code: 
  '''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  lowerCAmelCase__   :
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         @staticmethod
         def 		_lowerCamelCase	(*lowerCAmelCase_  : List[str]	, **lowerCAmelCase_  : Optional[int]    )   ->       Optional[Any]:
               pass
  
  
  
  @is_pipeline_test
  @require_torch_or_tf
  @require_vision
  class  lowerCAmelCase__   (       unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    _UpperCamelCase						= TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  
    def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Optional[int]	, lowerCAmelCase_  : Union[str, Any]    )   ->       List[Any]:
          snake_case_						       =							ImageClassificationPipeline(model=lowerCAmelCase_	, image_processor=lowerCAmelCase_	, top_k=2    )
          snake_case_						       =							[
              Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]
          return image_classifier, examples
  
    def 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : Optional[int]    )   ->       Optional[Any]:
          snake_case_						       =							image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  
          self.assertEqual(
              lowerCAmelCase_	, [
                  {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                  {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
              ]	, )
  
          import datasets
  
          snake_case_						       =							datasets.load_dataset("hf-internal-testing/fixtures_image_utils"	, "image"	, split="test"    )
  
          # Accepts URL + PIL.Image + lists
          snake_case_						       =							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(
              lowerCAmelCase_	, [
                  [
                      {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                      {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                  ],
                  [
                      {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                      {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                  ],
                  [
                      {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                      {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                  ],
                  [
                      {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                      {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                  ],
                  [
                      {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                      {"score": ANY(lowerCAmelCase_    ), "label": ANY(lowerCAmelCase_    )},
                  ],
              ]	, )
  
    @require_torch
    def 		_lowerCamelCase	(self  : List[str]    )   ->       List[Any]:
          snake_case_						       =							"hf-internal-testing/tiny-random-vit"
          snake_case_						       =							pipeline("image-classification"	, model=lowerCAmelCase_    )
  
          snake_case_						       =							image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"    )
          self.assertEqual(
              nested_simplify(lowerCAmelCase_	, decimals=4    )	, [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]	, )
  
          snake_case_						       =							image_classifier(
              [
                  "http://images.cocodataset.org/val2017/000000039769.jpg",
                  "http://images.cocodataset.org/val2017/000000039769.jpg",
              ]	, top_k=2	, )
          self.assertEqual(
              nested_simplify(lowerCAmelCase_	, 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]    )   ->       str:
          snake_case_						       =							"hf-internal-testing/tiny-random-vit"
          snake_case_						       =							pipeline("image-classification"	, model=lowerCAmelCase_	, framework="tf"    )
  
          snake_case_						       =							image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"    )
          self.assertEqual(
              nested_simplify(lowerCAmelCase_	, decimals=4    )	, [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]	, )
  
          snake_case_						       =							image_classifier(
              [
                  "http://images.cocodataset.org/val2017/000000039769.jpg",
                  "http://images.cocodataset.org/val2017/000000039769.jpg",
              ]	, top_k=2	, )
          self.assertEqual(
              nested_simplify(lowerCAmelCase_	, 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  : Any    )   ->       List[Any]:
          snake_case_						       =							PreTrainedTokenizer()
  
          # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
          snake_case_						       =							pipeline(
              "image-classification"	, model="hf-internal-testing/tiny-random-vit"	, tokenizer=lowerCAmelCase_    )
  
          self.assertIs(image_classifier.tokenizer	, lowerCAmelCase_    )
  
  
    @slow
    @require_torch
    def 		_lowerCamelCase	(self  : Tuple    )   ->       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
          snake_case_						       =							pipeline("image-classification"	, model="deepmind/vision-perceiver-conv"    )
          snake_case_						       =							image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"    )
          self.assertEqual(
              nested_simplify(lowerCAmelCase_	, decimals=4    )	, [
                  {"score": 0.4_385, "label": "tabby, tabby cat"},
                  {"score": 0.321, "label": "tiger cat"},
                  {"score": 0.0_502, "label": "Egyptian cat"},
                  {"score": 0.0_137, "label": "crib, cot"},
                  {"score": 0.007, "label": "radiator"},
              ]	, )
  
          snake_case_						       =							pipeline("image-classification"	, model="deepmind/vision-perceiver-fourier"    )
          snake_case_						       =							image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"    )
          self.assertEqual(
              nested_simplify(lowerCAmelCase_	, decimals=4    )	, [
                  {"score": 0.5_658, "label": "tabby, tabby cat"},
                  {"score": 0.1_309, "label": "tiger cat"},
                  {"score": 0.0_722, "label": "Egyptian cat"},
                  {"score": 0.0_707, "label": "remote control, remote"},
                  {"score": 0.0_082, "label": "computer keyboard, keypad"},
              ]	, )
  
          snake_case_						       =							pipeline("image-classification"	, model="deepmind/vision-perceiver-learned"    )
          snake_case_						       =							image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"    )
          self.assertEqual(
              nested_simplify(lowerCAmelCase_	, decimals=4    )	, [
                  {"score": 0.3_022, "label": "tabby, tabby cat"},
                  {"score": 0.2_362, "label": "Egyptian cat"},
                  {"score": 0.1_856, "label": "tiger cat"},
                  {"score": 0.0_324, "label": "remote control, remote"},
                  {"score": 0.0_096, "label": "quilt, comforter, comfort, puff"},
              ]	, )
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  A           =   {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["DetaImageProcessor"]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   [
           "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DetaForObjectDetection",
           "DetaModel",
           "DetaPreTrainedModel",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .image_processing_deta import DetaImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deta import (
                DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetaForObjectDetection,
                DetaModel,
                DetaPreTrainedModel,
            )
  
  else:
       import sys
  
       A           =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  