




import argparse

import torch

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


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




def 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					int  ,			A__       :					Any	):





       '''simple docstring'''



       __lowerCamelCase   =						WavaVecaForSequenceClassification.from_pretrained(A__  ,			config=A__	)
       __lowerCamelCase   =						downstream_dict["""projector.weight"""]
       __lowerCamelCase   =						downstream_dict["""projector.bias"""]
       __lowerCamelCase   =						downstream_dict["""model.post_net.linear.weight"""]
       __lowerCamelCase   =						downstream_dict["""model.post_net.linear.bias"""]
       return model




def 					lowerCamelCase__       (						A__       :					Optional[Any]  ,			A__       :					Optional[Any]  ,			A__       :					Union[str, Any]	):





       '''simple docstring'''



       __lowerCamelCase   =						WavaVecaForAudioFrameClassification.from_pretrained(A__  ,			config=A__	)
       __lowerCamelCase   =						downstream_dict["""model.linear.weight"""]
       __lowerCamelCase   =						downstream_dict["""model.linear.bias"""]
       return model




def 					lowerCamelCase__       (						A__       :					Optional[int]  ,			A__       :					Tuple  ,			A__       :					Dict	):





       '''simple docstring'''



       __lowerCamelCase   =						WavaVecaForXVector.from_pretrained(A__  ,			config=A__	)
       __lowerCamelCase   =						downstream_dict["""connector.weight"""]
       __lowerCamelCase   =						downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
              __lowerCamelCase   =						downstream_dict[
                  f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
              ]
              __lowerCamelCase   =						downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

       __lowerCamelCase   =						downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       __lowerCamelCase   =						downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       __lowerCamelCase   =						downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       __lowerCamelCase   =						downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       __lowerCamelCase   =						downstream_dict["""objective.W"""]
       return model




@torch.no_grad()
def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					Dict  ,			A__       :					List[str]  ,			A__       :					Tuple	):





       '''simple docstring'''



       __lowerCamelCase   =						torch.load(A__  ,			map_location="""cpu"""	)

       __lowerCamelCase   =						checkpoint["""Downstream"""]

       __lowerCamelCase   =						WavaVecaConfig.from_pretrained(A__	)
       __lowerCamelCase   =						WavaVecaFeatureExtractor.from_pretrained(
           A__  ,			return_attention_mask=A__  ,			do_normalize=A__	)

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

       if hf_config.use_weighted_layer_sum:
              __lowerCamelCase   =						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)





from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						bool:
    _lowercase       :							int      		=		int(number**0.5 )
    return number == sq * sq



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						tuple[int, int]:
    _lowercase       :							int      		=		x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
    _lowercase       :							int      		=		x_den * y_den * z_den
    _lowercase       :							int      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
    top //= hcf
    bottom //= hcf
    return top, bottom



def 				UpperCamelCase_(      lowerCamelCase_ = 35 )    ->						int:
    _lowercase       :							set      		=		set()
    _lowercase       :							int
    _lowercase       :							Fraction      		=		Fraction(0 )
    _lowercase       :							tuple[int, int]

    for x_num in range(1							, order + 1 ):
        for x_den in range(x_num + 1							, order + 1 ):
            for y_num in range(1							, order + 1 ):
                for y_den in range(y_num + 1							, order + 1 ):
                    # n=1
                    _lowercase       :							int      		=		x_num * y_den + x_den * y_num
                    _lowercase       :							int      		=		x_den * y_den
                    _lowercase       :							str      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _lowercase       :							List[Any]      		=		add_three(
                            lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                        unique_s.add(lowerCamelCase_ )

                    # n=2
                    _lowercase       :							Dict      		=		(
                        x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                    )
                    _lowercase       :							List[Any]      		=		x_den * x_den * y_den * y_den
                    if is_sq(lowerCamelCase_ ) and is_sq(lowerCamelCase_ ):
                        _lowercase       :							Tuple      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							int      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							Any      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _lowercase       :							Optional[int]      		=		add_three(
                                lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                            unique_s.add(lowerCamelCase_ )

                    # n=-1
                    _lowercase       :							Any      		=		x_num * y_num
                    _lowercase       :							str      		=		x_den * y_num + x_num * y_den
                    _lowercase       :							Any      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                    z_num //= hcf
                    z_den //= hcf
                    if 0 < z_num < z_den <= order:
                        _lowercase       :							int      		=		add_three(
                            lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                        unique_s.add(lowerCamelCase_ )

                    # n=2
                    _lowercase       :							str      		=		x_num * x_num * y_num * y_num
                    _lowercase       :							Optional[Any]      		=		(
                        x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                    )
                    if is_sq(lowerCamelCase_ ) and is_sq(lowerCamelCase_ ):
                        _lowercase       :							Tuple      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							List[str]      		=		int(sqrt(lowerCamelCase_ ) )
                        _lowercase       :							Union[str, Any]      		=		gcd(lowerCamelCase_							, lowerCamelCase_ )
                        z_num //= hcf
                        z_den //= hcf
                        if 0 < z_num < z_den <= order:
                            _lowercase       :							Tuple      		=		add_three(
                                lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                            unique_s.add(lowerCamelCase_ )

    for num, den in unique_s:
        total += Fraction(lowerCamelCase_							, lowerCamelCase_ )

    return total.denominator + total.numerator


if __name__ == "__main__":
 print(F"{solution() = }")


