


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__						:







							@staticmethod
							def __a						(							*_lowercase   :    str			,			**_lowercase   :    Dict		):
									pass



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







						def __a						(							self   :    List[str]			,			_lowercase   :    Optional[int]			,			_lowercase   :    str			,			_lowercase   :    Tuple		):
								A					       = ObjectDetectionPipeline(model=_SCREAMING_SNAKE_CASE			,			image_processor=_SCREAMING_SNAKE_CASE		)
								return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







						def __a						(							self   :    List[str]			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[Any]		):
								A					       = object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'			,			threshold=0.0		)

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

								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(_SCREAMING_SNAKE_CASE			,			threshold=0.0		)

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







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







						@require_torch
						def __a						(							self   :    Tuple		):
								A					       = 'hf-internal-testing/tiny-detr-mobilenetsv3'

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

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

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

								self.assertEqual(
								    nested_simplify(_SCREAMING_SNAKE_CASE			,			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 __a						(							self   :    Tuple		):
								A					       = 'facebook/detr-resnet-50'

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

								A					       = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		)
								self.assertEqual(
								    nested_simplify(_SCREAMING_SNAKE_CASE			,			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					       = object_detector(
								    [
								        'http://images.cocodataset.org/val2017/000000039769.jpg',
								        'http://images.cocodataset.org/val2017/000000039769.jpg',
								    ]		)
								self.assertEqual(
								    nested_simplify(_SCREAMING_SNAKE_CASE			,			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 __a						(							self   :    str		):
								A					       = 'facebook/detr-resnet-50'

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

								A					       = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		)
								self.assertEqual(
								    nested_simplify(_SCREAMING_SNAKE_CASE			,			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					       = object_detector(
								    [
								        'http://images.cocodataset.org/val2017/000000039769.jpg',
								        'http://images.cocodataset.org/val2017/000000039769.jpg',
								    ]		)
								self.assertEqual(
								    nested_simplify(_SCREAMING_SNAKE_CASE			,			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 __a						(							self   :    Tuple		):
								A					       = 0.9_9_8_5
								A					       = 'facebook/detr-resnet-50'

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

								A					       = object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'			,			threshold=_SCREAMING_SNAKE_CASE		)
								self.assertEqual(
								    nested_simplify(_SCREAMING_SNAKE_CASE			,			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 __a						(							self   :    Optional[int]		):
								A					       = 'Narsil/layoutlmv3-finetuned-funsd'
								A					       = 0.9_9_9_3

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

								A					       = object_detector(
								    'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'		)
								self.assertEqual(
								    nested_simplify(_SCREAMING_SNAKE_CASE			,			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 unittest.mock import Mock, patch

from file_transfer.send_file import send_file







@patch('socket.socket'	)
@patch('builtins.open'	)
def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		A					       = Mock()
		A					       = conn, Mock()
		A					       = iter([1, None]	)
		A					       = lambda UpperCamelCase__	: next(UpperCamelCase__	)

		# ===== invoke =====
		send_file(filename='mytext.txt' ,			testing=UpperCamelCase__	)

		# ===== ensurance =====
		sock.assert_called_once()
		sock.return_value.bind.assert_called_once()
		sock.return_value.listen.assert_called_once()
		sock.return_value.accept.assert_called_once()
		conn.recv.assert_called_once()

		file.return_value.__enter__.assert_called_once()
		file.return_value.__enter__.return_value.read.assert_called()

		conn.send.assert_called_once()
		conn.close.assert_called_once()
		sock.return_value.shutdown.assert_called_once()
		sock.return_value.close.assert_called_once()





