



"""simple docstring"""
import math
import os

import cva
import numpy as np

__a           =							2_55.0
def   A_    (     _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:Optional[int]		=     np.mean((original - contrast) ** 2      )
						if mse == 0:
												return 100

						return 20 * math.logaa(PIXEL_MAX / math.sqrt(_lowercase      )      )
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:Any		=     os.path.dirname(os.path.realpath(_lowercase      )      )
						# Loading images (original image and compressed image)
						snake_case_					:Optional[int]		=     cva.imread(os.path.join(_lowercase,   """image_data/original_image.png"""      )      )
						snake_case_					:Union[str, Any]		=     cva.imread(os.path.join(_lowercase,   """image_data/compressed_image.png"""      ),   1      )

						snake_case_					:Any		=     cva.imread(os.path.join(_lowercase,   """image_data/PSNR-example-base.png"""      )      )
						snake_case_					:Optional[int]		=     cva.imread(
						    os.path.join(_lowercase,   """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(_lowercase,   _lowercase      )} dB"""      )

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


if __name__ == "__main__":
						main()







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


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """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 SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

