shape[1]:
									__lowerCAmelCase							       =     (
									    "Wrong input data's shape... "
									    f"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"
									)
									raise ValueError(_UpperCamelCase )
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError("Wrong shape" )

			if dataset.dtype != value_array.dtype:
						__lowerCAmelCase							       =     (
						    "Input data have different datatype... "
						    f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
						)
						raise TypeError(_UpperCamelCase )

			__lowerCAmelCase							       =     []

			for value in value_array:
						__lowerCAmelCase							       =     euclidean(_UpperCamelCase       ,					dataset[0] )
						__lowerCAmelCase							       =     dataset[0].tolist()

						for dataset_value in dataset[1:]:
									__lowerCAmelCase							       =     euclidean(_UpperCamelCase       ,					_UpperCamelCase )

									if dist > temp_dist:
												__lowerCAmelCase							       =     temp_dist
												__lowerCAmelCase							       =     dataset_value.tolist()

						answer.append([vector, dist] )

			return answer


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			return np.dot(_UpperCamelCase       ,					_UpperCamelCase ) / (norm(_UpperCamelCase ) * norm(_UpperCamelCase ))


if __name__ == "__main__":
	import doctest

	doctest.testmod()




