

'''simple docstring'''

import argparse

import torch

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


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

def     __magic_name__       ( A					,   A					,   A							)			->		List[Any]:
	snake_case							 =				WavLMForSequenceClassification.from_pretrained(A					,   config=A							)
	snake_case							 =				downstream_dict['projector.weight']
	snake_case							 =				downstream_dict['projector.bias']
	snake_case							 =				downstream_dict['model.post_net.linear.weight']
	snake_case							 =				downstream_dict['model.post_net.linear.bias']
	return model

def     __magic_name__       ( A					,   A					,   A							)			->		Optional[int]:
	snake_case							 =				WavLMForAudioFrameClassification.from_pretrained(A					,   config=A							)
	snake_case							 =				downstream_dict['model.linear.weight']
	snake_case							 =				downstream_dict['model.linear.bias']
	return model

def     __magic_name__       ( A					,   A					,   A							)			->		Tuple:
	snake_case							 =				WavLMForXVector.from_pretrained(A					,   config=A							)
	snake_case							 =				downstream_dict['connector.weight']
	snake_case							 =				downstream_dict['connector.bias']
	for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
		snake_case							 =				downstream_dict[
		    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
		]
		snake_case							 =				downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

	snake_case							 =				downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
	snake_case							 =				downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
	snake_case							 =				downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
	snake_case							 =				downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
	snake_case							 =				downstream_dict['objective.W']
	return model

@torch.no_grad()
def     __magic_name__       ( A					,   A					,   A					,   A							)			->		List[str]:
	snake_case							 =				torch.load(A					,   map_location='cpu'							)

	snake_case							 =				checkpoint['Downstream']

	snake_case							 =				WavLMConfig.from_pretrained(A							)
	snake_case							 =				WavaVecaFeatureExtractor.from_pretrained(
	    A					,   return_attention_mask=A					,   do_normalize=A							)

	snake_case							 =				hf_config.architectures[0]
	if arch.endswith('ForSequenceClassification'							):
		snake_case							 =				convert_classification(A					,   A					,   A							)
	elif arch.endswith('ForAudioFrameClassification'							):
		snake_case							 =				convert_diarization(A					,   A					,   A							)
	elif arch.endswith('ForXVector'							):
		snake_case							 =				convert_xvector(A					,   A					,   A							)
	else:
		raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''							)

	if hf_config.use_weighted_layer_sum:
		snake_case							 =				checkpoint['Featurizer']['weights']

	hf_feature_extractor.save_pretrained(A							)
	hf_model.save_pretrained(A							)


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)








'''simple docstring'''

from itertools import compress, repeat
from math import ceil, sqrt

def     __magic_name__       ( A							)			->		list[int]:

	if num <= 2:
		return []
	if num == 3:
		return [2]

	# Odd sieve for numbers in range [3, num - 1]
	snake_case							 =				bytearray(b'\x01'							) * ((num >> 1) - 1)

	for i in range(3					,   int(sqrt(A							)							) + 1					,   2							):
		if sieve[(i >> 1) - 1]:
			snake_case							 =				i**2
			snake_case							 =				repeat(
			    0					,   ceil((num - i_squared) / (i << 1)							)							)

	return [2] + list(compress(range(3					,   A					,   2							)					,   A							)							)


if __name__ == "__main__":
						import doctest

						doctest.testmod()





