



'''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 DetaImageProcessor





class _UpperCamelCase		(     unittest.TestCase		):





				'''simple docstring'''





				def __init__(							self  :  List[Any]  ,       _lowerCAmelCase  :  Optional[int]  ,       _lowerCAmelCase  :  int=7  ,       _lowerCAmelCase  :  List[Any]=3  ,       _lowerCAmelCase  :  Dict=3_0  ,       _lowerCAmelCase  :  int=4_0_0  ,       _lowerCAmelCase  :  int=True  ,       _lowerCAmelCase  :  Optional[Any]=None  ,       _lowerCAmelCase  :  Optional[int]=True  ,       _lowerCAmelCase  :  Optional[Any]=[0.5, 0.5, 0.5]  ,       _lowerCAmelCase  :  Dict=[0.5, 0.5, 0.5]  ,       _lowerCAmelCase  :  Optional[int]=True  ,       _lowerCAmelCase  :  List[Any]=1 / 2_5_5  ,       _lowerCAmelCase  :  Union[str, Any]=True  ,       ):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''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 				__lowerCamelCase     (							self  :  Dict  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  List[Any]=False):







											'''simple docstring'''

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

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

											return expected_height, expected_width





@require_torch
@require_vision
class _UpperCamelCase		(     A      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    DetaImageProcessor if is_vision_available() else None

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											__lowercase       	=DetaImageProcessingTester(self)

				@property
				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											return self.image_processor_tester.prepare_image_processor_dict()

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											__lowercase       	=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  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=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)

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											pass

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







											'''simple docstring'''

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

											__lowercase	,				__lowercase       	=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       	=self.image_processor_tester.get_expected_values(_lowerCAmelCase  ,       batched=_lowerCAmelCase)

											__lowercase       	=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  :  Union[str, Any]):







											'''simple docstring'''

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

											__lowercase	,				__lowercase       	=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       	=image_processing(_lowerCAmelCase  ,       return_tensors='pt').pixel_values

											__lowercase	,				__lowercase       	=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  :  str):







											'''simple docstring'''

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

											__lowercase	,				__lowercase       	=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       	=image_processing(_lowerCAmelCase  ,       return_tensors='pt').pixel_values

											__lowercase	,				__lowercase       	=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  :  Tuple):







											'''simple docstring'''

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

											__lowercase       	={'image_id': 3_9_7_6_9, 'annotations': target}

											# encode them
											__lowercase       	=DetaImageProcessor()
											__lowercase       	=image_processing(images=_lowerCAmelCase  ,       annotations=_lowerCAmelCase  ,       return_tensors='pt')

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

											__lowercase       	=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
											__lowercase       	=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
											__lowercase       	=torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape  ,       _lowerCAmelCase)
											__lowercase       	=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
											__lowercase       	=torch.tensor([3_9_7_6_9])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,       _lowerCAmelCase))
											# verify is_crowd
											__lowercase       	=torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,       _lowerCAmelCase))
											# verify class_labels
											__lowercase       	=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       	=torch.tensor([4_8_0, 6_4_0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,       _lowerCAmelCase))
											# verify size
											__lowercase       	=torch.tensor([8_0_0, 1_0_6_6])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,       _lowerCAmelCase))

				@slow
				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''simple docstring'''

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

											__lowercase       	={'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

											__lowercase       	=pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

											# encode them
											__lowercase       	=DetaImageProcessor(format='coco_panoptic')
											__lowercase       	=image_processing(images=_lowerCAmelCase  ,       annotations=_lowerCAmelCase  ,       masks_path=_lowerCAmelCase  ,       return_tensors='pt')

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

											__lowercase       	=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
											__lowercase       	=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
											__lowercase       	=torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape  ,       _lowerCAmelCase)
											__lowercase       	=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
											__lowercase       	=torch.tensor([3_9_7_6_9])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']  ,       _lowerCAmelCase))
											# verify is_crowd
											__lowercase       	=torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']  ,       _lowerCAmelCase))
											# verify class_labels
											__lowercase       	=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       	=8_2_2_8_7_3
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()  ,       _lowerCAmelCase)
											# verify orig_size
											__lowercase       	=torch.tensor([4_8_0, 6_4_0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']  ,       _lowerCAmelCase))
											# verify size
											__lowercase       	=torch.tensor([8_0_0, 1_0_6_6])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']  ,       _lowerCAmelCase))








'''simple docstring'''



import string



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   ):



							"""simple docstring"""



							from timeit import timeit

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


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



