


"""simple docstring"""






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


__snake_case												=							logging.get_logger(__name__)

__snake_case												=							{
    # pcqm4mv1 now deprecated
    '''graphormer-base''': '''https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json''',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Optional[int]     =		'graphormer'
     A_							:						int     =		['past_key_values']


     def __init__(   self     ,					__UpperCAmelCase = 1     ,					__UpperCAmelCase = 512 * 9     ,					__UpperCAmelCase = 512 * 3     ,					__UpperCAmelCase = 512     ,					__UpperCAmelCase = 512     ,					__UpperCAmelCase = 512     ,					__UpperCAmelCase = 128     ,					__UpperCAmelCase = 5     ,					__UpperCAmelCase = 1024     ,					__UpperCAmelCase = "multi_hop"     ,					__UpperCAmelCase = 512     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = 12     ,					__UpperCAmelCase = 768     ,					__UpperCAmelCase = 768     ,					__UpperCAmelCase = 32     ,					__UpperCAmelCase = 0.1     ,					__UpperCAmelCase = 0.1     ,					__UpperCAmelCase = 0.0     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = "gelu"     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = 0     ,					__UpperCAmelCase = False     ,					__UpperCAmelCase = 0.0     ,					__UpperCAmelCase = 8     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=2     ,					**__UpperCAmelCase     ,					)							->				List[str]:
          _a     						=					num_classes
          _a     						=					num_atoms
          _a     						=					num_in_degree
          _a     						=					num_out_degree
          _a     						=					num_edges
          _a     						=					num_spatial
          _a     						=					num_edge_dis
          _a     						=					edge_type
          _a     						=					multi_hop_max_dist
          _a     						=					spatial_pos_max
          _a     						=					max_nodes
          _a     						=					num_hidden_layers
          _a     						=					embedding_dim
          _a     						=					embedding_dim
          _a     						=					ffn_embedding_dim
          _a     						=					num_attention_heads
          _a     						=					dropout
          _a     						=					attention_dropout
          _a     						=					layerdrop
          _a     						=					encoder_normalize_before
          _a     						=					pre_layernorm
          _a     						=					apply_graphormer_init
          _a     						=					activation_fn
          _a     						=					embed_scale
          _a     						=					freeze_embeddings
          _a     						=					num_trans_layers_to_freeze
          _a     						=					share_input_output_embed
          _a     						=					traceable
          _a     						=					q_noise
          _a     						=					qn_block_size

          # These parameters are here for future extensions
          # atm, the model only supports self attention
          _a     						=					kdim
          _a     						=					vdim
          _a     						=					self_attention
          _a     						=					bias

          super().__init__(
              pad_token_id=__UpperCAmelCase     ,					bos_token_id=__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)



"""simple docstring"""






import math
import os

import cva
import numpy as np

__snake_case												=							255.0
def    A_ ( _lowerCAmelCase  :       float,  _lowerCAmelCase  :       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_ ( ):






     """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()