






"""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 	_UpperCamelCase :



     '''simple docstring'''


     @staticmethod
     def 		A__      (      *__lowercase   ,			**__lowercase			):
      pass


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



    '''simple docstring'''


    __lowercase    :   Tuple											=						MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    __lowercase    :   Optional[int]											=						TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


    def 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				ImageClassificationPipeline(model=__lowercase   ,			image_processor=__lowercase   ,			top_k=2			)
     UpperCAmelCase__ 						=				[
         Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			),
         """http://images.cocodataset.org/val2017/000000039769.jpg""",
     ]
     return image_classifier, examples


    def 		A__      (      self   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)

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

     import datasets

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

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


    @require_torch
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"""hf-internal-testing/tiny-random-vit"""
     UpperCAmelCase__ 						=				pipeline("""image-classification"""   ,			model=__lowercase			)

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

     UpperCAmelCase__ 						=				image_classifier(
         [
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]   ,			top_k=2   ,			)
     self.assertEqual(
         nested_simplify(__lowercase   ,			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 		A__      (      self			):
     UpperCAmelCase__ 						=				"""hf-internal-testing/tiny-random-vit"""
     UpperCAmelCase__ 						=				pipeline("""image-classification"""   ,			model=__lowercase   ,			framework="""tf"""			)

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

     UpperCAmelCase__ 						=				image_classifier(
         [
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]   ,			top_k=2   ,			)
     self.assertEqual(
         nested_simplify(__lowercase   ,			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 		A__      (      self			):
     UpperCAmelCase__ 						=				PreTrainedTokenizer()

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

     self.assertIs(image_classifier.tokenizer   ,			__lowercase			)






    @slow
    @require_torch
    def 		A__      (      self			):
     # 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
     UpperCAmelCase__ 						=				pipeline("""image-classification"""   ,			model="""deepmind/vision-perceiver-conv"""			)
     UpperCAmelCase__ 						=				image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
     self.assertEqual(
         nested_simplify(__lowercase   ,			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"""},
         ]   ,			)

     UpperCAmelCase__ 						=				pipeline("""image-classification"""   ,			model="""deepmind/vision-perceiver-fourier"""			)
     UpperCAmelCase__ 						=				image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
     self.assertEqual(
         nested_simplify(__lowercase   ,			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"""},
         ]   ,			)

     UpperCAmelCase__ 						=				pipeline("""image-classification"""   ,			model="""deepmind/vision-perceiver-learned"""			)
     UpperCAmelCase__ 						=				image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
     self.assertEqual(
         nested_simplify(__lowercase   ,			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"""},
         ]   ,			)











"""simple docstring"""



from math import exp




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 return 8 * x - 2 * exp(-x			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 UpperCAmelCase__ 						=				lower_bound
 UpperCAmelCase__ 						=				upper_bound
 for _ in range(0       ,	_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__						,	UpperCAmelCase__ 						=				xa, xa - (f(_SCREAMING_SNAKE_CASE			) * (xa - xa)) / (f(_SCREAMING_SNAKE_CASE			) - f(_SCREAMING_SNAKE_CASE			))
 return xa


if __name__ == "__main__":
 print(F'''Example: {secant_method(1, 3, 2)}''')



