





'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:







  class   A_	:




   @staticmethod
   def      lowercase	(					*snake_case_       :    Dict	,			**snake_case_       :    Optional[Any]    ):
        pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   A_	(  unittest.TestCase			):
 _lowerCamelCase  : List[str]           =							MODEL_FOR_OBJECT_DETECTION_MAPPING




 def      lowercase	(					self       :    List[Any]	,			snake_case_       :    List[str]	,			snake_case_       :    str	,			snake_case_       :    str    ):
      _UpperCAmelCase     							=   ObjectDetectionPipeline(model=snake_case_	,			image_processor=snake_case_    )
      return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Tuple	,			snake_case_       :    List[str]    ):
      _UpperCAmelCase     							=   object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"	,			threshold=0.0    )

      self.assertGreater(len(snake_case_    )	,			0    )
      for detected_object in outputs:
           self.assertEqual(
               snake_case_	,			{
                   "score": ANY(snake_case_    ),
                   "label": ANY(snake_case_    ),
                   "box": {"xmin": ANY(snake_case_    ), "ymin": ANY(snake_case_    ), "xmax": ANY(snake_case_    ), "ymax": ANY(snake_case_    )},
               }	,			)

      import datasets

      _UpperCAmelCase     							=   datasets.load_dataset("hf-internal-testing/fixtures_image_utils"	,			"image"	,			split="test"    )

      _UpperCAmelCase     							=   [
          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"],
      ]
      _UpperCAmelCase     							=   object_detector(snake_case_	,			threshold=0.0    )

      self.assertEqual(len(snake_case_    )	,			len(snake_case_    )    )
      for outputs in batch_outputs:
           self.assertGreater(len(snake_case_    )	,			0    )
           for detected_object in outputs:
                self.assertEqual(
                    snake_case_	,			{
                        "score": ANY(snake_case_    ),
                        "label": ANY(snake_case_    ),
                        "box": {"xmin": ANY(snake_case_    ), "ymin": ANY(snake_case_    ), "xmax": ANY(snake_case_    ), "ymax": ANY(snake_case_    )},
                    }	,			)




 @require_tf
 @unittest.skip("Object detection not implemented in TF"    )
 def      lowercase	(					self       :    List[str]    ):
      pass




 @require_torch
 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   "hf-internal-testing/tiny-detr-mobilenetsv3"

      _UpperCAmelCase     							=   AutoModelForObjectDetection.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   AutoFeatureExtractor.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   ObjectDetectionPipeline(model=snake_case_	,			feature_extractor=snake_case_    )

      _UpperCAmelCase     							=   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"	,			threshold=0.0    )

      self.assertEqual(
          nested_simplify(snake_case_	,			decimals=4    )	,			[
              {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
              {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
          ]	,			)

      _UpperCAmelCase     							=   object_detector(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]	,			threshold=0.0	,			)

      self.assertEqual(
          nested_simplify(snake_case_	,			decimals=4    )	,			[
              [
                  {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
                  {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
              ],
              [
                  {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
                  {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
              ],
          ]	,			)




 @require_torch
 @slow
 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   "facebook/detr-resnet-50"

      _UpperCAmelCase     							=   AutoModelForObjectDetection.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   AutoFeatureExtractor.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   ObjectDetectionPipeline(model=snake_case_	,			feature_extractor=snake_case_    )

      _UpperCAmelCase     							=   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"    )
      self.assertEqual(
          nested_simplify(snake_case_	,			decimals=4    )	,			[
              {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
              {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
              {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
              {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
              {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
          ]	,			)

      _UpperCAmelCase     							=   object_detector(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]    )
      self.assertEqual(
          nested_simplify(snake_case_	,			decimals=4    )	,			[
              [
                  {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
                  {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
                  {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
                  {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
                  {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
              ],
              [
                  {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
                  {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
                  {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
                  {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
                  {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
              ],
          ]	,			)




 @require_torch
 @slow
 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   "facebook/detr-resnet-50"

      _UpperCAmelCase     							=   pipeline("object-detection"	,			model=snake_case_    )

      _UpperCAmelCase     							=   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"    )
      self.assertEqual(
          nested_simplify(snake_case_	,			decimals=4    )	,			[
              {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
              {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
              {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
              {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
              {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
          ]	,			)

      _UpperCAmelCase     							=   object_detector(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]    )
      self.assertEqual(
          nested_simplify(snake_case_	,			decimals=4    )	,			[
              [
                  {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
                  {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
                  {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
                  {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
                  {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
              ],
              [
                  {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
                  {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
                  {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
                  {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
                  {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
              ],
          ]	,			)




 @require_torch
 @slow
 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   0.9_9_8_5
      _UpperCAmelCase     							=   "facebook/detr-resnet-50"

      _UpperCAmelCase     							=   pipeline("object-detection"	,			model=snake_case_    )

      _UpperCAmelCase     							=   object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"	,			threshold=snake_case_    )
      self.assertEqual(
          nested_simplify(snake_case_	,			decimals=4    )	,			[
              {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
              {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
          ]	,			)




 @require_torch
 @require_pytesseract
 @slow
 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   "Narsil/layoutlmv3-finetuned-funsd"
      _UpperCAmelCase     							=   0.9_9_9_3

      _UpperCAmelCase     							=   pipeline("object-detection"	,			model=snake_case_	,			threshold=snake_case_    )

      _UpperCAmelCase     							=   object_detector(
          "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"    )
      self.assertEqual(
          nested_simplify(snake_case_	,			decimals=4    )	,			[
              {"score": 0.9_9_9_3, "label": "I-ANSWER", "box": {"xmin": 2_9_4, "ymin": 2_5_4, "xmax": 3_4_3, "ymax": 2_6_4}},
              {"score": 0.9_9_9_3, "label": "I-ANSWER", "box": {"xmin": 2_9_4, "ymin": 2_5_4, "xmax": 3_4_3, "ymax": 2_6_4}},
          ]	,			)











'''simple docstring'''




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_pt_objects import *  # noqa F403
else:
  from .scheduling_consistency_models import CMStochasticIterativeScheduler
  from .scheduling_ddim import DDIMScheduler
  from .scheduling_ddim_inverse import DDIMInverseScheduler
  from .scheduling_ddim_parallel import DDIMParallelScheduler
  from .scheduling_ddpm import DDPMScheduler
  from .scheduling_ddpm_parallel import DDPMParallelScheduler
  from .scheduling_deis_multistep import DEISMultistepScheduler
  from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
  from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
  from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
  from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
  from .scheduling_euler_discrete import EulerDiscreteScheduler
  from .scheduling_heun_discrete import HeunDiscreteScheduler
  from .scheduling_ipndm import IPNDMScheduler
  from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
  from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
  from .scheduling_karras_ve import KarrasVeScheduler
  from .scheduling_pndm import PNDMScheduler
  from .scheduling_repaint import RePaintScheduler
  from .scheduling_sde_ve import ScoreSdeVeScheduler
  from .scheduling_sde_vp import ScoreSdeVpScheduler
  from .scheduling_unclip import UnCLIPScheduler
  from .scheduling_unipc_multistep import UniPCMultistepScheduler
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_flax_objects import *  # noqa F403
else:
  from .scheduling_ddim_flax import FlaxDDIMScheduler
  from .scheduling_ddpm_flax import FlaxDDPMScheduler
  from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
  from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
  from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
  from .scheduling_pndm_flax import FlaxPNDMScheduler
  from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
  from .scheduling_utils_flax import (
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      broadcast_to_shape_from_left,
  )


try:
  if not (is_torch_available() and is_scipy_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
  from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
  if not (is_torch_available() and is_torchsde_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
  from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler




