

'''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 						A  (		*_a      :	Tuple							,					**_a      :	Optional[Any] )	->    Optional[Any]:





            '''simple docstring'''






            pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							A__    (       unittest.TestCase  ):
  A__		 =				MODEL_FOR_OBJECT_DETECTION_MAPPING





  def 						A  (		self      :	str							,					_a      :	Union[str, Any]							,					_a      :	Optional[Any]							,					_a      :	Tuple )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =ObjectDetectionPipeline(model=_a							,					image_processor=_a )
       return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]





  def 						A  (		self      :	List[Any]							,					_a      :	int							,					_a      :	List[Any] )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'							,					threshold=0.0 )

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

       import datasets

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

       _SCREAMING_SNAKE_CASE							    =[
           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'],
       ]
       _SCREAMING_SNAKE_CASE							    =object_detector(_a							,					threshold=0.0 )

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





  @require_tf
  @unittest.skip('Object detection not implemented in TF' )
  def 						A  (		self      :	Tuple )	->    int:





       '''simple docstring'''






       pass





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='hf-internal-testing/tiny-detr-mobilenetsv3'

       _SCREAMING_SNAKE_CASE							    =AutoModelForObjectDetection.from_pretrained(_a )
       _SCREAMING_SNAKE_CASE							    =AutoFeatureExtractor.from_pretrained(_a )
       _SCREAMING_SNAKE_CASE							    =ObjectDetectionPipeline(model=_a							,					feature_extractor=_a )

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

       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
               {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
           ]							,					)

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

       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               [
                   {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                   {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
               ],
               [
                   {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                   {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
               ],
           ]							,					)





  @require_torch
  @slow
  def 						A  (		self      :	List[str] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='facebook/detr-resnet-50'

       _SCREAMING_SNAKE_CASE							    =AutoModelForObjectDetection.from_pretrained(_a )
       _SCREAMING_SNAKE_CASE							    =AutoFeatureExtractor.from_pretrained(_a )
       _SCREAMING_SNAKE_CASE							    =ObjectDetectionPipeline(model=_a							,					feature_extractor=_a )

       _SCREAMING_SNAKE_CASE							    =object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
               {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
               {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
               {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
               {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
           ]							,					)

       _SCREAMING_SNAKE_CASE							    =object_detector(
           [
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ] )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               [
                   {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                   {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                   {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                   {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                   {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
               ],
               [
                   {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                   {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                   {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                   {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                   {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
               ],
           ]							,					)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='facebook/detr-resnet-50'

       _SCREAMING_SNAKE_CASE							    =pipeline('object-detection'							,					model=_a )

       _SCREAMING_SNAKE_CASE							    =object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
               {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
               {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
               {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
               {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
           ]							,					)

       _SCREAMING_SNAKE_CASE							    =object_detector(
           [
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ] )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               [
                   {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                   {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                   {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                   {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                   {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
               ],
               [
                   {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                   {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                   {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                   {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                   {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
               ],
           ]							,					)





  @require_torch
  @slow
  def 						A  (		self      :	int )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =0.99_85
       _SCREAMING_SNAKE_CASE							    ='facebook/detr-resnet-50'

       _SCREAMING_SNAKE_CASE							    =pipeline('object-detection'							,					model=_a )

       _SCREAMING_SNAKE_CASE							    =object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'							,					threshold=_a )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
               {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
           ]							,					)





  @require_torch
  @require_pytesseract
  @slow
  def 						A  (		self      :	int )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='Narsil/layoutlmv3-finetuned-funsd'
       _SCREAMING_SNAKE_CASE							    =0.99_93

       _SCREAMING_SNAKE_CASE							    =pipeline('object-detection'							,					model=_a							,					threshold=_a )

       _SCREAMING_SNAKE_CASE							    =object_detector(
           'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png' )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               {'score': 0.99_93, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
               {'score': 0.99_93, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
           ]							,					)



'''simple docstring'''
from typing import TYPE_CHECKING

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


lowerCamelCase :      int					 =     {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     ["GLPNFeatureExtractor"]
     lowerCamelCase :      Optional[int]					 =     ["GLPNImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GLPNForDepthEstimation",
         "GLPNLayer",
         "GLPNModel",
         "GLPNPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_glpn import GLPNFeatureExtractor
          from .image_processing_glpn import GLPNImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_glpn import (
              GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
              GLPNForDepthEstimation,
              GLPNLayer,
              GLPNModel,
              GLPNPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
