



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)






UpperCAmelCase							=       (1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
UpperCAmelCase							=       (-153, -1, 0, 1.2, 200, "A", [], {}, None)

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Initialization of sum and number of digits.
				a__					   =		0
				a__					   =		0
				a__					   =		n
				# Calculation of digits of the number
				while temp > 0:
								number_of_digits += 1
								temp //= 10
				# Dividing number into separate digits and find Armstrong number
				a__					   =		n
				while temp > 0:
								a__					   =		temp % 10
								total += rem**number_of_digits
								temp //= 10
				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False

				# Init a "histogram" of the digits
				a__					   =		[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
				a__					   =		0
				a__					   =		0
				a__					   =		n
				while temp > 0:
								a__   ,      a__					   =		divmod(__a			,		10		)
								digit_histogram[rem] += 1
								digit_total += 1

				for cnt, i in zip(__a			,		range(len(__a		)		)		):
								total += cnt * i**digit_total

				return n == total

def       A_   (						__a  :					int		):

				"""simple docstring"""
				if not isinstance(__a			,		__a		) or n < 1:
								return False
				a__					   =		len(str(__a		)		)  # the power that all digits will be raised to
				# check if sum of each digit multiplied expo times is equal to number
				return n == sum(int(__a		) ** expo for i in str(__a		)		)

def       A_   (						):

				"""simple docstring"""
				a__					   =		int(input("""Enter an integer to see if it is an Armstrong number: """		).strip()		)
				print(F'''{num} is {"" if armstrong_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if narcissistic_number(__a		) else "not "}an Armstrong number.'''		)
				print(F'''{num} is {"" if pluperfect_number(__a		) else "not "}an Armstrong number.'''		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

