






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 					_lowerCAmelCase				(		*_SCREAMING_SNAKE_CASE						, **_SCREAMING_SNAKE_CASE )       -> Any:
															pass




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


						'''simple docstring'''




						A   :      Dict						 =    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
						A   :      Optional[Any]						 =    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )       -> Union[str, Any]:
								snake_case_ :							List[str]         =				ImageClassificationPipeline(model=snake_case_						, image_processor=snake_case_						, top_k=2 )
								snake_case_ :							str         =				[
								    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
								    'http://images.cocodataset.org/val2017/000000039769.jpg',
								]
								return image_classifier, examples






						def 					_lowerCAmelCase				(		self						, _SCREAMING_SNAKE_CASE						, _SCREAMING_SNAKE_CASE )       -> Optional[int]:
								snake_case_ :							Any         =				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

								snake_case_ :							Optional[int]         =				datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						, "image"						, split="test" )

								# Accepts URL + PIL.Image + lists
								snake_case_ :							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(
								    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 					_lowerCAmelCase				(		self )       -> List[str]:
								snake_case_ :							Optional[Any]         =				'hf-internal-testing/tiny-random-vit'
								snake_case_ :							Optional[int]         =				pipeline("image-classification"						, model=snake_case_ )

								snake_case_ :							Dict         =				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}]						, )

								snake_case_ :							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}],
								    ]						, )






						@require_tf
						def 					_lowerCAmelCase				(		self )       -> List[Any]:
								snake_case_ :							Any         =				'hf-internal-testing/tiny-random-vit'
								snake_case_ :							Optional[Any]         =				pipeline("image-classification"						, model=snake_case_						, framework="tf" )

								snake_case_ :							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}]						, )

								snake_case_ :							Tuple         =				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 					_lowerCAmelCase				(		self )       -> Dict:
								snake_case_ :							Optional[int]         =				PreTrainedTokenizer()

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

								self.assertIs(image_classifier.tokenizer						, snake_case_ )






						@slow
						@require_torch
						def 					_lowerCAmelCase				(		self )       -> Tuple:
								snake_case_ :							Optional[Any]         =				pipeline("image-classification"						, model="deepmind/vision-perceiver-conv" )
								snake_case_ :							List[Any]         =				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"},
								    ]						, )

								snake_case_ :							Tuple         =				pipeline("image-classification"						, model="deepmind/vision-perceiver-fourier" )
								snake_case_ :							Union[str, 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"},
								    ]						, )

								snake_case_ :							Optional[Any]         =				pipeline("image-classification"						, model="deepmind/vision-perceiver-learned" )
								snake_case_ :							Optional[int]         =				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"},
								    ]						, )

'''simple docstring'''


def 					_lowerCamelCase    (      lowerCamelCase_					:							str   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      str(lowerCamelCase_   ).strip()
     if not oct_string:
          raise ValueError('Empty string was passed to the function'   )
     UpperCAmelCase_							:   Tuple   =      oct_string[0] == '-'
     if is_negative:
          UpperCAmelCase_							:   Tuple   =      oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCamelCase_   ) <= 7 for char in oct_string   ):
          raise ValueError('Non-octal value was passed to the function'   )
     UpperCAmelCase_							:   str   =      0
     for char in oct_string:
          UpperCAmelCase_							:   List[Any]   =      8 * decimal_number + int(lowerCamelCase_   )
     if is_negative:
          UpperCAmelCase_							:   List[Any]   =      -decimal_number
     return decimal_number


if __name__ == "__main__":
 from doctest import testmod

 testmod()






