





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'''






import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:







		class       lowerCamelCase       :


							@staticmethod
							def  SCREAMING_SNAKE_CASE_		(       *__snake_case						:	Optional[Any]							,  **__snake_case						:	str							):





													'''simple docstring'''





													pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class       lowerCamelCase       (    unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					_SCREAMING_SNAKE_CASE				 =     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		ImageClassificationPipeline(model=__snake_case							,  image_processor=__snake_case							,  top_k=2							)
											_snake_case: Tuple	       =		[
											    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											    'http://images.cocodataset.org/val2017/000000039769.jpg',
											]
											return image_classifier, examples


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Any							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: Dict	       =		image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'							)

											self.assertEqual(
											    __snake_case							,  [
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											    ]							,  )

											import datasets

											_snake_case: str	       =		datasets.load_dataset('hf-internal-testing/fixtures_image_utils'							,  'image'							,  split='test'							)

											# Accepts URL + PIL.Image + lists
											_snake_case: List[str]	       =		image_classifier(
											    [
											        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        # RGBA
											        dataset[0]['file'],
											        # LA
											        dataset[1]['file'],
											        # L
											        dataset[2]['file'],
											    ]							)
											self.assertEqual(
											    __snake_case							,  [
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											    ]							,  )


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Any	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							)

											_snake_case: List[str]	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: Dict	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					@require_tf
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							,  framework='tf'							)

											_snake_case: Any	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: str	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		PreTrainedTokenizer()

											# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
											_snake_case: Any	       =		pipeline(
											    'image-classification'							,  model='hf-internal-testing/tiny-random-vit'							,  tokenizer=__snake_case							)

											self.assertIs(image_classifier.tokenizer							,  __snake_case							)




					@slow
					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Any	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-conv'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.4_385, 'label': 'tabby, tabby cat'},
											        {'score': 0.321, 'label': 'tiger cat'},
											        {'score': 0.0_502, 'label': 'Egyptian cat'},
											        {'score': 0.0_137, 'label': 'crib, cot'},
											        {'score': 0.007, 'label': 'radiator'},
											    ]							,  )

											_snake_case: Union[str, Any]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-fourier'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.5_658, 'label': 'tabby, tabby cat'},
											        {'score': 0.1_309, 'label': 'tiger cat'},
											        {'score': 0.0_722, 'label': 'Egyptian cat'},
											        {'score': 0.0_707, 'label': 'remote control, remote'},
											        {'score': 0.0_082, 'label': 'computer keyboard, keypad'},
											    ]							,  )

											_snake_case: Optional[int]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-learned'							)
											_snake_case: str	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.3_022, 'label': 'tabby, tabby cat'},
											        {'score': 0.2_362, 'label': 'Egyptian cat'},
											        {'score': 0.1_856, 'label': 'tiger cat'},
											        {'score': 0.0_324, 'label': 'remote control, remote'},
											        {'score': 0.0_096, 'label': 'quilt, comforter, comfort, puff'},
											    ]							,  )





