

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 					A		(				_UpperCAmelCase       ,				unittest.TestCase					):







    """simple docstring"""





    lowerCamelCase						      =				MPNetTokenizer
    lowerCamelCase						      =				MPNetTokenizerFast
    lowerCamelCase						      =				True
    lowerCamelCase						      =				True


    def 		snake_case__	( self					:			List[str]						)->       Optional[int]:





        '''simple docstring'''







        super().setUp()

        A__        =  [
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        A__        =  os.path.join(self.tmpdirname,VOCAB_FILES_NAMES['vocab_file']						)
        with open(self.vocab_file,'w',encoding='utf-8'						) as vocab_writer:
            vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]						)						)


    def 		snake_case__	( self					:			List[Any],lowercase_					:			str						)->       Optional[int]:





        '''simple docstring'''







        A__        =  'UNwant\u00E9d,running'
        A__        =  'unwanted, running'
        return input_text, output_text


    def 		snake_case__	( self					:			int						)->       Tuple:





        '''simple docstring'''







        A__        =  self.tokenizer_class(self.vocab_file						)

        A__        =  tokenizer.tokenize('UNwant\u00E9d,running'						)
        self.assertListEqual(lowercase_,['un', '##want', '##ed', ',', 'runn', '##ing']						)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_						),[9, 6, 7, 1_2, 1_0, 1_1]						)


    @slow
    def 		snake_case__	( self					:			Dict						)->       str:





        '''simple docstring'''







        A__        =  self.tokenizer_class.from_pretrained('microsoft/mpnet-base'						)

        A__        =  tokenizer.encode('sequence builders',add_special_tokens=lowercase_						)
        A__        =  tokenizer.encode('multi-sequence build',add_special_tokens=lowercase_						)

        A__        =  tokenizer.build_inputs_with_special_tokens(lowercase_						)
        A__        =  tokenizer.build_inputs_with_special_tokens(lowercase_,lowercase_						)

        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]




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()

