



'''simple docstring'''


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import YolosImageProcessor




class      __UpperCamelCase       (unittest.TestCase			):







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=7 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=30 ,				_lowerCAmelCase=400 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=None ,				_lowerCAmelCase=True ,				_lowerCAmelCase=[0.5, 0.5, 0.5] ,				_lowerCAmelCase=[0.5, 0.5, 0.5] ,				_lowerCAmelCase=True ,				_lowerCAmelCase=1 / 255 ,				_lowerCAmelCase=True ,				)    ->    Any:
							'''simple docstring'''






							lowercase        =							size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
							lowercase        =							parent
							lowercase        =							batch_size
							lowercase        =							num_channels
							lowercase        =							min_resolution
							lowercase        =							max_resolution
							lowercase        =							do_resize
							lowercase        =							size
							lowercase        =							do_normalize
							lowercase        =							image_mean
							lowercase        =							image_std
							lowercase        =							do_rescale
							lowercase        =							rescale_factor
							lowercase        =							do_pad







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase=False						)    ->    int:
							'''simple docstring'''






							if not batched:
													lowercase        =							image_inputs[0]
													if isinstance(_lowerCAmelCase ,				Image.Image						):
																			lowercase     ,   lowercase        =							image.size
													else:
																			lowercase     ,   lowercase        =							image.shape[1], image.shape[2]
													if w < h:
																			lowercase        =							int(self.size["""shortest_edge"""] * h / w						)
																			lowercase        =							self.size["""shortest_edge"""]
													elif w > h:
																			lowercase        =							self.size["""shortest_edge"""]
																			lowercase        =							int(self.size["""shortest_edge"""] * w / h						)
													else:
																			lowercase        =							self.size["""shortest_edge"""]
																			lowercase        =							self.size["""shortest_edge"""]

							else:
													lowercase        =							[]
													for image in image_inputs:
																			lowercase     ,   lowercase        =							self.get_expected_values([image]						)
																			expected_values.append((expected_height, expected_width)						)
													lowercase        =							max(_lowerCAmelCase ,				key=lambda _lowerCAmelCase						: item[0]						)[0]
													lowercase        =							max(_lowerCAmelCase ,				key=lambda _lowerCAmelCase						: item[1]						)[1]

							return expected_height, expected_width




@require_torch
@require_vision
class      __UpperCamelCase       (_UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					YolosImageProcessor if is_vision_available() else None







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase        =							YolosImageProcessingTester(self						)







	@property
	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""image_mean"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""image_std"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""do_normalize"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""do_resize"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""size"""						)						)







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							lowercase        =							self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size ,				{"""shortest_edge""": 18, """longest_edge""": 1333}						)
							self.assertEqual(image_processor.do_pad ,				_lowerCAmelCase						)

							lowercase        =							self.image_processing_class.from_dict(
							    self.image_processor_dict ,				size=42 ,				max_size=84 ,				pad_and_return_pixel_mask=_lowerCAmelCase						)
							self.assertEqual(image_processor.size ,				{"""shortest_edge""": 42, """longest_edge""": 84}						)
							self.assertEqual(image_processor.do_pad ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase        =							prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_lowerCAmelCase						)
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				Image.Image						)

							# Test not batched input
							lowercase        =							image_processing(image_inputs[0] ,				return_tensors="""pt"""						).pixel_values

							lowercase     ,   lowercase        =							self.image_processor_tester.get_expected_values(_lowerCAmelCase						)

							self.assertEqual(
							    encoded_images.shape ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,				)

							# Test batched
							lowercase     ,   lowercase        =							self.image_processor_tester.get_expected_values(_lowerCAmelCase ,				batched=_lowerCAmelCase						)

							lowercase        =							image_processing(_lowerCAmelCase ,				return_tensors="""pt"""						).pixel_values
							self.assertEqual(
							    encoded_images.shape ,				(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,				)







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowercase        =							prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_lowerCAmelCase ,				numpify=_lowerCAmelCase						)
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				np.ndarray						)

							# Test not batched input
							lowercase        =							image_processing(image_inputs[0] ,				return_tensors="""pt"""						).pixel_values

							lowercase     ,   lowercase        =							self.image_processor_tester.get_expected_values(_lowerCAmelCase						)

							self.assertEqual(
							    encoded_images.shape ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,				)

							# Test batched
							lowercase        =							image_processing(_lowerCAmelCase ,				return_tensors="""pt"""						).pixel_values

							lowercase     ,   lowercase        =							self.image_processor_tester.get_expected_values(_lowerCAmelCase ,				batched=_lowerCAmelCase						)

							self.assertEqual(
							    encoded_images.shape ,				(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,				)







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowercase        =							prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_lowerCAmelCase ,				torchify=_lowerCAmelCase						)
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				torch.Tensor						)

							# Test not batched input
							lowercase        =							image_processing(image_inputs[0] ,				return_tensors="""pt"""						).pixel_values

							lowercase     ,   lowercase        =							self.image_processor_tester.get_expected_values(_lowerCAmelCase						)

							self.assertEqual(
							    encoded_images.shape ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,				)

							# Test batched
							lowercase        =							image_processing(_lowerCAmelCase ,				return_tensors="""pt"""						).pixel_values

							lowercase     ,   lowercase        =							self.image_processor_tester.get_expected_values(_lowerCAmelCase ,				batched=_lowerCAmelCase						)

							self.assertEqual(
							    encoded_images.shape ,				(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,				)







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)
							lowercase        =							self.image_processing_class(do_resize=_lowerCAmelCase ,				do_normalize=_lowerCAmelCase ,				do_rescale=_lowerCAmelCase						)
							# create random PyTorch tensors
							lowercase        =							prepare_image_inputs(self.image_processor_tester ,				equal_resolution=_lowerCAmelCase ,				torchify=_lowerCAmelCase						)
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				torch.Tensor						)

							# Test whether the method "pad" and calling the image processor return the same tensors
							lowercase        =							image_processing_a.pad(_lowerCAmelCase ,				return_tensors="""pt"""						)
							lowercase        =							image_processing_a(_lowerCAmelCase ,				return_tensors="""pt"""						)

							self.assertTrue(
							    torch.allclose(encoded_images_with_method["""pixel_values"""] ,				encoded_images["""pixel_values"""] ,				atol=1E-4						)						)







	@slow
	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
							with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" ,				"""r"""						) as f:
													lowercase        =							json.loads(f.read()						)

							lowercase        =							{"""image_id""": 3_9769, """annotations""": target}

							# encode them
							lowercase        =							YolosImageProcessor.from_pretrained("""hustvl/yolos-small"""						)
							lowercase        =							image_processing(images=_lowerCAmelCase ,				annotations=_lowerCAmelCase ,				return_tensors="""pt"""						)

							# verify pixel values
							lowercase        =							torch.Size([1, 3, 800, 1066]						)
							self.assertEqual(encoding["""pixel_values"""].shape ,				_lowerCAmelCase						)

							lowercase        =							torch.tensor([0.2796, 0.3138, 0.3481]						)
							self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,				_lowerCAmelCase ,				atol=1E-4						)						)

							# verify area
							lowercase        =							torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] ,				_lowerCAmelCase						)						)
							# verify boxes
							lowercase        =							torch.Size([6, 4]						)
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape ,				_lowerCAmelCase						)
							lowercase        =							torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] ,				_lowerCAmelCase ,				atol=1E-3						)						)
							# verify image_id
							lowercase        =							torch.tensor([3_9769]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] ,				_lowerCAmelCase						)						)
							# verify is_crowd
							lowercase        =							torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] ,				_lowerCAmelCase						)						)
							# verify class_labels
							lowercase        =							torch.tensor([75, 75, 63, 65, 17, 17]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] ,				_lowerCAmelCase						)						)
							# verify orig_size
							lowercase        =							torch.tensor([480, 640]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] ,				_lowerCAmelCase						)						)
							# verify size
							lowercase        =							torch.tensor([800, 1066]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] ,				_lowerCAmelCase						)						)







	@slow
	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
							with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" ,				"""r"""						) as f:
													lowercase        =							json.loads(f.read()						)

							lowercase        =							{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

							lowercase        =							pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""						)

							# encode them
							lowercase        =							YolosImageProcessor(format="""coco_panoptic"""						)
							lowercase        =							image_processing(images=_lowerCAmelCase ,				annotations=_lowerCAmelCase ,				masks_path=_lowerCAmelCase ,				return_tensors="""pt"""						)

							# verify pixel values
							lowercase        =							torch.Size([1, 3, 800, 1066]						)
							self.assertEqual(encoding["""pixel_values"""].shape ,				_lowerCAmelCase						)

							lowercase        =							torch.tensor([0.2796, 0.3138, 0.3481]						)
							self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,				_lowerCAmelCase ,				atol=1E-4						)						)

							# verify area
							lowercase        =							torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] ,				_lowerCAmelCase						)						)
							# verify boxes
							lowercase        =							torch.Size([6, 4]						)
							self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape ,				_lowerCAmelCase						)
							lowercase        =							torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] ,				_lowerCAmelCase ,				atol=1E-3						)						)
							# verify image_id
							lowercase        =							torch.tensor([3_9769]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] ,				_lowerCAmelCase						)						)
							# verify is_crowd
							lowercase        =							torch.tensor([0, 0, 0, 0, 0, 0]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] ,				_lowerCAmelCase						)						)
							# verify class_labels
							lowercase        =							torch.tensor([17, 17, 63, 75, 75, 93]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] ,				_lowerCAmelCase						)						)
							# verify masks
							lowercase        =							82_2873
							self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() ,				_lowerCAmelCase						)
							# verify orig_size
							lowercase        =							torch.tensor([480, 640]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] ,				_lowerCAmelCase						)						)
							# verify size
							lowercase        =							torch.tensor([800, 1066]						)
							self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] ,				_lowerCAmelCase						)						)







'''simple docstring'''


from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							HfArgumentParser(lowercase_						)
						lowercase        =							parser.parse_args_into_dataclasses()[0]
						lowercase        =							TensorFlowBenchmark(args=lowercase_						)
						try:
												lowercase        =							parser.parse_args_into_dataclasses()[0]
						except ValueError as e:
												lowercase        =							"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
												lowercase        =							""" """.join(str(lowercase_						).split(""" """						)[:-1]						)
												lowercase        =							""""""
												lowercase        =							eval(str(lowercase_						).split(""" """						)[-1]						)
												lowercase        =							[]
												for arg in depreciated_args:
																		# arg[2:] removes '--'
																		if arg[2:] in TensorFlowBenchmark.deprecated_args:
																								# arg[5:] removes '--no_'
																								full_error_msg += arg_error_msg.format(arg[5:]						)
																		else:
																								wrong_args.append(lowercase_						)
												if len(lowercase_						) > 0:
																		lowercase        =							full_error_msg + begin_error_msg + str(lowercase_						)
												raise ValueError(lowercase_						)
						benchmark.run()


if __name__ == "__main__":
						main()


