



'''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 ConditionalDetrImageProcessor






class 				lowercase__      (				unittest.TestCase  ):





	def __init__(					self   :							int					,lowerCamelCase__   :							Union[str, Any]					,lowerCamelCase__   :							Tuple=7					,lowerCamelCase__   :							Dict=3					,lowerCamelCase__   :							List[Any]=30					,lowerCamelCase__   :							Optional[Any]=400					,lowerCamelCase__   :							Tuple=True					,lowerCamelCase__   :							List[str]=None					,lowerCamelCase__   :							Tuple=True					,lowerCamelCase__   :							Dict=[0.5, 0.5, 0.5]					,lowerCamelCase__   :							int=[0.5, 0.5, 0.5]					,lowerCamelCase__   :							Optional[Any]=True					,lowerCamelCase__   :							Union[str, Any]=1 / 255					,lowerCamelCase__   :							List[Any]=True					,):







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





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''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   :							Optional[Any]					,lowerCamelCase__   :							Optional[Any]					,lowerCamelCase__   :							Optional[int]=False					):







							'''simple docstring'''
							if not batched:
													_UpperCamelCase   :						Any												=  image_inputs[0]
													if isinstance(lowerCamelCase__					,Image.Image					):
																			_UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  image.size
													else:
																			_UpperCamelCase							,     _UpperCamelCase   :						str												=  image.shape[1], image.shape[2]
													if w < h:
																			_UpperCamelCase   :						Optional[int]												=  int(self.size['shortest_edge'] * h / w					)
																			_UpperCamelCase   :						List[Any]												=  self.size['shortest_edge']
													elif w > h:
																			_UpperCamelCase   :						Union[str, Any]												=  self.size['shortest_edge']
																			_UpperCamelCase   :						Dict												=  int(self.size['shortest_edge'] * w / h					)
													else:
																			_UpperCamelCase   :						List[str]												=  self.size['shortest_edge']
																			_UpperCamelCase   :						str												=  self.size['shortest_edge']

							else:
													_UpperCamelCase   :						int												=  []
													for image in image_inputs:
																			_UpperCamelCase							,     _UpperCamelCase   :						int												=  self.get_expected_values([image]					)
																			expected_values.append((expected_height, expected_width)					)
													_UpperCamelCase   :						Union[str, Any]												=  max(lowerCamelCase__					,key=lambda lowerCamelCase__					: item[0]					)[0]
													_UpperCamelCase   :						Optional[int]												=  max(lowerCamelCase__					,key=lambda lowerCamelCase__					: item[1]					)[1]

							return expected_height, expected_width






@require_torch
@require_vision
class 				lowercase__      (				lowercase		,   unittest.TestCase  ):
	lowercase__    							=     ConditionalDetrImageProcessor if is_vision_available() else None





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  ConditionalDetrImageProcessingTester(self					)





	@property
	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  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   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, 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__					)

							_UpperCamelCase   :						Dict												=  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   :							Any					):







							'''simple docstring'''
							pass





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase   :						Tuple												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							_UpperCamelCase   :						int												=  prepare_image_inputs(self.image_processor_tester					,equal_resolution=lowerCamelCase__					)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__					,Image.Image					)

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

							_UpperCamelCase							,     _UpperCamelCase   :						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
							_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  self.image_processor_tester.get_expected_values(lowerCamelCase__					,batched=lowerCamelCase__					)

							_UpperCamelCase   :						Union[str, Any]												=  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   :							Tuple					):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase   :						Optional[Any]												=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							_UpperCamelCase   :						List[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
							_UpperCamelCase   :						Dict												=  image_processing(image_inputs[0]					,return_tensors='pt'					).pixel_values

							_UpperCamelCase							,     _UpperCamelCase   :						Union[str, 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
							_UpperCamelCase   :						Optional[Any]												=  image_processing(lowerCamelCase__					,return_tensors='pt'					).pixel_values

							_UpperCamelCase							,     _UpperCamelCase   :						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       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase   :						List[str]												=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							_UpperCamelCase   :						Union[str, 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
							_UpperCamelCase   :						List[str]												=  image_processing(image_inputs[0]					,return_tensors='pt'					).pixel_values

							_UpperCamelCase							,     _UpperCamelCase   :						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
							_UpperCamelCase   :						Dict												=  image_processing(lowerCamelCase__					,return_tensors='pt'					).pixel_values

							_UpperCamelCase							,     _UpperCamelCase   :						List[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       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							# prepare image and target
							_UpperCamelCase   :						Any												=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'					,'r'					) as f:
													_UpperCamelCase   :						Optional[Any]												=  json.loads(f.read()					)

							_UpperCamelCase   :						Dict												=  {'image_id': 39769, 'annotations': target}

							# encode them
							_UpperCamelCase   :						str												=  ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'					)
							_UpperCamelCase   :						Tuple												=  image_processing(images=lowerCamelCase__					,annotations=lowerCamelCase__					,return_tensors='pt'					)

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

							_UpperCamelCase   :						Union[str, Any]												=  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]					)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]					,lowerCamelCase__					,atol=1E-4					)					)

							# verify area
							_UpperCamelCase   :						Dict												=  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']					,lowerCamelCase__					)					)
							# verify boxes
							_UpperCamelCase   :						Union[str, Any]												=  torch.Size([6, 4]					)
							self.assertEqual(encoding['labels'][0]['boxes'].shape					,lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]					,lowerCamelCase__					,atol=1E-3					)					)
							# verify image_id
							_UpperCamelCase   :						Optional[int]												=  torch.tensor([39769]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,lowerCamelCase__					)					)
							# verify is_crowd
							_UpperCamelCase   :						int												=  torch.tensor([0, 0, 0, 0, 0, 0]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,lowerCamelCase__					)					)
							# verify class_labels
							_UpperCamelCase   :						Any												=  torch.tensor([75, 75, 63, 65, 17, 17]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,lowerCamelCase__					)					)
							# verify orig_size
							_UpperCamelCase   :						List[Any]												=  torch.tensor([480, 640]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,lowerCamelCase__					)					)
							# verify size
							_UpperCamelCase   :						str												=  torch.tensor([800, 1066]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,lowerCamelCase__					)					)





	@slow
	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							# prepare image, target and masks_path
							_UpperCamelCase   :						Dict												=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
							with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'					,'r'					) as f:
													_UpperCamelCase   :						int												=  json.loads(f.read()					)

							_UpperCamelCase   :						Any												=  {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

							_UpperCamelCase   :						Dict												=  pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'					)

							# encode them
							_UpperCamelCase   :						Union[str, Any]												=  ConditionalDetrImageProcessor(format='coco_panoptic'					)
							_UpperCamelCase   :						List[str]												=  image_processing(images=lowerCamelCase__					,annotations=lowerCamelCase__					,masks_path=lowerCamelCase__					,return_tensors='pt'					)

							# verify pixel values
							_UpperCamelCase   :						str												=  torch.Size([1, 3, 800, 1066]					)
							self.assertEqual(encoding['pixel_values'].shape					,lowerCamelCase__					)

							_UpperCamelCase   :						int												=  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]					)
							self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]					,lowerCamelCase__					,atol=1E-4					)					)

							# verify area
							_UpperCamelCase   :						Optional[int]												=  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']					,lowerCamelCase__					)					)
							# verify boxes
							_UpperCamelCase   :						List[Any]												=  torch.Size([6, 4]					)
							self.assertEqual(encoding['labels'][0]['boxes'].shape					,lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]					,lowerCamelCase__					,atol=1E-3					)					)
							# verify image_id
							_UpperCamelCase   :						Dict												=  torch.tensor([39769]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,lowerCamelCase__					)					)
							# verify is_crowd
							_UpperCamelCase   :						int												=  torch.tensor([0, 0, 0, 0, 0, 0]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,lowerCamelCase__					)					)
							# verify class_labels
							_UpperCamelCase   :						Optional[int]												=  torch.tensor([17, 17, 63, 75, 75, 93]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,lowerCamelCase__					)					)
							# verify masks
							_UpperCamelCase   :						Dict												=  822873
							self.assertEqual(encoding['labels'][0]['masks'].sum().item()					,lowerCamelCase__					)
							# verify orig_size
							_UpperCamelCase   :						int												=  torch.tensor([480, 640]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,lowerCamelCase__					)					)
							# verify size
							_UpperCamelCase   :						List[str]												=  torch.tensor([800, 1066]					)
							self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,lowerCamelCase__					)					)







'''simple docstring'''
def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						return base * power(UpperCAmelCase_    ,    (exponent - 1)				) if exponent else 1


if __name__ == "__main__":
					print('Raise base to the power of exponent using recursion...')
					snake_case_					:      int    			=    int(input('Enter the base: ').strip())
					snake_case_					:      Optional[int]    			=    int(input('Enter the exponent: ').strip())
					snake_case_					:      Optional[int]    			=    power(base, abs(exponent))
					if exponent < 0:  # power() does not properly deal w/ negative exponents
										snake_case_					:      List[Any]    			=    1 / result
					print(F"""{base} to the power of {exponent} is {result}""")


