

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


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				'qdqbert'


    def __init__( self					:			Any,lowercase_					:			int=3_0_5_2_2,lowercase_					:			str=7_6_8,lowercase_					:			List[str]=1_2,lowercase_					:			int=1_2,lowercase_					:			Optional[int]=3_0_7_2,lowercase_					:			List[str]="gelu",lowercase_					:			Dict=0.1,lowercase_					:			Optional[int]=0.1,lowercase_					:			Any=5_1_2,lowercase_					:			List[str]=2,lowercase_					:			Any=0.02,lowercase_					:			List[str]=1E-12,lowercase_					:			Tuple=True,lowercase_					:			Dict=1,lowercase_					:			str=0,lowercase_					:			List[str]=2,**lowercase_					:			Tuple,)->       List[Any]:





        '''simple docstring'''







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

        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__        =  layer_norm_eps
        A__        =  use_cache




import math
import os

import cva
import numpy as np

lowercase_									=    255.0
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      float       ,     SCREAMING_SNAKE_CASE__     :      float )				-> float:





    '''simple docstring'''






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

    return 20 * math.logaa(PIXEL_MAX / math.sqrt(SCREAMING_SNAKE_CASE__ ) )
def 		_snake_case(       )				-> None:





    '''simple docstring'''






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

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

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


if __name__ == "__main__":
     main()

