
'''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"},
            ]	, )



'''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__)

