






"""simple docstring"""



import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_reformer import ReformerTokenizer
else:
						__A		:	Union[str, Any]      =		None


__A		:	Dict      =		logging.get_logger(__name__)


__A		:	Optional[int]      =		'''▁'''

__A		:	Union[str, Any]      =		{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A		:	int      =		{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

__A		:	str      =		{
    '''google/reformer-crime-and-punishment''': 524_288,
}





class  _UpperCAmelCase	(			_A							):
					SCREAMING_SNAKE_CASE_		:    Any							   =       VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:    Dict							   =       PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:    List[str]							   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:    str							   =       ["input_ids", "attention_mask"]
					SCREAMING_SNAKE_CASE_		:    Optional[Any]							   =       ReformerTokenizer




					def __init__(    self					:						List[Any]   ,						A					:						Optional[Any]=None   ,						A					:						List[Any]=None   ,						A					:						str="</s>"   ,						A					:						Tuple="<unk>"   ,						A					:						Tuple=[]   ,						**A					:						int   ,						)  ->  List[Any]:
							super().__init__(
							    A   ,						tokenizer_file=A   ,						eos_token=A   ,						unk_token=A   ,						additional_special_tokens=A   ,						**A   ,						)

							lowercase_			:			int								=       vocab_file
							lowercase_			:			Dict								=       False if not self.vocab_file else True






					def   A  (    self					:						Optional[Any]   ,						A					:						str   ,						A					:						Optional[str] = None       )  ->  Tuple[str]:
							if not self.can_save_slow_tokenizer:
									raise ValueError(
									    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
									    '''tokenizer.'''       )

							if not os.path.isdir(A       ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
									return
							lowercase_			:			int								=       os.path.join(
							    A   ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(A       ):
									copyfile(self.vocab_file   ,						A       )

							return (out_vocab_file,)











"""simple docstring"""



import math
import os

import cva
import numpy as np

__A		:	int      =		255.0



def 						lowercase   (    __snake_case    :			float		, __snake_case    :			float     ):
		lowercase_			:			Any								=       np.mean((original - contrast) ** 2     )
		if mse == 0:
				return 1_0_0

		return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__snake_case     )     )



def 						lowercase   (    ):
		lowercase_			:			Any								=       os.path.dirname(os.path.realpath(__snake_case     )     )
		# Loading images (original image and compressed image)
		lowercase_			:			Union[str, Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/original_image.png'''     )     )
		lowercase_			:			List[Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/compressed_image.png'''     )		, 1     )

		lowercase_			:			Tuple								=       cva.imread(os.path.join(__snake_case		, '''image_data/PSNR-example-base.png'''     )     )
		lowercase_			:			List[str]								=       cva.imread(
		    os.path.join(__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(__snake_case		, __snake_case     )} dB'''     )

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


if __name__ == "__main__":
						main()



