






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 DeformableDetrImageProcessor

class 		_snake_case  (  unittest.TestCase					):






			def __init__(       self		,					a		,					a=7		,					a=3		,					a=30		,					a=400		,					a=True		,					a=None		,					a=True		,					a=[0.5, 0.5, 0.5]		,					a=[0.5, 0.5, 0.5]		,					a=True		,					a=1 / 255		,					a=True		,					)      ->       str:
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						SCREAMING_SNAKE_CASE						= size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= min_resolution
						SCREAMING_SNAKE_CASE						= max_resolution
						SCREAMING_SNAKE_CASE						= do_resize
						SCREAMING_SNAKE_CASE						= size
						SCREAMING_SNAKE_CASE						= do_normalize
						SCREAMING_SNAKE_CASE						= image_mean
						SCREAMING_SNAKE_CASE						= image_std
						SCREAMING_SNAKE_CASE						= do_rescale
						SCREAMING_SNAKE_CASE						= rescale_factor
						SCREAMING_SNAKE_CASE						= do_pad






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=False)      ->       Tuple:
						if not batched:
									SCREAMING_SNAKE_CASE						= image_inputs[0]
									if isinstance(a		,					Image.Image):
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= image.size
									else:
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= image.shape[1], image.shape[2]
									if w < h:
												SCREAMING_SNAKE_CASE						= int(self.size['shortest_edge'] * h / w)
												SCREAMING_SNAKE_CASE						= self.size['shortest_edge']
									elif w > h:
												SCREAMING_SNAKE_CASE						= self.size['shortest_edge']
												SCREAMING_SNAKE_CASE						= int(self.size['shortest_edge'] * w / h)
									else:
												SCREAMING_SNAKE_CASE						= self.size['shortest_edge']
												SCREAMING_SNAKE_CASE						= self.size['shortest_edge']

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

						return expected_height, expected_width



@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    List[Any]			     =							DeformableDetrImageProcessor if is_vision_available() else None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= DeformableDetrImageProcessingTester(self)






			@property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						return self.image_processor_tester.prepare_image_processor_dict()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(a		,					'image_mean'))
						self.assertTrue(hasattr(a		,					'image_std'))
						self.assertTrue(hasattr(a		,					'do_normalize'))
						self.assertTrue(hasattr(a		,					'do_resize'))
						self.assertTrue(hasattr(a		,					'do_rescale'))
						self.assertTrue(hasattr(a		,					'do_pad'))
						self.assertTrue(hasattr(a		,					'size'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= 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		,					a)

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						pass






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					Image.Image)

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

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a)

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

						# Test batched
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a		,					batched=a)

						SCREAMING_SNAKE_CASE						= image_processing(a		,					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    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					numpify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					np.ndarray)

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

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a)

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

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values

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

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					torch.Tensor)

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

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a)

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

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values

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

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






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						# prepare image and target
						SCREAMING_SNAKE_CASE						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'		,					'r') as f:
									SCREAMING_SNAKE_CASE						= json.loads(f.read())

						SCREAMING_SNAKE_CASE						= {'image_id': 3_9769, 'annotations': target}

						# encode them
						SCREAMING_SNAKE_CASE						= DeformableDetrImageProcessor()
						SCREAMING_SNAKE_CASE						= image_processing(images=a		,					annotations=a		,					return_tensors='pt')

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

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

						# verify area
						SCREAMING_SNAKE_CASE						= 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']		,					a))
						# verify boxes
						SCREAMING_SNAKE_CASE						= torch.Size([6, 4])
						self.assertEqual(encoding['labels'][0]['boxes'].shape		,					a)
						SCREAMING_SNAKE_CASE						= torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,					a		,					atol=1E-3))
						# verify image_id
						SCREAMING_SNAKE_CASE						= torch.tensor([3_9769])
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,					a))
						# verify is_crowd
						SCREAMING_SNAKE_CASE						= torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,					a))
						# verify class_labels
						SCREAMING_SNAKE_CASE						= torch.tensor([75, 75, 63, 65, 17, 17])
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,					a))
						# verify orig_size
						SCREAMING_SNAKE_CASE						= torch.tensor([480, 640])
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,					a))
						# verify size
						SCREAMING_SNAKE_CASE						= torch.tensor([800, 1066])
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,					a))







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						# prepare image, target and masks_path
						SCREAMING_SNAKE_CASE						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		,					'r') as f:
									SCREAMING_SNAKE_CASE						= json.loads(f.read())

						SCREAMING_SNAKE_CASE						= {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

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

						# encode them
						SCREAMING_SNAKE_CASE						= DeformableDetrImageProcessor(format='coco_panoptic')
						SCREAMING_SNAKE_CASE						= image_processing(images=a		,					annotations=a		,					masks_path=a		,					return_tensors='pt')

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

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

						# verify area
						SCREAMING_SNAKE_CASE						= 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']		,					a))
						# verify boxes
						SCREAMING_SNAKE_CASE						= torch.Size([6, 4])
						self.assertEqual(encoding['labels'][0]['boxes'].shape		,					a)
						SCREAMING_SNAKE_CASE						= torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,					a		,					atol=1E-3))
						# verify image_id
						SCREAMING_SNAKE_CASE						= torch.tensor([3_9769])
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,					a))
						# verify is_crowd
						SCREAMING_SNAKE_CASE						= torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,					a))
						# verify class_labels
						SCREAMING_SNAKE_CASE						= torch.tensor([17, 17, 63, 75, 75, 93])
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,					a))
						# verify masks
						SCREAMING_SNAKE_CASE						= 82_2873
						self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,					a)
						# verify orig_size
						SCREAMING_SNAKE_CASE						= torch.tensor([480, 640])
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,					a))
						# verify size
						SCREAMING_SNAKE_CASE						= torch.tensor([800, 1066])
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,					a))












a_		:						Tuple             =    {
    'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
    'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
    'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '.-.', 'S': '...', 'T': '-', 'U': '..-',
    'V': '...-', 'W': '.--', 'X': '-..-', 'Y': '-.--', 'Z': '--..', '1': '.----',
    '2': '..---', '3': '...--', '4': '....-', '5': '.....', '6': '-....', '7': '--...',
    '8': '---..', '9': '----.', '0': '-----', '&': '.-...', '@': '.--.-.',
    ':': '---...', ',': '--..--', '.': '.-.-.-', '\'': '.----.', '"': '.-..-.',
    '?': '..--..', '/': '-..-.', '=': '-...-', '+': '.-.-.', '-': '-....-',
    '(': '-.--.', ')': '-.--.-', '!': '-.-.--', ' ': '/'
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
a_		:						List[Any]             =    {value: key for key, value in MORSE_CODE_DICT.items()}






def 				lowerCamelCase__				(_UpperCAmelCase):
			return " ".join(MORSE_CODE_DICT[char] for char in message.upper())






def 				lowerCamelCase__				(_UpperCAmelCase):
			return "".join(REVERSE_DICT[char] for char in message.split())






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= 'Morse code here!'
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= encrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= decrypt(_UpperCAmelCase)
			print(_UpperCAmelCase)


if __name__ == "__main__":
				main()




