





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 					__magic_name__  :





      """simple docstring"""



      @staticmethod
      def 						SCREAMING_SNAKE_CASE		(							*snake_case       :Union[str, Any]			,      **snake_case       :Union[str, Any]  ):





         '''simple docstring'''




         pass


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





     """simple docstring"""



     __UpperCamelCase					    =     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     __UpperCamelCase					    =     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



     def 						SCREAMING_SNAKE_CASE		(							self       :Any			,      snake_case       :Optional[int]			,      snake_case       :str			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        A_					:				List[str]          =			ImageClassificationPipeline(model=snake_case			,      image_processor=snake_case			,      top_k=2  )
        A_					:				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       :Union[str, Any]			,      snake_case       :List[str]			,      snake_case       :Tuple  ):





        '''simple docstring'''




        A_					:				int          =			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

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

        # Accepts URL + PIL.Image + lists
        A_					:				Tuple          =			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[Any]  ):





        '''simple docstring'''




        A_					:				Dict          =			"hf-internal-testing/tiny-random-vit"
        A_					:				int          =			pipeline("image-classification"			,      model=snake_case  )

        A_					:				List[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}]			,      )

        A_					:				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       :Optional[int]  ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			"hf-internal-testing/tiny-random-vit"
        A_					:				Tuple          =			pipeline("image-classification"			,      model=snake_case			,      framework="tf"  )

        A_					:				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}]			,      )

        A_					:				Optional[int]          =			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       :Any  ):





        '''simple docstring'''




        A_					:				List[str]          =			PreTrainedTokenizer()

        # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
        A_					:				Tuple          =			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       :str  ):





        '''simple docstring'''




        A_					:				List[Any]          =			pipeline("image-classification"			,      model="deepmind/vision-perceiver-conv"  )
        A_					:				List[str]          =			image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"  )
        self.assertEqual(
            nested_simplify(snake_case			,      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"},
            ]			,      )

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

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







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[Any]          =			(inputs > threshold).type(inputs.type()  )
        if mask.sum() < nb_min:
           # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				List[str]          =			mask.flatten()
        A_					:				List[str]          =			0
        A_					:				Union[str, Any]          =			1
        return mask
