




import math
import os

import cva
import numpy as np

SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       2_55.0
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_       ):
    a__      =       np.mean((original - contrast) ** 2       )
    if mse == 0:
        return 100

    return 20 * math.logaa(PIXEL_MAX / math.sqrt(UpperCAmelCase_       )       )
def        __lowerCAmelCase ():
    a__      =       os.path.dirname(os.path.realpath(UpperCAmelCase_       )       )
    # Loading images (original image and compressed image)
    a__      =       cva.imread(os.path.join(UpperCAmelCase_   ,    """image_data/original_image.png"""       )       )
    a__      =       cva.imread(os.path.join(UpperCAmelCase_   ,    """image_data/compressed_image.png"""       )   ,    1       )

    a__      =       cva.imread(os.path.join(UpperCAmelCase_   ,    """image_data/PSNR-example-base.png"""       )       )
    a__      =       cva.imread(
        os.path.join(UpperCAmelCase_   ,    """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(UpperCAmelCase_   ,    UpperCAmelCase_       )} dB"""       )

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


if __name__ == "__main__":
 main()












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 		__UpperCAmelCase:





      """simple docstring"""



      @staticmethod
      def 				__SCREAMING_SNAKE_CASE		(      *A :       str						,				**A :       int    )			->			Union[str, Any]:






          """simple docstring"""
          pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: Tuple						=    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     __UpperCAmelCase	: Dict						=    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

     def 				__SCREAMING_SNAKE_CASE		(      self :       str						,				A :       Optional[int]						,				A :       Tuple						,				A :       int    )			->			str:






         """simple docstring"""
         a__      =       ImageClassificationPipeline(model=A						,				image_processor=A						,				top_k=2    )
         a__      =       [
             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 :       Any						,				A :       List[str]						,				A :       Dict    )			->			Union[str, Any]:






         """simple docstring"""
         a__      =       image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )

         self.assertEqual(
             A						,				[
                 {"""score""": ANY(A    ), """label""": ANY(A    )},
                 {"""score""": ANY(A    ), """label""": ANY(A    )},
             ]						,				)

         import datasets

         a__      =       datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""						,				"""image"""						,				split="""test"""    )

         # Accepts URL + PIL.Image + lists
         a__      =       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(
             A						,				[
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
                 [
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                     {"""score""": ANY(A    ), """label""": ANY(A    )},
                 ],
             ]						,				)

     @require_torch
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       """hf-internal-testing/tiny-random-vit"""
         a__      =       pipeline("""image-classification"""						,				model=A    )

         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]						,				)

         a__      =       image_classifier(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]						,				top_k=2						,				)
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
             ]						,				)

     @require_tf
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			str:






         """simple docstring"""
         a__      =       """hf-internal-testing/tiny-random-vit"""
         a__      =       pipeline("""image-classification"""						,				model=A						,				framework="""tf"""    )

         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]						,				)

         a__      =       image_classifier(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]						,				top_k=2						,				)
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
                 [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
             ]						,				)

     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			str:






         """simple docstring"""
         a__      =       PreTrainedTokenizer()

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

         self.assertIs(image_classifier.tokenizer						,				A    )






     @slow
     @require_torch
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Dict:






         """simple docstring"""
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         a__      =       pipeline("""image-classification"""						,				model="""deepmind/vision-perceiver-conv"""    )
         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 {"""score""": 0.4_3_8_5, """label""": """tabby, tabby cat"""},
                 {"""score""": 0.3_2_1, """label""": """tiger cat"""},
                 {"""score""": 0.0_5_0_2, """label""": """Egyptian cat"""},
                 {"""score""": 0.0_1_3_7, """label""": """crib, cot"""},
                 {"""score""": 0.0_0_7, """label""": """radiator"""},
             ]						,				)

         a__      =       pipeline("""image-classification"""						,				model="""deepmind/vision-perceiver-fourier"""    )
         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 {"""score""": 0.5_6_5_8, """label""": """tabby, tabby cat"""},
                 {"""score""": 0.1_3_0_9, """label""": """tiger cat"""},
                 {"""score""": 0.0_7_2_2, """label""": """Egyptian cat"""},
                 {"""score""": 0.0_7_0_7, """label""": """remote control, remote"""},
                 {"""score""": 0.0_0_8_2, """label""": """computer keyboard, keypad"""},
             ]						,				)

         a__      =       pipeline("""image-classification"""						,				model="""deepmind/vision-perceiver-learned"""    )
         a__      =       image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""    )
         self.assertEqual(
             nested_simplify(A						,				decimals=4    )						,				[
                 {"""score""": 0.3_0_2_2, """label""": """tabby, tabby cat"""},
                 {"""score""": 0.2_3_6_2, """label""": """Egyptian cat"""},
                 {"""score""": 0.1_8_5_6, """label""": """tiger cat"""},
                 {"""score""": 0.0_3_2_4, """label""": """remote control, remote"""},
                 {"""score""": 0.0_0_9_6, """label""": """quilt, comforter, comfort, puff"""},
             ]						,				)






