






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 :







       '''simple docstring'''




       @staticmethod
       def 						lowerCAmelCase_       (			*UpperCamelCase_    :     Dict	,		**UpperCamelCase_    :     Dict			):


           """simple docstring"""



           pass


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







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     SCREAMING_SNAKE_CASE				  =			TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




     def 						lowerCAmelCase_       (			self    :     int	,		UpperCamelCase_    :     Union[str, Any]	,		UpperCamelCase_    :     Union[str, Any]	,		UpperCamelCase_    :     List[str]			):


         """simple docstring"""



         __A             =		ImageClassificationPipeline(model=UpperCamelCase_	,		image_processor=UpperCamelCase_	,		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 						lowerCAmelCase_       (			self    :     Any	,		UpperCamelCase_    :     Any	,		UpperCamelCase_    :     Tuple			):


         """simple docstring"""



         __A             =		image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)

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

         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(
             UpperCamelCase_	,		[
                 [
                     {"""score""": ANY(UpperCamelCase_			), """label""": ANY(UpperCamelCase_			)},
                     {"""score""": ANY(UpperCamelCase_			), """label""": ANY(UpperCamelCase_			)},
                 ],
                 [
                     {"""score""": ANY(UpperCamelCase_			), """label""": ANY(UpperCamelCase_			)},
                     {"""score""": ANY(UpperCamelCase_			), """label""": ANY(UpperCamelCase_			)},
                 ],
                 [
                     {"""score""": ANY(UpperCamelCase_			), """label""": ANY(UpperCamelCase_			)},
                     {"""score""": ANY(UpperCamelCase_			), """label""": ANY(UpperCamelCase_			)},
                 ],
                 [
                     {"""score""": ANY(UpperCamelCase_			), """label""": ANY(UpperCamelCase_			)},
                     {"""score""": ANY(UpperCamelCase_			), """label""": ANY(UpperCamelCase_			)},
                 ],
                 [
                     {"""score""": ANY(UpperCamelCase_			), """label""": ANY(UpperCamelCase_			)},
                     {"""score""": ANY(UpperCamelCase_			), """label""": ANY(UpperCamelCase_			)},
                 ],
             ]	,		)




     @require_torch
     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         __A             =		"""hf-internal-testing/tiny-random-vit"""
         __A             =		pipeline("""image-classification"""	,		model=UpperCamelCase_			)

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

         __A             =		image_classifier(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]	,		top_k=2	,		)
         self.assertEqual(
             nested_simplify(UpperCamelCase_	,		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[Any]			):


         """simple docstring"""



         __A             =		"""hf-internal-testing/tiny-random-vit"""
         __A             =		pipeline("""image-classification"""	,		model=UpperCamelCase_	,		framework="""tf"""			)

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

         __A             =		image_classifier(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]	,		top_k=2	,		)
         self.assertEqual(
             nested_simplify(UpperCamelCase_	,		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    :     Optional[Any]			):


         """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=UpperCamelCase_			)

         self.assertIs(image_classifier.tokenizer	,		UpperCamelCase_			)





     @slow
     @require_torch
     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



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












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     list[int]      )  -> list[int]:





    """simple docstring"""

    if len(__lowercase      ) > 1:
        __A             =		len(__lowercase      ) // 2  # Finds the middle of the array
        __A             =		arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        __A             =		arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        __A             =		len(__lowercase      )
        __A             =		len(__lowercase      )
        merge(__lowercase      )  # Starts sorting the left.
        merge(__lowercase      )  # Starts sorting the right
        __A             =		0  # Left Counter
        __A             =		0  # Right Counter
        __A             =		0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                __A             =		left_array[left_index]
                left_index += 1
            else:
                __A             =		right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            __A             =		left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            __A             =		right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
  import doctest

  doctest.testmod()




