



"""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 	a__		:


        @staticmethod
        def 							lowercase					(				*lowerCAmelCase   :				Union[str, Any],  **lowerCAmelCase   :				List[str]      )     ->							Any:
              pass


@is_pipeline_test
@require_torch_or_tf
@require_vision
class 	a__		(    unittest.TestCase  ):
   _lowerCamelCase       				=					MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   _lowerCamelCase       				=					TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


   def 							lowercase					(				self   :				Union[str, Any],  lowerCAmelCase   :				str,  lowerCAmelCase   :				Union[str, Any],  lowerCAmelCase   :				Optional[int]      )     ->							Dict:
         lowercase	:   str											= ImageClassificationPipeline(model=lowerCAmelCase,  image_processor=lowerCAmelCase,  top_k=2      )
         lowercase	:   Union[str, Any]											= [
             Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      ),
             'http://images.cocodataset.org/val2017/000000039769.jpg',
         ]
         return image_classifier, examples


   def 							lowercase					(				self   :				Any,  lowerCAmelCase   :				Optional[int],  lowerCAmelCase   :				Tuple      )     ->							Union[str, Any]:
         lowercase	:   Optional[Any]											= image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'      )

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

         import datasets

         lowercase	:   List[Any]											= datasets.load_dataset('hf-internal-testing/fixtures_image_utils',  'image',  split='test'      )

         # Accepts URL + PIL.Image + lists
         lowercase	:   Union[str, Any]											= 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(
             lowerCAmelCase,  [
                 [
                     {'score': ANY(lowerCAmelCase      ), 'label': ANY(lowerCAmelCase      )},
                     {'score': ANY(lowerCAmelCase      ), 'label': ANY(lowerCAmelCase      )},
                 ],
                 [
                     {'score': ANY(lowerCAmelCase      ), 'label': ANY(lowerCAmelCase      )},
                     {'score': ANY(lowerCAmelCase      ), 'label': ANY(lowerCAmelCase      )},
                 ],
                 [
                     {'score': ANY(lowerCAmelCase      ), 'label': ANY(lowerCAmelCase      )},
                     {'score': ANY(lowerCAmelCase      ), 'label': ANY(lowerCAmelCase      )},
                 ],
                 [
                     {'score': ANY(lowerCAmelCase      ), 'label': ANY(lowerCAmelCase      )},
                     {'score': ANY(lowerCAmelCase      ), 'label': ANY(lowerCAmelCase      )},
                 ],
                 [
                     {'score': ANY(lowerCAmelCase      ), 'label': ANY(lowerCAmelCase      )},
                     {'score': ANY(lowerCAmelCase      ), 'label': ANY(lowerCAmelCase      )},
                 ],
             ],  )


   @require_torch
   def 							lowercase					(				self   :				Any      )     ->							str:
         lowercase	:   Dict											= 'hf-internal-testing/tiny-random-vit'
         lowercase	:   Tuple											= pipeline('image-classification',  model=lowerCAmelCase      )

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

         lowercase	:   Tuple											= image_classifier(
             [
                 'http://images.cocodataset.org/val2017/000000039769.jpg',
                 'http://images.cocodataset.org/val2017/000000039769.jpg',
             ],  top_k=2,  )
         self.assertEqual(
             nested_simplify(lowerCAmelCase,  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 							lowercase					(				self   :				List[Any]      )     ->							Union[str, Any]:
         lowercase	:   Optional[int]											= 'hf-internal-testing/tiny-random-vit'
         lowercase	:   Optional[Any]											= pipeline('image-classification',  model=lowerCAmelCase,  framework='tf'      )

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

         lowercase	:   Optional[Any]											= image_classifier(
             [
                 'http://images.cocodataset.org/val2017/000000039769.jpg',
                 'http://images.cocodataset.org/val2017/000000039769.jpg',
             ],  top_k=2,  )
         self.assertEqual(
             nested_simplify(lowerCAmelCase,  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 							lowercase					(				self   :				Dict      )     ->							Dict:
         lowercase	:   Union[str, Any]											= PreTrainedTokenizer()

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

         self.assertIs(image_classifier.tokenizer,  lowerCAmelCase      )






   @slow
   @require_torch
   def 							lowercase					(				self   :				Optional[Any]      )     ->							Tuple:
         # 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
         lowercase	:   Any											= pipeline('image-classification',  model='deepmind/vision-perceiver-conv'      )
         lowercase	:   Union[str, Any]											= image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'      )
         self.assertEqual(
             nested_simplify(lowerCAmelCase,  decimals=4      ),  [
                 {'score': 0.4385, 'label': 'tabby, tabby cat'},
                 {'score': 0.321, 'label': 'tiger cat'},
                 {'score': 0.0502, 'label': 'Egyptian cat'},
                 {'score': 0.0137, 'label': 'crib, cot'},
                 {'score': 0.007, 'label': 'radiator'},
             ],  )

         lowercase	:   str											= pipeline('image-classification',  model='deepmind/vision-perceiver-fourier'      )
         lowercase	:   Any											= image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'      )
         self.assertEqual(
             nested_simplify(lowerCAmelCase,  decimals=4      ),  [
                 {'score': 0.5658, 'label': 'tabby, tabby cat'},
                 {'score': 0.1309, 'label': 'tiger cat'},
                 {'score': 0.0722, 'label': 'Egyptian cat'},
                 {'score': 0.0707, 'label': 'remote control, remote'},
                 {'score': 0.0082, 'label': 'computer keyboard, keypad'},
             ],  )

         lowercase	:   Any											= pipeline('image-classification',  model='deepmind/vision-perceiver-learned'      )
         lowercase	:   Dict											= image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'      )
         self.assertEqual(
             nested_simplify(lowerCAmelCase,  decimals=4      ),  [
                 {'score': 0.3022, 'label': 'tabby, tabby cat'},
                 {'score': 0.2362, 'label': 'Egyptian cat'},
                 {'score': 0.1856, 'label': 'tiger cat'},
                 {'score': 0.0324, 'label': 'remote control, remote'},
                 {'score': 0.0096, 'label': 'quilt, comforter, comfort, puff'},
             ],  )





"""simple docstring"""




from __future__ import annotations

# Define the Gravitational Constant G and the function
_UpperCamelCase:					Tuple     				=	6.6_7_4_3e-1_1  # unit of G : m^3 * kg^-1 * s^-2


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					)  ->      dict[str, float]:






      '''simple docstring'''


      lowercase	:   str											= mass_a * mass_a

      if (force, mass_a, mass_a, distance).count(0					) != 1:
            raise ValueError('One and only one argument must be 0'					)
      if force < 0:
            raise ValueError('Gravitational force can not be negative'					)
      if distance < 0:
            raise ValueError('Distance can not be negative'					)
      if mass_a < 0 or mass_a < 0:
            raise ValueError('Mass can not be negative'					)
      if force == 0:
            lowercase	:   str											= GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
            return {"force": force}
      elif mass_a == 0:
            lowercase	:   Optional[int]											= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_1": mass_a}
      elif mass_a == 0:
            lowercase	:   Optional[int]											= (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
            return {"mass_2": mass_a}
      elif distance == 0:
            lowercase	:   List[Any]											= (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
            return {"distance": distance}
      raise ValueError('One and only one argument must be 0'					)


# Run doctest
if __name__ == "__main__":
     import doctest

     doctest.testmod()
