


"""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  lowercase_  :







           @staticmethod
           def 				_lowerCAmelCase       (      *_lowercase :							Dict		,  **_lowercase :							Union[str, Any]    ):
             pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class  lowercase_  ( unittest.TestCase						):
      __magic_name__     :							Optional[int]          =				MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      __magic_name__     :							str          =				TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







      def 				_lowerCAmelCase       (      self :							List[str]		,  _lowercase :							List[str]		,  _lowercase :							List[Any]		,  _lowercase :							Optional[Any]    ):
        lowerCAmelCase__    :    List[str]								=					ImageClassificationPipeline(model=SCREAMING_SNAKE_CASE_		,  image_processor=SCREAMING_SNAKE_CASE_		,  top_k=2    )
        lowerCAmelCase__    :    str								=					[
            Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
            "http://images.cocodataset.org/val2017/000000039769.jpg",
        ]
        return image_classifier, examples







      def 				_lowerCAmelCase       (      self :							List[str]		,  _lowercase :							int		,  _lowercase :							Any    ):
        lowerCAmelCase__    :    Union[str, Any]								=					image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"    )

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

        import datasets

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

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







      @require_torch
      def 				_lowerCAmelCase       (      self :							List[Any]    ):
        lowerCAmelCase__    :    Union[str, Any]								=					"hf-internal-testing/tiny-random-vit"
        lowerCAmelCase__    :    List[Any]								=					pipeline("image-classification"		,  model=SCREAMING_SNAKE_CASE_    )

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

        lowerCAmelCase__    :    Tuple								=					image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ]		,  top_k=2		,  )
        self.assertEqual(
            nested_simplify(SCREAMING_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 				_lowerCAmelCase       (      self :							List[str]    ):
        lowerCAmelCase__    :    List[Any]								=					"hf-internal-testing/tiny-random-vit"
        lowerCAmelCase__    :    str								=					pipeline("image-classification"		,  model=SCREAMING_SNAKE_CASE_		,  framework="tf"    )

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

        lowerCAmelCase__    :    Any								=					image_classifier(
            [
                "http://images.cocodataset.org/val2017/000000039769.jpg",
                "http://images.cocodataset.org/val2017/000000039769.jpg",
            ]		,  top_k=2		,  )
        self.assertEqual(
            nested_simplify(SCREAMING_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 				_lowerCAmelCase       (      self :							List[str]    ):
        lowerCAmelCase__    :    Any								=					PreTrainedTokenizer()

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

        self.assertIs(image_classifier.tokenizer		,  SCREAMING_SNAKE_CASE_    )







      @slow
      @require_torch
      def 				_lowerCAmelCase       (      self :							List[str]    ):
        # 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
        lowerCAmelCase__    :    List[str]								=					pipeline("image-classification"		,  model="deepmind/vision-perceiver-conv"    )
        lowerCAmelCase__    :    Any								=					image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"    )
        self.assertEqual(
            nested_simplify(SCREAMING_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"},
            ]		,  )

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

        lowerCAmelCase__    :    Optional[int]								=					pipeline("image-classification"		,  model="deepmind/vision-perceiver-learned"    )
        lowerCAmelCase__    :    Dict								=					image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"    )
        self.assertEqual(
            nested_simplify(SCREAMING_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"},
            ]		,  )




"""simple docstring"""





from __future__ import annotations





def     lowercase__	(  lowerCamelCase     :  list				,	lowerCamelCase     :  list				)					->     list:
  lowerCAmelCase__    :    Dict								=					[None] * (len(lowerCamelCase				) + len(lowerCamelCase				))

  lowerCAmelCase__    :    Optional[Any]								=					0  # pointer to current index for left Half
  lowerCAmelCase__    :    Any								=					0  # pointer to current index for the right Half
  lowerCAmelCase__    :    Dict								=					0  # pointer to current index for the sorted array Half

  while pointera < len(lowerCamelCase				) and pointera < len(lowerCamelCase				):
    if left_half[pointera] < right_half[pointera]:
      lowerCAmelCase__    :    Tuple								=					left_half[pointera]
      pointera += 1
      index += 1
    else:
      lowerCAmelCase__    :    int								=					right_half[pointera]
      pointera += 1
      index += 1
  while pointera < len(lowerCamelCase				):
    lowerCAmelCase__    :    Tuple								=					left_half[pointera]
    pointera += 1
    index += 1

  while pointera < len(lowerCamelCase				):
    lowerCAmelCase__    :    Optional[int]								=					right_half[pointera]
    pointera += 1
    index += 1

  return sorted_array





def     lowercase__	(  lowerCamelCase     :  list				)					->     list:
  if len(lowerCamelCase				) <= 1:
    return array
  # the actual formula to calculate the middle element = left + (right - left) // 2
  # this avoids integer overflow in case of large N
  lowerCAmelCase__    :    Optional[int]								=					0 + (len(lowerCamelCase				) - 0) // 2

  # Split the array into halves till the array length becomes equal to One
  # merge the arrays of single length returned by mergeSort function and
  # pass them into the merge arrays function which merges the array
  lowerCAmelCase__    :    Union[str, Any]								=					array[:middle]
  lowerCAmelCase__    :    List[str]								=					array[middle:]

  return merge(merge_sort(lowerCamelCase				)				,	merge_sort(lowerCamelCase				)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
