code: 
  
  """simple docstring"""
  
  
  
  
  from hashlib import mda, shaaaa
  
  __A  :					List[str]												=	(shaaaa, mda)
  
  
  
  
  
  class     __UpperCamelCase				:
  
  
  		def __init__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	int = 8):
  							A    =      0B0
  							A    =      size
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	str):
  							A    =      self.hash_(__SCREAMING_SNAKE_CASE)
  							self.bitarray |= h
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	str):
  							A    =      self.hash_(__SCREAMING_SNAKE_CASE)
  							return (h & self.bitarray) == h
  
  
  		def __contains__(self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	str):
  							return self.exists(__SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	int):
  							A    =      bin(__SCREAMING_SNAKE_CASE)[2:]
  							return res.zfill(self.size)
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
  							return self.format_bin(self.bitarray)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	str):
  							A    =      0B0
  							for func in HASH_FUNCTIONS:
  												A    =      (
  												    int.from_bytes(func(value.encode()).digest()  ,  "little") % self.size
  												)
  												res |= 2**position
  							return res
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	str):
  							return self.format_bin(self.hash_(__SCREAMING_SNAKE_CASE))
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							A    =      bin(self.bitarray).count("1")
  							return (n_ones / self.size) ** len(__SCREAMING_SNAKE_CASE)
  
  
  
  
  
  
style_context: 
  
  """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     __UpperCamelCase				:
  
  
  			@staticmethod
  			def       SCREAMING_SNAKE_CASE__     (*__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  **__SCREAMING_SNAKE_CASE				:	Any):
  								pass
  
  
  
  
  
  @is_pipeline_test
  @require_torch_or_tf
  @require_vision
  class     __UpperCamelCase				(     unittest.TestCase		):
  		SCREAMING_SNAKE_CASE              =					MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  		SCREAMING_SNAKE_CASE              =					TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							A    =      ImageClassificationPipeline(model=__SCREAMING_SNAKE_CASE  ,  image_processor=__SCREAMING_SNAKE_CASE  ,  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       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
  							A    =      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
  
  							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(
  							    __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       SCREAMING_SNAKE_CASE__     (self				:	str):
  							A    =      "hf-internal-testing/tiny-random-vit"
  							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE)
  
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]  ,  )
  
  							A    =      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": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							    ]  ,  )
  
  
  		@require_tf
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							A    =      "hf-internal-testing/tiny-random-vit"
  							A    =      pipeline("image-classification"  ,  model=__SCREAMING_SNAKE_CASE  ,  framework="tf")
  
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]  ,  )
  
  							A    =      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": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							        [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
  							    ]  ,  )
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							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=__SCREAMING_SNAKE_CASE)
  
  							self.assertIs(image_classifier.tokenizer  ,  __SCREAMING_SNAKE_CASE)
  
  
  		@slow
  		@require_torch
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							# 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
  							A    =      pipeline("image-classification"  ,  model="deepmind/vision-perceiver-conv")
  							A    =      image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
  							self.assertEqual(
  							    nested_simplify(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
  							        {"score": 0.3_2_1, "label": "tiger cat"},
  							        {"score": 0.0_5_0_2, "label": "Egyptian cat"},
  							        {"score": 0.0_1_3_7, "label": "crib, cot"},
  							        {"score": 0.0_0_7, "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(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
  							        {"score": 0.1_3_0_9, "label": "tiger cat"},
  							        {"score": 0.0_7_2_2, "label": "Egyptian cat"},
  							        {"score": 0.0_7_0_7, "label": "remote control, remote"},
  							        {"score": 0.0_0_8_2, "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(__SCREAMING_SNAKE_CASE  ,  decimals=4)  ,  [
  							        {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
  							        {"score": 0.2_3_6_2, "label": "Egyptian cat"},
  							        {"score": 0.1_8_5_6, "label": "tiger cat"},
  							        {"score": 0.0_3_2_4, "label": "remote control, remote"},
  							        {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
  							    ]  ,  )
  
  
  
  
  
  