

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


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				'pegasus_x'
    lowerCamelCase						      =				['past_key_values']
    lowerCamelCase						      =				{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


    def __init__( self					:			Optional[int],lowercase_					:			List[Any]=9_6_1_0_3,lowercase_					:			Any=1_6_3_8_4,lowercase_					:			List[str]=1_6,lowercase_					:			int=4_0_9_6,lowercase_					:			Union[str, Any]=1_6,lowercase_					:			int=1_6,lowercase_					:			int=4_0_9_6,lowercase_					:			List[str]=1_6,lowercase_					:			Tuple=0.0,lowercase_					:			str=0.0,lowercase_					:			Optional[Any]=True,lowercase_					:			Dict=True,lowercase_					:			Any="gelu",lowercase_					:			Optional[Any]=1_0_2_4,lowercase_					:			Dict=0.1,lowercase_					:			List[Any]=0.0,lowercase_					:			List[str]=0.0,lowercase_					:			List[str]=0.02,lowercase_					:			Tuple=0,lowercase_					:			Optional[Any]=True,lowercase_					:			List[Any]=0,lowercase_					:			Union[str, Any]=1,lowercase_					:			Dict=1,lowercase_					:			Any=3_2,lowercase_					:			List[str]=5_1_2,lowercase_					:			List[Any]=True,**lowercase_					:			Dict,)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  vocab_size
        A__        =  max_position_embeddings
        A__        =  d_model
        A__        =  encoder_ffn_dim
        A__        =  encoder_layers
        A__        =  encoder_attention_heads
        A__        =  decoder_ffn_dim
        A__        =  decoder_layers
        A__        =  decoder_attention_heads
        A__        =  dropout
        A__        =  attention_dropout
        A__        =  activation_dropout
        A__        =  activation_function
        A__        =  init_std
        A__        =  encoder_layerdrop
        A__        =  decoder_layerdrop
        A__        =  use_cache
        A__        =  encoder_layers
        A__        =  scale_embedding  # scale factor will be sqrt(d_model) if True

        A__        =  num_global_tokens
        A__        =  block_size
        A__        =  stagger_local_blocks

        super().__init__(
            pad_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,decoder_start_token_id=lowercase_,forced_eos_token_id=lowercase_,**lowercase_,)


    @property
    def 		snake_case__	( self					:			Optional[int]						)->       int:





        '''simple docstring'''







        return self.encoder_attention_heads


    @property
    def 		snake_case__	( self					:			str						)->       int:





        '''simple docstring'''







        return self.d_model




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()

