


'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification

def _SCREAMING_SNAKE_CASE				(A	):






       """simple docstring"""







       lowercase__				      =			[
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  ,			A	)

def _SCREAMING_SNAKE_CASE				(A	):






       """simple docstring"""







       lowercase__      ,lowercase__				      =			emb.weight.shape
       lowercase__				      =			nn.Linear(A  ,			A  ,			bias=A	)
       lowercase__				      =			emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE				(A  ,			A="uclanlp/plbart-base"  ,			A=False  ,			A=False	):






       """simple docstring"""







       lowercase__				      =			torch.load(A  ,			map_location='''cpu'''	)['''model''']
       remove_ignore_keys_(A	)
       lowercase__				      =			state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__				      =			PLBartConfig.from_pretrained(A  ,			vocab_size=A	)

       lowercase__				      =			state_dict['''decoder.embed_tokens.weight''']
       if not classification:
              lowercase__				      =			PLBartForConditionalGeneration(A	)
              model.model.load_state_dict(A	)
              if finetuned:
                     lowercase__				      =			make_linear_from_emb(model.model.shared	)

       else:
              lowercase__				      =			{}
              for key, value in state_dict.copy().items():
                     if key.startswith('''classification_heads.sentence_classification_head'''	):
                            lowercase__				      =			value
                            state_dict.pop(A	)
              lowercase__				      =			PLBartForSequenceClassification(A	)
              model.model.load_state_dict(A	)
              model.classification_head.load_state_dict(A	)

       return model


if __name__ == "__main__":
 lowerCamelCase							:       Union[str, Any]  						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config',
     default='uclanlp/plbart-base',
     type=str,
     help='Which huggingface architecture to use: plbart-base',
 )
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 parser.add_argument(
     '--classification', action='store_true', help='whether the model is a classification checkpoint'
 )
 lowerCamelCase							:       int  						=     parser.parse_args()
 lowerCamelCase							:       Optional[Any]  						=     convert_fairseq_plbart_checkpoint_from_disk(
     args.fairseq_path,
     hf_config_path=args.hf_config,
     finetuned=args.finetuned,
     classification=args.classification,
 )
 model.save_pretrained(args.pytorch_dump_folder_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()




