



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 	_lowercase							(   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		,       ):
						"""simple docstring"""

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




	def      UpperCamelCase_       (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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		):
						"""simple docstring"""

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

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

						return expected_height, expected_width




@require_torch
@require_vision
class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							YolosImageProcessor if is_vision_available() else None




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   YolosImageProcessingTester(self		)




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return self.image_processor_tester.prepare_image_processor_dict()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   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					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						pass




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						a					     =   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					     =   image_processing(image_inputs[0]		,       return_tensors="pt"		).pixel_values

						a     ,				a					     =   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					     =   self.image_processor_tester.get_expected_values(lowerCamelCase_		,       batched=lowerCamelCase_		)

						a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						a					     =   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					     =   image_processing(image_inputs[0]		,       return_tensors="pt"		).pixel_values

						a     ,				a					     =   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					     =   image_processing(lowerCamelCase_		,       return_tensors="pt"		).pixel_values

						a     ,				a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						a					     =   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					     =   image_processing(image_inputs[0]		,       return_tensors="pt"		).pixel_values

						a     ,				a					     =   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					     =   image_processing(lowerCamelCase_		,       return_tensors="pt"		).pixel_values

						a     ,				a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.image_processing_class(**self.image_processor_dict		)
						a					     =   self.image_processing_class(do_resize=lowerCamelCase_		,       do_normalize=lowerCamelCase_		,       do_rescale=lowerCamelCase_		)
						# create random PyTorch tensors
						a					     =   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
						a					     =   image_processing_a.pad(lowerCamelCase_		,       return_tensors="pt"		)
						a					     =   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      UpperCamelCase_       (self		):
						"""simple docstring"""

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

						a					     =   {"image_id": 39769, "annotations": target}

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

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

						a					     =   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					     =   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
						a					     =   torch.Size([6, 4]		)
						self.assertEqual(encoding["labels"][0]["boxes"].shape		,       lowerCamelCase_		)
						a					     =   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					     =   torch.tensor([39769]		)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,       lowerCamelCase_		)		)
						# verify is_crowd
						a					     =   torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,       lowerCamelCase_		)		)
						# verify class_labels
						a					     =   torch.tensor([75, 75, 63, 65, 17, 17]		)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,       lowerCamelCase_		)		)
						# verify orig_size
						a					     =   torch.tensor([480, 640]		)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,       lowerCamelCase_		)		)
						# verify size
						a					     =   torch.tensor([800, 1066]		)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,       lowerCamelCase_		)		)




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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

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

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

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

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

						a					     =   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					     =   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
						a					     =   torch.Size([6, 4]		)
						self.assertEqual(encoding["labels"][0]["boxes"].shape		,       lowerCamelCase_		)
						a					     =   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					     =   torch.tensor([39769]		)
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]		,       lowerCamelCase_		)		)
						# verify is_crowd
						a					     =   torch.tensor([0, 0, 0, 0, 0, 0]		)
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]		,       lowerCamelCase_		)		)
						# verify class_labels
						a					     =   torch.tensor([17, 17, 63, 75, 75, 93]		)
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]		,       lowerCamelCase_		)		)
						# verify masks
						a					     =   822873
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()		,       lowerCamelCase_		)
						# verify orig_size
						a					     =   torch.tensor([480, 640]		)
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]		,       lowerCamelCase_		)		)
						# verify size
						a					     =   torch.tensor([800, 1066]		)
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]		,       lowerCamelCase_		)		)





import cmath
import math






def     a(      A       :    float ,     A       :    float ,     A       :    float ,     A       :    float       )		->       complex:
					"""simple docstring"""





					a					     =   math.radians(A       )
					a					     =   math.radians(A       )

					# Convert voltage and current to rectangular form
					a					     =   cmath.rect(A ,     A       )
					a					     =   cmath.rect(A ,     A       )

					# Calculate apparent power
					return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()
