



import argparse

import torch

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


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

def       A_   (						__a  :					Tuple			,		__a  :					Optional[Any]			,		__a  :					Union[str, Any]		):

				"""simple docstring"""
				a__					   =		WavLMForSequenceClassification.from_pretrained(__a			,		config=__a		)
				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       A_   (						__a  :					str			,		__a  :					List[str]			,		__a  :					Tuple		):

				"""simple docstring"""
				a__					   =		WavLMForAudioFrameClassification.from_pretrained(__a			,		config=__a		)
				a__					   =		downstream_dict["""model.linear.weight"""]
				a__					   =		downstream_dict["""model.linear.bias"""]
				return model

def       A_   (						__a  :					Dict			,		__a  :					int			,		__a  :					Optional[Any]		):

				"""simple docstring"""
				a__					   =		WavLMForXVector.from_pretrained(__a			,		config=__a		)
				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       A_   (						__a  :					int			,		__a  :					str			,		__a  :					List[Any]			,		__a  :					Tuple		):

				"""simple docstring"""
				a__					   =		torch.load(__a			,		map_location="""cpu"""		)

				a__					   =		checkpoint["""Downstream"""]

				a__					   =		WavLMConfig.from_pretrained(__a		)
				a__					   =		WavaVecaFeatureExtractor.from_pretrained(
				    __a			,		return_attention_mask=__a			,		do_normalize=__a		)

				a__					   =		hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""		):
								a__					   =		convert_classification(__a			,		__a			,		__a		)
				elif arch.endswith("""ForAudioFrameClassification"""		):
								a__					   =		convert_diarization(__a			,		__a			,		__a		)
				elif arch.endswith("""ForXVector"""		):
								a__					   =		convert_xvector(__a			,		__a			,		__a		)
				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(__a		)
				hf_model.save_pretrained(__a		)


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



'''simple docstring'''





import math
from timeit import timeit

def   A			(							A_				:       int							):
						snake_case							:  List[str]			=     0
						snake_case							:  Any			=     abs(A_							)
						while True:
												snake_case							:  List[Any]			=     n // 10
												digits += 1
												if n == 0:
																		break
						return digits

def   A			(							A_				:       int							):
						return 1 if n == 0 else math.floor(math.log(abs(A_							)     ,				10							) + 1							)

def   A			(							A_				:       int							):
						return len(str(abs(A_							)							)							)

def   A			(							):
						from collections.abc import Callable

						def benchmark_a_function(A_				:       Callable     ,				A_				:       int							) -> None:
												snake_case							:  Optional[int]			=     F"""{func.__name__}({value})"""
												snake_case							:  Optional[int]			=     timeit(F"""__main__.{call}"""     ,				setup='''import __main__'''							)
												print(F"""{call}: {func(A_							)} -- {timing} seconds"""							)

						for value in (262144, 1125899906842624, 1267650600228229401496703205376):
												for func in (num_digits, num_digits_fast, num_digits_faster):
																		benchmark_a_function(A_     ,				A_							)
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()

