


'''simple docstring'''

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

lowerCamelCase							:       Dict  						=     ["UtteranceLevel"]

@torch.no_grad()
def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A	):






       """simple docstring"""







       lowercase__				      =			torch.load(_lowerCAmelCase  ,			map_location='''cpu'''	)
       if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
              raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}"	)

       lowercase__				      =			checkpoint['''Downstream''']

       lowercase__				      =			HubertConfig.from_pretrained(_lowerCAmelCase	)
       lowercase__				      =			HubertForSequenceClassification.from_pretrained(_lowerCAmelCase  ,			config=_lowerCAmelCase	)
       lowercase__				      =			WavaVecaFeatureExtractor.from_pretrained(
           _lowerCAmelCase  ,			return_attention_mask=_lowerCAmelCase  ,			do_normalize=_lowerCAmelCase	)

       if hf_congfig.use_weighted_layer_sum:
              lowercase__				      =			checkpoint['''Featurizer''']['''weights''']

       lowercase__				      =			downstream_dict['''projector.weight''']
       lowercase__				      =			downstream_dict['''projector.bias''']
       lowercase__				      =			downstream_dict['''model.post_net.linear.weight''']
       lowercase__				      =			downstream_dict['''model.post_net.linear.bias''']

       hf_feature_extractor.save_pretrained(_lowerCAmelCase	)
       hf_model.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
 lowerCamelCase							:       Union[str, Any]  						=     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							:       Dict  						=     parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








'''simple docstring'''

import math
import os

import cva
import numpy as np

lowerCamelCase							:       Optional[Any]  						=     2_5_5.0

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	float:






       """simple docstring"""







       lowercase__				      =			np.mean((original - contrast) ** 2	)
       if mse == 0:
              return 100

       return 20 * math.logaa(PIXEL_MAX / math.sqrt(A	)	)

def _SCREAMING_SNAKE_CASE				()	->	None:






       """simple docstring"""







       lowercase__				      =			os.path.dirname(os.path.realpath(A	)	)
       # Loading images (original image and compressed image)
       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/original_image.png'''	)	)
       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/compressed_image.png'''	)  ,			1	)

       lowercase__				      =			cva.imread(os.path.join(A  ,			'''image_data/PSNR-example-base.png'''	)	)
       lowercase__				      =			cva.imread(
           os.path.join(A  ,			'''image_data/PSNR-example-comp-10.jpg'''	)  ,			1	)

       # Value expected: 29.73dB
       print('''-- First Test --'''	)
       print(f"PSNR value is {peak_signal_to_noise_ratio(A  ,			A	)} dB"	)

       # # Value expected: 31.53dB (Wikipedia Example)
       print('''\n-- Second Test --'''	)
       print(f"PSNR value is {peak_signal_to_noise_ratio(A  ,			A	)} dB"	)


if __name__ == "__main__":
 main()




