

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase_									=    logging.get_logger(__name__)

lowercase_									=    {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				'longt5'
    lowerCamelCase						      =				['past_key_values']
    lowerCamelCase						      =				{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


    def __init__( self					:			str,lowercase_					:			Tuple=3_2_1_2_8,lowercase_					:			List[str]=5_1_2,lowercase_					:			Any=6_4,lowercase_					:			Optional[int]=2_0_4_8,lowercase_					:			Tuple=6,lowercase_					:			List[Any]=None,lowercase_					:			str=8,lowercase_					:			List[str]=1_2_7,lowercase_					:			List[Any]=1_6,lowercase_					:			Union[str, Any]=3_2,lowercase_					:			List[str]=1_2_8,lowercase_					:			Optional[Any]=0.1,lowercase_					:			Any=1E-6,lowercase_					:			Any=1.0,lowercase_					:			int="relu",lowercase_					:			Any=True,lowercase_					:			Optional[int]="local",lowercase_					:			List[Any]=True,lowercase_					:			Dict=0,lowercase_					:			List[Any]=1,**lowercase_					:			Dict,)->       Optional[Any]:





        '''simple docstring'''







        A__        =  vocab_size
        A__        =  d_model
        A__        =  d_kv
        A__        =  d_ff
        A__        =  num_layers
        # default = symmetry
        A__        =  num_decoder_layers if num_decoder_layers is not None else self.num_layers
        A__        =  num_heads
        A__        =  local_radius
        A__        =  global_block_size
        A__        =  relative_attention_num_buckets
        A__        =  relative_attention_max_distance
        A__        =  dropout_rate
        A__        =  layer_norm_epsilon
        A__        =  initializer_factor
        A__        =  feed_forward_proj
        A__        =  encoder_attention_type
        A__        =  use_cache

        A__        =  self.feed_forward_proj.split('-'						)
        A__        =  act_info[-1]
        A__        =  act_info[0] == 'gated'

        if len(lowercase_						) > 1 and act_info[0] != "gated" or len(lowercase_						) > 2:
            raise ValueError(
                F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                '\'gated-gelu\' or \'relu\''						)

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            A__        =  'gelu_new'

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




class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    @property
    def 		snake_case__	( self					:			str						)->       Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        A__        =  {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
            A__        =  'past_encoder_sequence + sequence'
            A__        =  {0: 'batch'}
            A__        =  {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
            A__        =  {0: 'batch', 1: 'decoder_sequence'}
            A__        =  {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
            self.fill_with_past_key_values_(lowercase_,direction='inputs'						)

        return common_inputs


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





        '''simple docstring'''







        return 1_3




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()

