


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   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_=7       ,  lowercase_=3       ,  lowercase_=30       ,  lowercase_=400       ,  lowercase_=True       ,  lowercase_=None       ,  lowercase_=True       ,  lowercase_=[0.5, 0.5, 0.5]       ,  lowercase_=[0.5, 0.5, 0.5]       ,  lowercase_=True       ,  lowercase_=1 / 255       ,  lowercase_=True       ,  )  -> List[str]:
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											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)  -> List[Any]:
											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       ,  lowercase_       ,  lowercase_=False)  -> int:
											if not batched:
																		a__      =image_inputs[0]
																		if isinstance(lowercase_       ,  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(lowercase_       ,  key=lambda lowercase_: item[0])[0]
																		a__      =max(lowercase_       ,  key=lambda lowercase_: item[1])[1]

											return expected_height, expected_width







@require_torch
@require_vision
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =YolosImageProcessor if is_vision_available() else None





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =YolosImageProcessingTester(self)





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											return self.image_processor_tester.prepare_image_processor_dict()





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(lowercase_       ,  'image_mean'))
											self.assertTrue(hasattr(lowercase_       ,  'image_std'))
											self.assertTrue(hasattr(lowercase_       ,  'do_normalize'))
											self.assertTrue(hasattr(lowercase_       ,  'do_resize'))
											self.assertTrue(hasattr(lowercase_       ,  'size'))





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											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       ,  lowercase_)

											a__      =self.image_processing_class.from_dict(
											    self.image_processor_dict       ,  size=42       ,  max_size=84       ,  pad_and_return_pixel_mask=lowercase_)
											self.assertEqual(image_processor.size       ,  {'shortest_edge': 42, 'longest_edge': 84})
											self.assertEqual(image_processor.do_pad       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											pass





				def 						__UpperCamelCase    (				self)  -> Any:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PIL images
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  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(lowercase_)

											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(lowercase_       ,  batched=lowercase_)

											a__      =image_processing(lowercase_       ,  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)  -> Any:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random numpy tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  numpify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  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(lowercase_)

											self.assertEqual(
											    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

											# Test batched
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_       ,  batched=lowercase_)

											self.assertEqual(
											    encoded_images.shape       ,  (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )       ,  )





				def 						__UpperCamelCase    (				self)  -> Dict:
											# Initialize image_processing
											a__      =self.image_processing_class(**self.image_processor_dict)
											# create random PyTorch tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  torchify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  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(lowercase_)

											self.assertEqual(
											    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

											# Test batched
											a__      =image_processing(lowercase_       ,  return_tensors='pt').pixel_values

											a__    ,     a__      =self.image_processor_tester.get_expected_values(lowercase_       ,  batched=lowercase_)

											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[Any]:
											# Initialize image_processings
											a__      =self.image_processing_class(**self.image_processor_dict)
											a__      =self.image_processing_class(do_resize=lowercase_       ,  do_normalize=lowercase_       ,  do_rescale=lowercase_)
											# create random PyTorch tensors
											a__      =prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=lowercase_       ,  torchify=lowercase_)
											for image in image_inputs:
																		self.assertIsInstance(lowercase_       ,  torch.Tensor)

											# Test whether the method "pad" and calling the image processor return the same tensors
											a__      =image_processing_a.pad(lowercase_       ,  return_tensors='pt')
											a__      =image_processing_a(lowercase_       ,  return_tensors='pt')

											self.assertTrue(
											    torch.allclose(encoded_images_with_method['pixel_values']       ,  encoded_images['pixel_values']       ,  atol=1e-4))





				@slow
				def 						__UpperCamelCase    (				self)  -> int:
											# prepare image and target
											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=lowercase_       ,  annotations=lowercase_       ,  return_tensors='pt')

											# verify pixel values
											a__      =torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['pixel_values'].shape       ,  lowercase_)

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

											# verify area
											a__      =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']       ,  lowercase_))
											# verify boxes
											a__      =torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  lowercase_)
											a__      =torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  lowercase_       ,  atol=1e-3))
											# verify image_id
											a__      =torch.tensor([39769])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  lowercase_))
											# verify is_crowd
											a__      =torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  lowercase_))
											# verify class_labels
											a__      =torch.tensor([75, 75, 63, 65, 17, 17])
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  lowercase_))
											# verify orig_size
											a__      =torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  lowercase_))
											# verify size
											a__      =torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  lowercase_))





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											# prepare image, target and masks_path
											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=lowercase_       ,  annotations=lowercase_       ,  masks_path=lowercase_       ,  return_tensors='pt')

											# verify pixel values
											a__      =torch.Size([1, 3, 800, 1066])
											self.assertEqual(encoding['pixel_values'].shape       ,  lowercase_)

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

											# verify area
											a__      =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']       ,  lowercase_))
											# verify boxes
											a__      =torch.Size([6, 4])
											self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  lowercase_)
											a__      =torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
											self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  lowercase_       ,  atol=1e-3))
											# verify image_id
											a__      =torch.tensor([39769])
											self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  lowercase_))
											# verify is_crowd
											a__      =torch.tensor([0, 0, 0, 0, 0, 0])
											self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  lowercase_))
											# verify class_labels
											a__      =torch.tensor([17, 17, 63, 75, 75, 93])
											self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  lowercase_))
											# verify masks
											a__      =822873
											self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,  lowercase_)
											# verify orig_size
											a__      =torch.tensor([480, 640])
											self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  lowercase_))
											# verify size
											a__      =torch.tensor([800, 1066])
											self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  lowercase_))





_lowerCAmelCase:						List[str]   				=       'ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def   _lowercase(					):
							a__      =input('Enter message: '     )
							a__      =input('Enter key [alphanumeric]: '     )
							a__      =input('Encrypt/Decrypt [e/d]: '     )

							if mode.lower().startswith('e'     ):
														a__      ='encrypt'
														a__      =encrypt_message(__a    ,     __a     )
							elif mode.lower().startswith('d'     ):
														a__      ='decrypt'
														a__      =decrypt_message(__a    ,     __a     )

							print(f"""\n{mode.title()}ed message:"""     )
							print(__a     )






def   _lowercase(					__a				:						str    ,     __a				:						str     ):
							return translate_message(__a    ,     __a    ,     'encrypt'     )






def   _lowercase(					__a				:						str    ,     __a				:						str     ):
							return translate_message(__a    ,     __a    ,     'decrypt'     )






def   _lowercase(					__a				:						str    ,     __a				:						str    ,     __a				:						str     ):
							a__      =[]
							a__      =0
							a__      =key.upper()

							for symbol in message:
														a__      =LETTERS.find(symbol.upper()     )
														if num != -1:
																					if mode == "encrypt":
																												num += LETTERS.find(key[key_index]     )
																					elif mode == "decrypt":
																												num -= LETTERS.find(key[key_index]     )

																					num %= len(__a     )

																					if symbol.isupper():
																												translated.append(LETTERS[num]     )
																					elif symbol.islower():
																												translated.append(LETTERS[num].lower()     )

																					key_index += 1
																					if key_index == len(__a     ):
																												a__      =0
														else:
																					translated.append(__a     )
							return "".join(__a     )


if __name__ == "__main__":
	main()

