






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 DetrImageProcessor

class 				lowerCAmelCase_	(		unittest.TestCase			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=7 ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=3_0 ,						_lowerCAmelCase=4_0_0 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=None ,						_lowerCAmelCase=True ,						_lowerCAmelCase=1 / 2_5_5 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=[0.5, 0.5, 0.5] ,						_lowerCAmelCase=[0.5, 0.5, 0.5] ,						_lowerCAmelCase=True ,						):
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											_lowercase							:   Dict 							=   size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
											_lowercase							:   Optional[Any] 							=   parent
											_lowercase							:   Optional[int] 							=   batch_size
											_lowercase							:   int 							=   num_channels
											_lowercase							:   Optional[int] 							=   min_resolution
											_lowercase							:   Union[str, Any] 							=   max_resolution
											_lowercase							:   List[Any] 							=   do_resize
											_lowercase							:   Dict 							=   size
											_lowercase							:   Dict 							=   do_rescale
											_lowercase							:   Dict 							=   rescale_factor
											_lowercase							:   Union[str, Any] 							=   do_normalize
											_lowercase							:   int 							=   image_mean
											_lowercase							:   str 							=   image_std
											_lowercase							:   List[Any] 							=   do_pad


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


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

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

											return expected_height, expected_width




@require_torch
@require_vision
class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       List[str]							       = DetrImageProcessor if is_vision_available() else None


				def 						__a					(					self      ):
											_lowercase							:   List[str] 							=   DetrImageProcessingTester(self      )


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


				def 						__a					(					self      ):
											_lowercase							:   Optional[int] 							=   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_rescale'      )      )
											self.assertTrue(hasattr(_lowerCAmelCase ,						'rescale_factor'      )      )
											self.assertTrue(hasattr(_lowerCAmelCase ,						'do_resize'      )      )
											self.assertTrue(hasattr(_lowerCAmelCase ,						'size'      )      )
											self.assertTrue(hasattr(_lowerCAmelCase ,						'do_pad'      )      )


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   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 ,						_lowerCAmelCase      )

											_lowercase							:   Optional[Any] 							=   self.image_processing_class.from_dict(
											    self.image_processor_dict ,						size=4_2 ,						max_size=8_4 ,						pad_and_return_pixel_mask=_lowerCAmelCase      )
											self.assertEqual(image_processor.size ,						{'shortest_edge': 4_2, 'longest_edge': 8_4}      )
											self.assertEqual(image_processor.do_pad ,						_lowerCAmelCase      )


				def 						__a					(					self      ):
											pass


				def 						__a					(					self      ):
											# Initialize image_processing
											_lowercase							:   Dict 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random PIL images
											_lowercase							:   List[Any] 							=   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							:   List[Any] 							=   image_processing(image_inputs[0] ,						return_tensors='pt'      ).pixel_values

											_lowercase							,							_lowercase							:   Tuple 							=   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							:   Optional[int] 							=   self.image_processor_tester.get_expected_values(_lowerCAmelCase ,						batched=_lowerCAmelCase      )

											_lowercase							:   int 							=   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      ):
											# Initialize image_processing
											_lowercase							:   Tuple 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random numpy tensors
											_lowercase							:   Tuple 							=   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							:   Optional[int] 							=   image_processing(image_inputs[0] ,						return_tensors='pt'      ).pixel_values

											_lowercase							,							_lowercase							:   str 							=   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							:   Optional[Any] 							=   image_processing(_lowerCAmelCase ,						return_tensors='pt'      ).pixel_values

											_lowercase							,							_lowercase							:   Optional[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 						__a					(					self      ):
											# Initialize image_processing
											_lowercase							:   str 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random PyTorch tensors
											_lowercase							:   List[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
											_lowercase							:   Any 							=   image_processing(image_inputs[0] ,						return_tensors='pt'      ).pixel_values

											_lowercase							,							_lowercase							:   str 							=   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							:   Dict 							=   image_processing(_lowerCAmelCase ,						return_tensors='pt'      ).pixel_values

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

											_lowercase							:   List[str] 							=   {'image_id': 3_9_7_6_9, 'annotations': target}

											# encode them
											_lowercase							:   List[Any] 							=   DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'      )
											_lowercase							:   Tuple 							=   image_processing(images=_lowerCAmelCase ,						annotations=_lowerCAmelCase ,						return_tensors='pt'      )

											# verify pixel values
											_lowercase							:   Tuple 							=   torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
											self.assertEqual(encoding['pixel_values'].shape ,						_lowerCAmelCase      )

											_lowercase							:   Optional[int] 							=   torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
											self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,						_lowerCAmelCase ,						atol=1E-4      )      )

											# verify area
											_lowercase							:   Optional[Any] 							=   torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,						_lowerCAmelCase      )      )
											# verify boxes
											_lowercase							:   Dict 							=   torch.Size([6, 4]      )
											self.assertEqual(encoding['labels'][0]['boxes'].shape ,						_lowerCAmelCase      )
											_lowercase							:   Optional[Any] 							=   torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,						_lowerCAmelCase ,						atol=1E-3      )      )
											# verify image_id
											_lowercase							:   List[Any] 							=   torch.tensor([3_9_7_6_9]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,						_lowerCAmelCase      )      )
											# verify is_crowd
											_lowercase							:   List[Any] 							=   torch.tensor([0, 0, 0, 0, 0, 0]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,						_lowerCAmelCase      )      )
											# verify class_labels
											_lowercase							:   int 							=   torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,						_lowerCAmelCase      )      )
											# verify orig_size
											_lowercase							:   List[str] 							=   torch.tensor([4_8_0, 6_4_0]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,						_lowerCAmelCase      )      )
											# verify size
											_lowercase							:   str 							=   torch.tensor([8_0_0, 1_0_6_6]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,						_lowerCAmelCase      )      )


				@slow
				def 						__a					(					self      ):
											# prepare image, target and masks_path
											_lowercase							:   List[str] 							=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
											with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' ,						'r'      ) as f:
																		_lowercase							:   int 							=   json.loads(f.read()      )

											_lowercase							:   List[str] 							=   {'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

											_lowercase							:   Optional[Any] 							=   pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'      )

											# encode them
											_lowercase							:   str 							=   DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'      )
											_lowercase							:   List[Any] 							=   image_processing(images=_lowerCAmelCase ,						annotations=_lowerCAmelCase ,						masks_path=_lowerCAmelCase ,						return_tensors='pt'      )

											# verify pixel values
											_lowercase							:   str 							=   torch.Size([1, 3, 8_0_0, 1_0_6_6]      )
											self.assertEqual(encoding['pixel_values'].shape ,						_lowerCAmelCase      )

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

											# verify area
											_lowercase							:   Dict 							=   torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,						_lowerCAmelCase      )      )
											# verify boxes
											_lowercase							:   int 							=   torch.Size([6, 4]      )
											self.assertEqual(encoding['labels'][0]['boxes'].shape ,						_lowerCAmelCase      )
											_lowercase							:   List[Any] 							=   torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,						_lowerCAmelCase ,						atol=1E-3      )      )
											# verify image_id
											_lowercase							:   List[str] 							=   torch.tensor([3_9_7_6_9]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,						_lowerCAmelCase      )      )
											# verify is_crowd
											_lowercase							:   Tuple 							=   torch.tensor([0, 0, 0, 0, 0, 0]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,						_lowerCAmelCase      )      )
											# verify class_labels
											_lowercase							:   Union[str, Any] 							=   torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,						_lowerCAmelCase      )      )
											# verify masks
											_lowercase							:   Optional[int] 							=   8_2_2_8_7_3
											self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,						_lowerCAmelCase      )
											# verify orig_size
											_lowercase							:   Any 							=   torch.tensor([4_8_0, 6_4_0]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,						_lowerCAmelCase      )      )
											# verify size
											_lowercase							:   Any 							=   torch.tensor([8_0_0, 1_0_6_6]      )
											self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,						_lowerCAmelCase      )      )







import string





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       ''
							for i in sequence:
														snake_case_     							=       ord(UpperCamelCase__ )
														if 65 <= extract <= 90:
																					output += chr(155 - extract )
														elif 97 <= extract <= 122:
																					output += chr(219 - extract )
														else:
																					output += i
							return output





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       string.ascii_letters
							snake_case_     							=       string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
							return "".join(
							    letters_reversed[letters.index(UpperCamelCase__ )] if c in letters else c for c in sequence )





def    __lowerCamelCase      (					):







							'''simple docstring'''






							from timeit import timeit

							print('Running performance benchmarks...' )
							snake_case_     							=       'from string import printable ; from __main__ import atbash, atbash_slow'
							print(F'''> atbash_slow(): {timeit("atbash_slow(printable)"  ,				setup=UpperCamelCase__ )} seconds''' )
							print(F'''>      atbash(): {timeit("atbash(printable)"  ,				setup=UpperCamelCase__ )} seconds''' )


if __name__ == "__main__":
					for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
										print(F'''{example} encrypted in atbash: {atbash(example)}''')
					benchmark()




