


'''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 						_lowerCamelCase					:




		'''simple docstring'''


		@staticmethod
		def  snake_case__   (      *__lowercase      ,      **__lowercase						):

			"""simple docstring"""







			pass

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




	'''simple docstring'''
	__lowercase				:		int    							=						MODEL_FOR_OBJECT_DETECTION_MAPPING


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

		"""simple docstring"""







		__A  :		List[Any]       =			ObjectDetectionPipeline(model=__UpperCamelCase      ,      image_processor=__UpperCamelCase						)
		return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


	def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

		"""simple docstring"""







		__A  :		Optional[Any]       =			object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'      ,      threshold=0.0						)

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

		import datasets

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

		__A  :		List[Any]       =			[
		    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  :		str       =			object_detector(__UpperCamelCase      ,      threshold=0.0						)

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


	@require_tf
	@unittest.skip('Object detection not implemented in TF'						)
	def  snake_case__   (      self						):

		"""simple docstring"""







		pass


	@require_torch
	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		List[str]       =			'hf-internal-testing/tiny-detr-mobilenetsv3'

		__A  :		List[str]       =			AutoModelForObjectDetection.from_pretrained(__UpperCamelCase						)
		__A  :		Dict       =			AutoFeatureExtractor.from_pretrained(__UpperCamelCase						)
		__A  :		str       =			ObjectDetectionPipeline(model=__UpperCamelCase      ,      feature_extractor=__UpperCamelCase						)

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

		self.assertEqual(
		    nested_simplify(__UpperCamelCase      ,      decimals=4						)      ,      [
		        {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
		        {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
		    ]      ,      )

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

		self.assertEqual(
		    nested_simplify(__UpperCamelCase      ,      decimals=4						)      ,      [
		        [
		            {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
		            {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
		        ],
		        [
		            {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
		            {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
		        ],
		    ]      ,      )


	@require_torch
	@slow
	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Optional[int]       =			'facebook/detr-resnet-50'

		__A  :		Optional[int]       =			AutoModelForObjectDetection.from_pretrained(__UpperCamelCase						)
		__A  :		str       =			AutoFeatureExtractor.from_pretrained(__UpperCamelCase						)
		__A  :		List[Any]       =			ObjectDetectionPipeline(model=__UpperCamelCase      ,      feature_extractor=__UpperCamelCase						)

		__A  :		Tuple       =			object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						)
		self.assertEqual(
		    nested_simplify(__UpperCamelCase      ,      decimals=4						)      ,      [
		        {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
		        {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
		        {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
		        {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
		        {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
		    ]      ,      )

		__A  :		List[str]       =			object_detector(
		    [
		        'http://images.cocodataset.org/val2017/000000039769.jpg',
		        'http://images.cocodataset.org/val2017/000000039769.jpg',
		    ]						)
		self.assertEqual(
		    nested_simplify(__UpperCamelCase      ,      decimals=4						)      ,      [
		        [
		            {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
		            {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
		            {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
		            {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
		            {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
		        ],
		        [
		            {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
		            {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
		            {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
		            {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
		            {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
		        ],
		    ]      ,      )


	@require_torch
	@slow
	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		List[Any]       =			'facebook/detr-resnet-50'

		__A  :		Tuple       =			pipeline('object-detection'      ,      model=__UpperCamelCase						)

		__A  :		Union[str, Any]       =			object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						)
		self.assertEqual(
		    nested_simplify(__UpperCamelCase      ,      decimals=4						)      ,      [
		        {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
		        {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
		        {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
		        {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
		        {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
		    ]      ,      )

		__A  :		List[Any]       =			object_detector(
		    [
		        'http://images.cocodataset.org/val2017/000000039769.jpg',
		        'http://images.cocodataset.org/val2017/000000039769.jpg',
		    ]						)
		self.assertEqual(
		    nested_simplify(__UpperCamelCase      ,      decimals=4						)      ,      [
		        [
		            {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
		            {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
		            {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
		            {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
		            {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
		        ],
		        [
		            {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
		            {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
		            {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
		            {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
		            {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
		        ],
		    ]      ,      )


	@require_torch
	@slow
	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Optional[Any]       =			0.9_9_8_5
		__A  :		str       =			'facebook/detr-resnet-50'

		__A  :		Optional[Any]       =			pipeline('object-detection'      ,      model=__UpperCamelCase						)

		__A  :		str       =			object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'      ,      threshold=__UpperCamelCase						)
		self.assertEqual(
		    nested_simplify(__UpperCamelCase      ,      decimals=4						)      ,      [
		        {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
		        {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
		    ]      ,      )







	@require_torch
	@require_pytesseract
	@slow
	def  snake_case__   (      self						):

		"""simple docstring"""







		__A  :		Optional[int]       =			'Narsil/layoutlmv3-finetuned-funsd'
		__A  :		Optional[Any]       =			0.9_9_9_3

		__A  :		Union[str, Any]       =			pipeline('object-detection'      ,      model=__UpperCamelCase      ,      threshold=__UpperCamelCase						)

		__A  :		Dict       =			object_detector(
		    'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'						)
		self.assertEqual(
		    nested_simplify(__UpperCamelCase      ,      decimals=4						)      ,      [
		        {'score': 0.9_9_9_3, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
		        {'score': 0.9_9_9_3, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
		    ]      ,      )










'''simple docstring'''
import unittest

from transformers import DonutProcessor


UpperCAmelCase_ :     str								=      'naver-clova-ix/donut-base'

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			DonutProcessor.from_pretrained(__lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			{
      'name': 'John Doe',
      'age': '99',
      'city': 'Atlanta',
      'state': 'GA',
      'zip': '30301',
      'phone': '123-4567',
      'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
  }

  __A  :		Optional[Any]       =			(
      '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
      '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
      '<s_nicknames><s_nickname>Johnny</s_nickname>'
      '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
  )
  __A  :		List[Any]       =			self.processor.tokenajson(__lowercase						)

  self.assertDictEqual(__lowercase      ,      __lowercase						)






