





import math
import os

import cva
import numpy as np

_SCREAMING_SNAKE_CASE   						=		2_5_5.0


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_		:      Optional[Any]           =						np.mean((original - contrast) ** 2 )
	if mse == 0:
		return 1_00

	return 20 * math.logaa(PIXEL_MAX / math.sqrt(__a ) )


def 				SCREAMING_SNAKE_CASE__    (     ):
	snake_case_		:      Any           =						os.path.dirname(os.path.realpath(__a ) )
	# Loading images (original image and compressed image)
	snake_case_		:      int           =						cva.imread(os.path.join(__a ,			'image_data/original_image.png' ) )
	snake_case_		:      Tuple           =						cva.imread(os.path.join(__a ,			'image_data/compressed_image.png' ) ,			1 )

	snake_case_		:      Dict           =						cva.imread(os.path.join(__a ,			'image_data/PSNR-example-base.png' ) )
	snake_case_		:      Optional[Any]           =						cva.imread(
	    os.path.join(__a ,			'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(__a ,			__a )} dB""" )

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


if __name__ == "__main__":
						main()



"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case					  =						{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						[
	    'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'QDQBertForMaskedLM',
	    'QDQBertForMultipleChoice',
	    'QDQBertForNextSentencePrediction',
	    'QDQBertForQuestionAnswering',
	    'QDQBertForSequenceClassification',
	    'QDQBertForTokenClassification',
	    'QDQBertLayer',
	    'QDQBertLMHeadModel',
	    'QDQBertModel',
	    'QDQBertPreTrainedModel',
	    'load_tf_weights_in_qdqbert',
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

	snake_case					  =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
