





import math
import os

import cva
import numpy as np

snake_case__      :							List[Any]									=  255.0



def _a       (			lowerCamelCase:     float      ,       lowerCamelCase:     float				)			->		float:







      '''simple docstring'''






      __A      =       np.mean((original - contrast) ** 2				)
      if mse == 0:
            return 1_00

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



def _a       (			)			->		None:







      '''simple docstring'''






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

      __A      =       cva.imread(os.path.join(lowerCamelCase      ,       '''image_data/PSNR-example-base.png'''				)				)
      __A      =       cva.imread(
          os.path.join(lowerCamelCase      ,       '''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(lowerCamelCase      ,       lowerCamelCase				)} dB"""				)

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


if __name__ == "__main__":
   main()












from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

snake_case__      :							List[str]									=  {
    'uw-madison/nystromformer-512': 'https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class A_      (    _lowerCamelCase     ):
   lowerCAmelCase__       =				"""nystromformer"""




   def __init__(self			:Union[str, Any]					,     _UpperCamelCase			:Optional[int]=3_0000					,     _UpperCamelCase			:Optional[int]=768					,     _UpperCamelCase			:List[str]=12					,     _UpperCamelCase			:Dict=12					,     _UpperCamelCase			:Any=3072					,     _UpperCamelCase			:List[str]="gelu_new"					,     _UpperCamelCase			:str=0.1					,     _UpperCamelCase			:Dict=0.1					,     _UpperCamelCase			:List[Any]=510					,     _UpperCamelCase			:str=2					,     _UpperCamelCase			:List[str]=64					,     _UpperCamelCase			:List[str]=64					,     _UpperCamelCase			:Optional[int]=65					,     _UpperCamelCase			:Optional[int]=False					,     _UpperCamelCase			:Optional[int]=0.0_2					,     _UpperCamelCase			:Dict=1e-5					,     _UpperCamelCase			:Tuple=1					,     _UpperCamelCase			:Union[str, Any]=0					,     _UpperCamelCase			:Optional[int]=2					,     **_UpperCamelCase			:Dict					,     )->      Any:
         __A      =       vocab_size
         __A      =       max_position_embeddings
         __A      =       hidden_size
         __A      =       num_hidden_layers
         __A      =       num_attention_heads
         __A      =       intermediate_size
         __A      =       hidden_act
         __A      =       hidden_dropout_prob
         __A      =       attention_probs_dropout_prob
         __A      =       initializer_range
         __A      =       type_vocab_size
         __A      =       segment_means_seq_len
         __A      =       num_landmarks
         __A      =       conv_kernel_size
         __A      =       inv_coeff_init_option
         __A      =       layer_norm_eps
         super().__init__(pad_token_id=_UpperCamelCase					,     bos_token_id=_UpperCamelCase					,     eos_token_id=_UpperCamelCase					,     **_UpperCamelCase				)





