import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:


       class 			UpperCAmelCase__    :



         """simple docstring"""






         @staticmethod
         def 	_UpperCAmelCase				(				*__lowerCAmelCase:			List[Any]				,				**__lowerCAmelCase:			List[Any]   ) -> Optional[int]:




           '''simple docstring'''




           pass


@is_pipeline_test
@require_vision
@require_torch
class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	List[str]    		= MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






  def 	_UpperCAmelCase				(				self:			Optional[int]				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Optional[int]   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       pipeline(
        "zero-shot-object-detection"				,				model="hf-internal-testing/tiny-random-owlvit-object-detection"   )

    __UpperCAmelCase						    =       [
        {
            "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "candidate_labels": ["cat", "remote", "couch"],
        }
    ]
    return object_detector, examples






  def 	_UpperCAmelCase				(				self:			Dict				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Dict   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       object_detector(examples[0]				,				threshold=0.0   )

    __UpperCAmelCase						    =       len(A_   )
    self.assertGreater(A_				,				0   )
    self.assertEqual(
        A_				,				[
            {
                "score": ANY(A_   ),
                "label": ANY(A_   ),
                "box": {"xmin": ANY(A_   ), "ymin": ANY(A_   ), "xmax": ANY(A_   ), "ymax": ANY(A_   )},
            }
            for i in range(A_   )
        ]				,				)






  @require_tf
  @unittest.skip("Zero Shot Object Detection not implemented in TF"   )
  def 	_UpperCAmelCase				(				self:			Dict   ) -> Any:




    '''simple docstring'''




    pass






  @require_torch
  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> int:




    '''simple docstring'''




    __UpperCAmelCase						    =       pipeline(
        "zero-shot-object-detection"				,				model="hf-internal-testing/tiny-random-owlvit-object-detection"   )

    __UpperCAmelCase						    =       object_detector(
        "./tests/fixtures/tests_samples/COCO/000000039769.png"				,				candidate_labels=["cat", "remote", "couch"]				,				threshold=0.64				,				)

    self.assertEqual(
        nested_simplify(A_				,				decimals=4   )				,				[
            {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
            {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
            {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
            {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
            {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
            {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
            {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
            {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
            {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
        ]				,				)

    __UpperCAmelCase						    =       object_detector(
        [
            {
                "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                "candidate_labels": ["cat", "remote", "couch"],
            }
        ]				,				threshold=0.64				,				)

    self.assertEqual(
        nested_simplify(A_				,				decimals=4   )				,				[
            [
                {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
            ]
        ]				,				)






  @require_torch
  @slow
  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       pipeline("zero-shot-object-detection"   )

    __UpperCAmelCase						    =       object_detector(
        "http://images.cocodataset.org/val2017/000000039769.jpg"				,				candidate_labels=["cat", "remote", "couch"]				,				)
    self.assertEqual(
        nested_simplify(A_				,				decimals=4   )				,				[
            {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
            {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
            {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
            {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
            {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
        ]				,				)

    __UpperCAmelCase						    =       object_detector(
        [
            {
                "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                "candidate_labels": ["cat", "remote", "couch"],
            },
            {
                "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
                "candidate_labels": ["cat", "remote", "couch"],
            },
        ]				,				)
    self.assertEqual(
        nested_simplify(A_				,				decimals=4   )				,				[
            [
                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
            ],
            [
                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
            ],
        ]				,				)






  @require_tf
  @unittest.skip("Zero Shot Object Detection not implemented in TF"   )
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> Optional[int]:




    '''simple docstring'''




    pass






  @require_torch
  @slow
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       0.2
    __UpperCAmelCase						    =       pipeline("zero-shot-object-detection"   )

    __UpperCAmelCase						    =       object_detector(
        "http://images.cocodataset.org/val2017/000000039769.jpg"				,				candidate_labels=["cat", "remote", "couch"]				,				threshold=A_				,				)
    self.assertEqual(
        nested_simplify(A_				,				decimals=4   )				,				[
            {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
            {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
            {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
        ]				,				)






  @require_torch
  @slow
  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       2
    __UpperCAmelCase						    =       pipeline("zero-shot-object-detection"   )

    __UpperCAmelCase						    =       object_detector(
        "http://images.cocodataset.org/val2017/000000039769.jpg"				,				candidate_labels=["cat", "remote", "couch"]				,				top_k=A_				,				)
    self.assertEqual(
        nested_simplify(A_				,				decimals=4   )				,				[
            {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
            {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
        ]				,				)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_							=      {
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""SqueezeBertTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SqueezeBertForMaskedLM""",
           """SqueezeBertForMultipleChoice""",
           """SqueezeBertForQuestionAnswering""",
           """SqueezeBertForSequenceClassification""",
           """SqueezeBertForTokenClassification""",
           """SqueezeBertModel""",
           """SqueezeBertModule""",
           """SqueezeBertPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_squeezebert import (
           SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SqueezeBertConfig,
           SqueezeBertOnnxConfig,
       )
       from .tokenization_squeezebert import SqueezeBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_squeezebert import (
                  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SqueezeBertForMaskedLM,
                  SqueezeBertForMultipleChoice,
                  SqueezeBertForQuestionAnswering,
                  SqueezeBertForSequenceClassification,
                  SqueezeBertForTokenClassification,
                  SqueezeBertModel,
                  SqueezeBertModule,
                  SqueezeBertPreTrainedModel,
              )

else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



