






"""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 DeformableDetrImageProcessor



class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	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     ,     ):
		# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
		A_		:						Any	    =   size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
		A_		:						Dict	    =   parent
		A_		:						List[str]	    =   batch_size
		A_		:						Optional[Any]	    =   num_channels
		A_		:						Tuple	    =   min_resolution
		A_		:						int	    =   max_resolution
		A_		:						Dict	    =   do_resize
		A_		:						Any	    =   size
		A_		:						List[str]	    =   do_normalize
		A_		:						List[Any]	    =   image_mean
		A_		:						int	    =   image_std
		A_		:						List[Any]	    =   do_rescale
		A_		:						Tuple	    =   rescale_factor
		A_		:						Dict	    =   do_pad





	def    lowerCamelCase(self							):
		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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=False							):
		if not batched:
			A_		:						List[str]	    =   image_inputs[0]
			if isinstance(lowerCAmelCase_     ,     Image.Image							):
				A_    ,     A_		:						Any	    =   image.size
			else:
				A_    ,     A_		:						Optional[int]	    =   image.shape[1], image.shape[2]
			if w < h:
				A_		:						Dict	    =   int(self.size["""shortest_edge"""] * h / w							)
				A_		:						str	    =   self.size["""shortest_edge"""]
			elif w > h:
				A_		:						int	    =   self.size["""shortest_edge"""]
				A_		:						Any	    =   int(self.size["""shortest_edge"""] * w / h							)
			else:
				A_		:						Tuple	    =   self.size["""shortest_edge"""]
				A_		:						Dict	    =   self.size["""shortest_edge"""]

		else:
			A_		:						str	    =   []
			for image in image_inputs:
				A_    ,     A_		:						Tuple	    =   self.get_expected_values([image]							)
				expected_values.append((expected_height, expected_width)							)
			A_		:						Dict	    =   max(lowerCAmelCase_     ,     key=lambda lowerCAmelCase_							: item[0]							)[0]
			A_		:						Optional[int]	    =   max(lowerCAmelCase_     ,     key=lambda lowerCAmelCase_							: item[1]							)[1]

		return expected_height, expected_width






@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				int    =				DeformableDetrImageProcessor if is_vision_available() else None





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   DeformableDetrImageProcessingTester(self							)





	@property
	def    lowerCamelCase(self							):
		return self.image_processor_tester.prepare_image_processor_dict()





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   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_     ,     """do_rescale"""							)							)
		self.assertTrue(hasattr(lowerCAmelCase_     ,     """do_pad"""							)							)
		self.assertTrue(hasattr(lowerCAmelCase_     ,     """size"""							)							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   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_							)

		A_		:						Union[str, Any]	    =   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    lowerCamelCase(self							):
		pass





	def    lowerCamelCase(self							):
		# Initialize image_processing
		A_		:						List[str]	    =   self.image_processing_class(**self.image_processor_dict							)
		# create random PIL images
		A_		:						Tuple	    =   prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=lowerCAmelCase_							)
		for image in image_inputs:
			self.assertIsInstance(lowerCAmelCase_     ,     Image.Image							)

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

		A_    ,     A_		:						Dict	    =   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
		A_    ,     A_		:						Union[str, Any]	    =   self.image_processor_tester.get_expected_values(lowerCAmelCase_     ,     batched=lowerCAmelCase_							)

		A_		:						Tuple	    =   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    lowerCamelCase(self							):
		# Initialize image_processing
		A_		:						Optional[int]	    =   self.image_processing_class(**self.image_processor_dict							)
		# create random numpy tensors
		A_		:						Any	    =   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
		A_		:						List[str]	    =   image_processing(image_inputs[0]     ,     return_tensors="""pt"""							).pixel_values

		A_    ,     A_		:						Optional[int]	    =   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
		A_		:						str	    =   image_processing(lowerCAmelCase_     ,     return_tensors="""pt"""							).pixel_values

		A_    ,     A_		:						Union[str, Any]	    =   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    lowerCamelCase(self							):
		# Initialize image_processing
		A_		:						Optional[int]	    =   self.image_processing_class(**self.image_processor_dict							)
		# create random PyTorch tensors
		A_		:						Any	    =   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
		A_		:						List[Any]	    =   image_processing(image_inputs[0]     ,     return_tensors="""pt"""							).pixel_values

		A_    ,     A_		:						List[Any]	    =   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
		A_		:						Optional[int]	    =   image_processing(lowerCAmelCase_     ,     return_tensors="""pt"""							).pixel_values

		A_    ,     A_		:						Union[str, Any]	    =   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,
		    )     ,     )





	@slow
	def    lowerCamelCase(self							):
		# prepare image and target
		A_		:						List[Any]	    =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
		with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""     ,     """r"""							) as f:
			A_		:						Optional[int]	    =   json.loads(f.read()							)

		A_		:						List[str]	    =   {"""image_id""": 39769, """annotations""": target}

		# encode them
		A_		:						str	    =   DeformableDetrImageProcessor()
		A_		:						Union[str, Any]	    =   image_processing(images=lowerCAmelCase_     ,     annotations=lowerCAmelCase_     ,     return_tensors="""pt"""							)

		# verify pixel values
		A_		:						Dict	    =   torch.Size([1, 3, 800, 1066]							)
		self.assertEqual(encoding["""pixel_values"""].shape     ,     lowerCAmelCase_							)

		A_		:						Optional[Any]	    =   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
		A_		:						Any	    =   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]     ,     lowerCAmelCase_							)							)
		# verify boxes
		A_		:						int	    =   torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape     ,     lowerCAmelCase_							)
		A_		:						Tuple	    =   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
		A_		:						List[Any]	    =   torch.tensor([39769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]     ,     lowerCAmelCase_							)							)
		# verify is_crowd
		A_		:						Dict	    =   torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]     ,     lowerCAmelCase_							)							)
		# verify class_labels
		A_		:						Tuple	    =   torch.tensor([75, 75, 63, 65, 17, 17]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]     ,     lowerCAmelCase_							)							)
		# verify orig_size
		A_		:						Union[str, Any]	    =   torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]     ,     lowerCAmelCase_							)							)
		# verify size
		A_		:						Tuple	    =   torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]     ,     lowerCAmelCase_							)							)





	@slow
	def    lowerCamelCase(self							):
		# prepare image, target and masks_path
		A_		:						Dict	    =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
		with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""     ,     """r"""							) as f:
			A_		:						Tuple	    =   json.loads(f.read()							)

		A_		:						Any	    =   {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

		A_		:						int	    =   pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

		# encode them
		A_		:						List[str]	    =   DeformableDetrImageProcessor(format="""coco_panoptic"""							)
		A_		:						Any	    =   image_processing(images=lowerCAmelCase_     ,     annotations=lowerCAmelCase_     ,     masks_path=lowerCAmelCase_     ,     return_tensors="""pt"""							)

		# verify pixel values
		A_		:						Tuple	    =   torch.Size([1, 3, 800, 1066]							)
		self.assertEqual(encoding["""pixel_values"""].shape     ,     lowerCAmelCase_							)

		A_		:						Dict	    =   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
		A_		:						str	    =   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]     ,     lowerCAmelCase_							)							)
		# verify boxes
		A_		:						str	    =   torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape     ,     lowerCAmelCase_							)
		A_		:						List[str]	    =   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
		A_		:						Dict	    =   torch.tensor([39769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]     ,     lowerCAmelCase_							)							)
		# verify is_crowd
		A_		:						Any	    =   torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]     ,     lowerCAmelCase_							)							)
		# verify class_labels
		A_		:						Dict	    =   torch.tensor([17, 17, 63, 75, 75, 93]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]     ,     lowerCAmelCase_							)							)
		# verify masks
		A_		:						str	    =   822873
		self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()     ,     lowerCAmelCase_							)
		# verify orig_size
		A_		:						Optional[Any]	    =   torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]     ,     lowerCAmelCase_							)							)
		# verify size
		A_		:						int	    =   torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]     ,     lowerCAmelCase_							)							)









"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						List[str]	    =   prime_factors(snake_case__					)
	if is_square_free(snake_case__					):
		return -1 if len(snake_case__					) % 2 else 1
	return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()

