



'''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 				lowercase__      :





						@staticmethod
						def       UpperCamelCase_    (					*lowerCamelCase__   :							str					,**lowerCamelCase__   :							List[Any]					):







												'''simple docstring'''
												pass






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





	def       UpperCamelCase_    (					self   :							Dict					,lowerCamelCase__   :							int					,lowerCamelCase__   :							Dict					,lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  ObjectDetectionPipeline(model=lowerCamelCase__					,image_processor=lowerCamelCase__					)
							return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]





	def       UpperCamelCase_    (					self   :							Dict					,lowerCamelCase__   :							Tuple					,lowerCamelCase__   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  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

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

							_UpperCamelCase   :						Dict												=  [
							    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   :						Any												=  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       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							pass





	@require_torch
	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  'hf-internal-testing/tiny-detr-mobilenetsv3'

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

							_UpperCamelCase   :						List[Any]												=  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': 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}},
							    ]					,)

							_UpperCamelCase   :						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(lowerCamelCase__					,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       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  'facebook/detr-resnet-50'

							_UpperCamelCase   :						Dict												=  AutoModelForObjectDetection.from_pretrained(lowerCamelCase__					)
							_UpperCamelCase   :						str												=  AutoFeatureExtractor.from_pretrained(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  ObjectDetectionPipeline(model=lowerCamelCase__					,feature_extractor=lowerCamelCase__					)

							_UpperCamelCase   :						Tuple												=  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': 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}},
							    ]					,)

							_UpperCamelCase   :						Any												=  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': 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       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  'facebook/detr-resnet-50'

							_UpperCamelCase   :						Tuple												=  pipeline('object-detection'					,model=lowerCamelCase__					)

							_UpperCamelCase   :						str												=  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': 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}},
							    ]					,)

							_UpperCamelCase   :						Any												=  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': 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       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  0.9_9_8_5
							_UpperCamelCase   :						str												=  'facebook/detr-resnet-50'

							_UpperCamelCase   :						List[str]												=  pipeline('object-detection'					,model=lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  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': 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       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  'Narsil/layoutlmv3-finetuned-funsd'
							_UpperCamelCase   :						Union[str, Any]												=  0.9_9_9_3

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

							_UpperCamelCase   :						int												=  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': 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'''
from __future__ import annotations






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  position
						_UpperCamelCase   :						Any												=  [
						    (y + 1, x + 2),
						    (y - 1, x + 2),
						    (y + 1, x - 2),
						    (y - 1, x - 2),
						    (y + 2, x + 1),
						    (y + 2, x - 1),
						    (y - 2, x + 1),
						    (y - 2, x - 1),
						]
						_UpperCamelCase   :						Optional[Any]												=  []

						for position in positions:
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(UpperCAmelCase_				)

						return permissible_positions






def A__       (	UpperCAmelCase_				):
						return not any(elem == 0 for row in board for elem in row				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):

						if is_complete(UpperCAmelCase_				):
												return True

						for position in get_valid_pos(UpperCAmelCase_    ,    len(UpperCAmelCase_				)				):
												_UpperCamelCase							,     _UpperCamelCase   :						Any												=  position

												if board[y][x] == 0:
																		_UpperCamelCase   :						int												=  curr + 1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    UpperCAmelCase_    ,    curr + 1				):
																								return True
																		_UpperCamelCase   :						Union[str, Any]												=  0

						return False






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  [[0 for i in range(UpperCAmelCase_				)] for j in range(UpperCAmelCase_				)]

						for i in range(UpperCAmelCase_				):
												for j in range(UpperCAmelCase_				):
																		_UpperCamelCase   :						Tuple												=  1
																		if open_knight_tour_helper(UpperCAmelCase_    ,    (i, j)    ,    1				):
																								return board
																		_UpperCamelCase   :						Union[str, Any]												=  0

						_UpperCamelCase   :						int												=  f'Open Kight Tour cannot be performed on a board of size {n}'
						raise ValueError(UpperCAmelCase_				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


