


"""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 DeformableDetrImageProcessor





class      __A     (unittest.TestCase):





							'''simple docstring'''

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



														"""simple docstring"""



														snake_case_           =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		num_channels
														snake_case_           =		min_resolution
														snake_case_           =		max_resolution
														snake_case_           =		do_resize
														snake_case_           =		size
														snake_case_           =		do_normalize
														snake_case_           =		image_mean
														snake_case_           =		image_std
														snake_case_           =		do_rescale
														snake_case_           =		rescale_factor
														snake_case_           =		do_pad



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->int:



														"""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							:      str		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Any=False       )					->List[str]:



														"""simple docstring"""



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

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

														return expected_height, expected_width





@require_torch
@require_vision
class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		str							      =    DeformableDetrImageProcessor if is_vision_available() else None



							def 		lowerCAmelCase       (				self							:      Dict       )					->Dict:



														"""simple docstring"""



														snake_case_           =		DeformableDetrImageProcessingTester(self       )



							@property
							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->List[str]:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



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



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """image_mean"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """image_std"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_normalize"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_resize"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_rescale"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_pad"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """size"""       )       )



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Any:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict       )
														self.assertEqual(image_processor.size		,       {"""shortest_edge""": 18, """longest_edge""": 1_333}       )
														self.assertEqual(image_processor.do_pad		,       UpperCAmelCase_       )

														snake_case_           =		self.image_processing_class.from_dict(
														    self.image_processor_dict		,       size=42		,       max_size=84		,       pad_and_return_pixel_mask=UpperCAmelCase_       )
														self.assertEqual(image_processor.size		,       {"""shortest_edge""": 42, """longest_edge""": 84}       )
														self.assertEqual(image_processor.do_pad		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Dict       )					->Dict:



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any       )					->int:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														# create random PIL images
														snake_case_           =		prepare_image_inputs(self.image_processor_tester		,       equal_resolution=UpperCAmelCase_       )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase_		,       Image.Image       )

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

														snake_case_       ,						snake_case_           =		self.image_processor_tester.get_expected_values(UpperCAmelCase_       )

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

														# Test batched
														snake_case_       ,						snake_case_           =		self.image_processor_tester.get_expected_values(UpperCAmelCase_		,       batched=UpperCAmelCase_       )

														snake_case_           =		image_processing(UpperCAmelCase_		,       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							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														# create random numpy tensors
														snake_case_           =		prepare_image_inputs(self.image_processor_tester		,       equal_resolution=UpperCAmelCase_		,       numpify=UpperCAmelCase_       )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase_		,       np.ndarray       )

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

														snake_case_       ,						snake_case_           =		self.image_processor_tester.get_expected_values(UpperCAmelCase_       )

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

														# Test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       ).pixel_values

														snake_case_       ,						snake_case_           =		self.image_processor_tester.get_expected_values(UpperCAmelCase_		,       batched=UpperCAmelCase_       )

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



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														# create random PyTorch tensors
														snake_case_           =		prepare_image_inputs(self.image_processor_tester		,       equal_resolution=UpperCAmelCase_		,       torchify=UpperCAmelCase_       )
														for image in image_inputs:
																					self.assertIsInstance(UpperCAmelCase_		,       torch.Tensor       )

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

														snake_case_       ,						snake_case_           =		self.image_processor_tester.get_expected_values(UpperCAmelCase_       )

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

														# Test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       ).pixel_values

														snake_case_       ,						snake_case_           =		self.image_processor_tester.get_expected_values(UpperCAmelCase_		,       batched=UpperCAmelCase_       )

														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							:      Union[str, Any]       )					->Optional[int]:



														"""simple docstring"""



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

														snake_case_           =		{"""image_id""": 39_769, """annotations""": target}

														# encode them
														snake_case_           =		DeformableDetrImageProcessor()
														snake_case_           =		image_processing(images=UpperCAmelCase_		,       annotations=UpperCAmelCase_		,       return_tensors="""pt"""       )

														# verify pixel values
														snake_case_           =		torch.Size([1, 3, 800, 1_066]       )
														self.assertEqual(encoding["""pixel_values"""].shape		,       UpperCAmelCase_       )

														snake_case_           =		torch.tensor([0.2_796, 0.3_138, 0.3_481]       )
														self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]		,       UpperCAmelCase_		,       atol=1E-4       )       )

														# verify area
														snake_case_           =		torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]		,       UpperCAmelCase_       )       )
														# verify boxes
														snake_case_           =		torch.Size([6, 4]       )
														self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape		,       UpperCAmelCase_       )
														snake_case_           =		torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]		,       UpperCAmelCase_		,       atol=1E-3       )       )
														# verify image_id
														snake_case_           =		torch.tensor([39_769]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]		,       UpperCAmelCase_       )       )
														# verify is_crowd
														snake_case_           =		torch.tensor([0, 0, 0, 0, 0, 0]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]		,       UpperCAmelCase_       )       )
														# verify class_labels
														snake_case_           =		torch.tensor([75, 75, 63, 65, 17, 17]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]		,       UpperCAmelCase_       )       )
														# verify orig_size
														snake_case_           =		torch.tensor([480, 640]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]		,       UpperCAmelCase_       )       )
														# verify size
														snake_case_           =		torch.tensor([800, 1_066]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]		,       UpperCAmelCase_       )       )



							@slow
							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->int:



														"""simple docstring"""



														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:
																					snake_case_           =		json.loads(f.read()       )

														snake_case_           =		{"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}

														snake_case_           =		pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""       )

														# encode them
														snake_case_           =		DeformableDetrImageProcessor(format="""coco_panoptic"""       )
														snake_case_           =		image_processing(images=UpperCAmelCase_		,       annotations=UpperCAmelCase_		,       masks_path=UpperCAmelCase_		,       return_tensors="""pt"""       )

														# verify pixel values
														snake_case_           =		torch.Size([1, 3, 800, 1_066]       )
														self.assertEqual(encoding["""pixel_values"""].shape		,       UpperCAmelCase_       )

														snake_case_           =		torch.tensor([0.2_796, 0.3_138, 0.3_481]       )
														self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]		,       UpperCAmelCase_		,       atol=1E-4       )       )

														# verify area
														snake_case_           =		torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]		,       UpperCAmelCase_       )       )
														# verify boxes
														snake_case_           =		torch.Size([6, 4]       )
														self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape		,       UpperCAmelCase_       )
														snake_case_           =		torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]		,       UpperCAmelCase_		,       atol=1E-3       )       )
														# verify image_id
														snake_case_           =		torch.tensor([39_769]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]		,       UpperCAmelCase_       )       )
														# verify is_crowd
														snake_case_           =		torch.tensor([0, 0, 0, 0, 0, 0]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]		,       UpperCAmelCase_       )       )
														# verify class_labels
														snake_case_           =		torch.tensor([17, 17, 63, 75, 75, 93]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]		,       UpperCAmelCase_       )       )
														# verify masks
														snake_case_           =		822_873
														self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()		,       UpperCAmelCase_       )
														# verify orig_size
														snake_case_           =		torch.tensor([480, 640]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]		,       UpperCAmelCase_       )       )
														# verify size
														snake_case_           =		torch.tensor([800, 1_066]       )
														self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]		,       UpperCAmelCase_       )       )







"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation







def 						_a				(      _SCREAMING_SNAKE_CASE = 8				)    ->				str:
							snake_case_           =		ascii_letters + digits + punctuation
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							# Password Generator = full boot with random_number, random_letters, and
							# random_character FUNCTIONS
							# Put your code here...
							i -= len(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		i // 3
							snake_case_           =		i % 3
							# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
							#     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
							snake_case_           =		(
							    chars_incl
							    + random(_SCREAMING_SNAKE_CASE				, quotient + remainder				)
							    + random(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							    + random(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							)
							snake_case_           =		list(_SCREAMING_SNAKE_CASE				)
							shuffle(_SCREAMING_SNAKE_CASE				)
							return "".join(_SCREAMING_SNAKE_CASE				)

							# random is a generalised function for letters, characters and numbers







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				str:
							return "".join(secrets.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				List[str]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[int]:
							pass  # Put your code here...







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE = 8				)    ->				bool:

							if len(_SCREAMING_SNAKE_CASE				) < min_length:
														# Your Password must be at least 8 characters long
														return False

							snake_case_           =		any(char in ascii_uppercase for char in password				)
							snake_case_           =		any(char in ascii_lowercase for char in password				)
							snake_case_           =		any(char in digits for char in password				)
							snake_case_           =		any(char in punctuation for char in password				)

							return upper and lower and num and spec_char
							# Passwords should contain UPPERCASE, lowerase
							# numbers, and special characters







def 						_a				(      )    ->				str:
							snake_case_           =		int(input("""Please indicate the max length of your password: """				).strip()				)
							snake_case_           =		input(
							    """Please indicate the characters that must be in your password: """				).strip()
							print("""Password generated:"""				, password_generator(_SCREAMING_SNAKE_CASE				)				)
							print(
							    """Alternative Password generated:"""				, alternative_password_generator(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)				, )
							print("""[If you are thinking of using this passsword, You better save it.]"""				)


if __name__ == "__main__":
				main()



