

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 SCREAMING_SNAKE_CASE     :
     """simple docstring"""






     @staticmethod
     def 	SCREAMING_SNAKE_CASE   (      *lowerCAmelCase			:   str	,							**lowerCAmelCase			:   str   )							->     Optional[int]:






        """simple docstring"""







        pass



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Any										=MODEL_FOR_OBJECT_DETECTION_MAPPING







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Optional[Any]   )							->     List[str]:






     """simple docstring"""







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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Tuple   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	,							threshold=0.0   )

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

     import datasets

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

     __lowerCAmelCase  :      int    =						[
         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"""],
     ]
     __lowerCAmelCase  :      Dict    =						object_detector(__lowerCAmelCase	,							threshold=0.0   )

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







  @require_tf
  @unittest.skip("""Object detection not implemented in TF"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     str:






     """simple docstring"""







     pass







  @require_torch
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						"""hf-internal-testing/tiny-detr-mobilenetsv3"""

     __lowerCAmelCase  :      Union[str, Any]    =						AutoModelForObjectDetection.from_pretrained(__lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						AutoFeatureExtractor.from_pretrained(__lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						ObjectDetectionPipeline(model=__lowerCAmelCase	,							feature_extractor=__lowerCAmelCase   )

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

     self.assertEqual(
         nested_simplify(__lowerCAmelCase	,							decimals=4   )	,							[
             {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
             {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
         ]	,							)

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

     self.assertEqual(
         nested_simplify(__lowerCAmelCase	,							decimals=4   )	,							[
             [
                 {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
                 {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
             ],
             [
                 {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
                 {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 1_59, """ymin""": 1_20, """xmax""": 4_80, """ymax""": 3_59}},
             ],
         ]	,							)







  @require_torch
  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						"""facebook/detr-resnet-50"""

     __lowerCAmelCase  :      Union[str, Any]    =						AutoModelForObjectDetection.from_pretrained(__lowerCAmelCase   )
     __lowerCAmelCase  :      Any    =						AutoFeatureExtractor.from_pretrained(__lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						ObjectDetectionPipeline(model=__lowerCAmelCase	,							feature_extractor=__lowerCAmelCase   )

     __lowerCAmelCase  :      Optional[int]    =						object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
     self.assertEqual(
         nested_simplify(__lowerCAmelCase	,							decimals=4   )	,							[
             {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
             {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
             {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
             {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
             {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
         ]	,							)

     __lowerCAmelCase  :      str    =						object_detector(
         [
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]   )
     self.assertEqual(
         nested_simplify(__lowerCAmelCase	,							decimals=4   )	,							[
             [
                 {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
             [
                 {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
         ]	,							)







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						"""facebook/detr-resnet-50"""

     __lowerCAmelCase  :      List[str]    =						pipeline("""object-detection"""	,							model=__lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
     self.assertEqual(
         nested_simplify(__lowerCAmelCase	,							decimals=4   )	,							[
             {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
             {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
             {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
             {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
             {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
         ]	,							)

     __lowerCAmelCase  :      Optional[int]    =						object_detector(
         [
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
             """http://images.cocodataset.org/val2017/000000039769.jpg""",
         ]   )
     self.assertEqual(
         nested_simplify(__lowerCAmelCase	,							decimals=4   )	,							[
             [
                 {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
             [
                 {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 1_75, """ymax""": 1_17}},
                 {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 3_33, """ymin""": 72, """xmax""": 3_68, """ymax""": 1_87}},
                 {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 6_39, """ymax""": 4_73}},
                 {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
                 {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
             ],
         ]	,							)







  @require_torch
  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						0.9985
     __lowerCAmelCase  :      Optional[int]    =						"""facebook/detr-resnet-50"""

     __lowerCAmelCase  :      List[Any]    =						pipeline("""object-detection"""	,							model=__lowerCAmelCase   )

     __lowerCAmelCase  :      List[Any]    =						object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""	,							threshold=__lowerCAmelCase   )
     self.assertEqual(
         nested_simplify(__lowerCAmelCase	,							decimals=4   )	,							[
             {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 3_14, """ymax""": 4_70}},
             {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 3_45, """ymin""": 23, """xmax""": 6_40, """ymax""": 3_68}},
         ]	,							)







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






     """simple docstring"""







     __lowerCAmelCase  :      str    =						"""Narsil/layoutlmv3-finetuned-funsd"""
     __lowerCAmelCase  :      str    =						0.9993

     __lowerCAmelCase  :      List[str]    =						pipeline("""object-detection"""	,							model=__lowerCAmelCase	,							threshold=__lowerCAmelCase   )

     __lowerCAmelCase  :      Any    =						object_detector(
         """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""   )
     self.assertEqual(
         nested_simplify(__lowerCAmelCase	,							decimals=4   )	,							[
             {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_94, """ymin""": 2_54, """xmax""": 3_43, """ymax""": 2_64}},
             {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 2_94, """ymin""": 2_54, """xmax""": 3_43, """ymax""": 2_64}},
         ]	,							)





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__UpperCAmelCase								=			logging.get_logger(__name__)



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def __init__(      self			:   Optional[Any]	,							*lowerCAmelCase			:   int	,							**lowerCAmelCase			:   Optional[int]   )							->     None:






     """simple docstring"""







     warnings.warn(
         """The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
         """ use GLPNImageProcessor instead."""	,							lowerCAmelCase	,							)
     super().__init__(*lowerCAmelCase	,							**lowerCAmelCase   )


