




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 			a__    (					unittest.TestCase      ):






						def __init__(   self		,			_A		,			_A=7		,			_A=3		,			_A=3_0		,			_A=4_0_0		,			_A=True		,			_A=None		,			_A=True		,			_A=[0.5, 0.5, 0.5]		,			_A=[0.5, 0.5, 0.5]		,			_A=True		,			_A=1 / 2_5_5		,			_A=True		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       size if size is not None else {"""shortest_edge""": 1_8, """longest_edge""": 1_3_3_3}
											__lowerCAmelCase				   =       parent
											__lowerCAmelCase				   =       batch_size
											__lowerCAmelCase				   =       num_channels
											__lowerCAmelCase				   =       min_resolution
											__lowerCAmelCase				   =       max_resolution
											__lowerCAmelCase				   =       do_resize
											__lowerCAmelCase				   =       size
											__lowerCAmelCase				   =       do_normalize
											__lowerCAmelCase				   =       image_mean
											__lowerCAmelCase				   =       image_std
											__lowerCAmelCase				   =       do_rescale
											__lowerCAmelCase				   =       rescale_factor
											__lowerCAmelCase				   =       do_pad






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""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 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A=False			):




											"""simple docstring"""



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

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

											return expected_height, expected_width






@require_torch
@require_vision
class 			a__    (					UpperCAmelCase__ ,				unittest.TestCase      ):
						_a							:    List[str]					=    YolosImageProcessor if is_vision_available() else None






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       YolosImageProcessingTester(self			)






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.image_processing_class.from_dict(self.image_processor_dict			)
											self.assertEqual(image_processor.size		,			{"shortest_edge": 1_8, "longest_edge": 1_3_3_3}			)
											self.assertEqual(image_processor.do_pad		,			_A			)

											__lowerCAmelCase				   =       self.image_processing_class.from_dict(
											    self.image_processor_dict		,			size=4_2		,			max_size=8_4		,			pad_and_return_pixel_mask=_A			)
											self.assertEqual(image_processor.size		,			{"shortest_edge": 4_2, "longest_edge": 8_4}			)
											self.assertEqual(image_processor.do_pad		,			_A			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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

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

											__lowerCAmelCase				   =       self.image_processor_tester.get_expected_values(_A			)

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

											# Test batched
											__lowerCAmelCase				   =       self.image_processor_tester.get_expected_values(_A		,			batched=_A			)

											__lowerCAmelCase				   =       image_processing(_A		,			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 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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

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

											__lowerCAmelCase				   =       self.image_processor_tester.get_expected_values(_A			)

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

											# Test batched
											__lowerCAmelCase				   =       image_processing(_A		,			return_tensors="pt"			).pixel_values

											__lowerCAmelCase				   =       self.image_processor_tester.get_expected_values(_A		,			batched=_A			)

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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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

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

											__lowerCAmelCase				   =       self.image_processor_tester.get_expected_values(_A			)

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

											# Test batched
											__lowerCAmelCase				   =       image_processing(_A		,			return_tensors="pt"			).pixel_values

											__lowerCAmelCase				   =       self.image_processor_tester.get_expected_values(_A		,			batched=_A			)

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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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

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

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






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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

											__lowerCAmelCase				   =       {"""image_id""": 3_9_7_6_9, """annotations""": target}

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

											# verify pixel values
											__lowerCAmelCase				   =       torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
											self.assertEqual(encoding["pixel_values"].shape		,			_A			)

											__lowerCAmelCase				   =       torch.tensor([0.27_96, 0.31_38, 0.34_81]			)
											self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,			_A		,			atol=1E-4			)			)

											# verify area
											__lowerCAmelCase				   =       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,			_A			)			)
											# verify boxes
											__lowerCAmelCase				   =       torch.Size([6, 4]			)
											self.assertEqual(encoding["labels"][0]["boxes"].shape		,			_A			)
											__lowerCAmelCase				   =       torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,			_A		,			atol=1E-3			)			)
											# verify image_id
											__lowerCAmelCase				   =       torch.tensor([3_9_7_6_9]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,			_A			)			)
											# verify is_crowd
											__lowerCAmelCase				   =       torch.tensor([0, 0, 0, 0, 0, 0]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,			_A			)			)
											# verify class_labels
											__lowerCAmelCase				   =       torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,			_A			)			)
											# verify orig_size
											__lowerCAmelCase				   =       torch.tensor([4_8_0, 6_4_0]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,			_A			)			)
											# verify size
											__lowerCAmelCase				   =       torch.tensor([8_0_0, 1_0_6_6]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,			_A			)			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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

											__lowerCAmelCase				   =       {"""file_name""": """000000039769.png""", """image_id""": 3_9_7_6_9, """segments_info""": target}

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

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

											# verify pixel values
											__lowerCAmelCase				   =       torch.Size([1, 3, 8_0_0, 1_0_6_6]			)
											self.assertEqual(encoding["pixel_values"].shape		,			_A			)

											__lowerCAmelCase				   =       torch.tensor([0.27_96, 0.31_38, 0.34_81]			)
											self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]		,			_A		,			atol=1E-4			)			)

											# verify area
											__lowerCAmelCase				   =       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["area"]		,			_A			)			)
											# verify boxes
											__lowerCAmelCase				   =       torch.Size([6, 4]			)
											self.assertEqual(encoding["labels"][0]["boxes"].shape		,			_A			)
											__lowerCAmelCase				   =       torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]		,			_A		,			atol=1E-3			)			)
											# verify image_id
											__lowerCAmelCase				   =       torch.tensor([3_9_7_6_9]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,			_A			)			)
											# verify is_crowd
											__lowerCAmelCase				   =       torch.tensor([0, 0, 0, 0, 0, 0]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,			_A			)			)
											# verify class_labels
											__lowerCAmelCase				   =       torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,			_A			)			)
											# verify masks
											__lowerCAmelCase				   =       8_2_2_8_7_3
											self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,			_A			)
											# verify orig_size
											__lowerCAmelCase				   =       torch.tensor([4_8_0, 6_4_0]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,			_A			)			)
											# verify size
											__lowerCAmelCase				   =       torch.tensor([8_0_0, 1_0_6_6]			)
											self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,			_A			)			)








'''simple docstring'''







from math import isqrt


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int  )  ->			bool:
       return all(number % divisor != 0 for divisor in range(2    ,isqrt(_UpperCAmelCase  ) + 1  )  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10**6  )  ->			int:
       _a    : List[Any]     					=0
       _a    : str     					=1
       _a    : Optional[Any]     					=7
       while prime_candidate < max_prime:
              primes_count += is_prime(_UpperCAmelCase  )

              cube_index += 1
              prime_candidate += 6 * cube_index

       return primes_count


if __name__ == "__main__":
   print(F"{solution() = }")


