



'''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 	UpperCAmelCase	:


			'''simple docstring'''




			@staticmethod
			def       lowercase_  (   *lowerCAmelCase_							,  **lowerCAmelCase_) ->      Optional[int]:



								"""simple docstring"""







								pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	UpperCAmelCase	(		unittest.TestCase):


	'''simple docstring'''




	__magic_name__					: int  =      MODEL_FOR_OBJECT_DETECTION_MAPPING




	def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      str:



						"""simple docstring"""







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




	def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Optional[int]:



						"""simple docstring"""







						a_       		=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

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

						a_       		=[
						    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"],
						]
						a_       		=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       lowercase_  (   self) ->      List[Any]:



						"""simple docstring"""







						pass




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



						"""simple docstring"""







						a_       		="hf-internal-testing/tiny-detr-mobilenetsv3"

						a_       		=AutoModelForObjectDetection.from_pretrained(lowerCAmelCase_)
						a_       		=AutoFeatureExtractor.from_pretrained(lowerCAmelCase_)
						a_       		=ObjectDetectionPipeline(model=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_)

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

						self.assertEqual(
						    nested_simplify(lowerCAmelCase_							,  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}},
						    ]							,  )

						a_       		=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": 0.3_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) ->      Union[str, Any]:



						"""simple docstring"""







						a_       		="facebook/detr-resnet-50"

						a_       		=AutoModelForObjectDetection.from_pretrained(lowerCAmelCase_)
						a_       		=AutoFeatureExtractor.from_pretrained(lowerCAmelCase_)
						a_       		=ObjectDetectionPipeline(model=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_)

						a_       		=object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
						self.assertEqual(
						    nested_simplify(lowerCAmelCase_							,  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}},
						    ]							,  )

						a_       		=object_detector(
						    [
						        "http://images.cocodataset.org/val2017/000000039769.jpg",
						        "http://images.cocodataset.org/val2017/000000039769.jpg",
						    ])
						self.assertEqual(
						    nested_simplify(lowerCAmelCase_							,  decimals=4)							,  [
						        [
						            {"score": 0.9_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) ->      Any:



						"""simple docstring"""







						a_       		="facebook/detr-resnet-50"

						a_       		=pipeline("object-detection"							,  model=lowerCAmelCase_)

						a_       		=object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
						self.assertEqual(
						    nested_simplify(lowerCAmelCase_							,  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}},
						    ]							,  )

						a_       		=object_detector(
						    [
						        "http://images.cocodataset.org/val2017/000000039769.jpg",
						        "http://images.cocodataset.org/val2017/000000039769.jpg",
						    ])
						self.assertEqual(
						    nested_simplify(lowerCAmelCase_							,  decimals=4)							,  [
						        [
						            {"score": 0.9_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]:



						"""simple docstring"""







						a_       		=0.9_9_8_5
						a_       		="facebook/detr-resnet-50"

						a_       		=pipeline("object-detection"							,  model=lowerCAmelCase_)

						a_       		=object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"							,  threshold=lowerCAmelCase_)
						self.assertEqual(
						    nested_simplify(lowerCAmelCase_							,  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) ->      Union[str, Any]:



						"""simple docstring"""







						a_       		="Narsil/layoutlmv3-finetuned-funsd"
						a_       		=0.9_9_9_3

						a_       		=pipeline("object-detection"							,  model=lowerCAmelCase_							,  threshold=lowerCAmelCase_)

						a_       		=object_detector(
						    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png")
						self.assertEqual(
						    nested_simplify(lowerCAmelCase_							,  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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase							       =			{
    '''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', '''VisionEncoderDecoderOnnxConfig''']
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''VisionEncoderDecoderModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''TFVisionEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			['''FlaxVisionEncoderDecoderModel''']

if TYPE_CHECKING:
  from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
  import sys

  lowercase							       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


