




"""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 ConditionalDetrImageProcessor





class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 def __init__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Optional[int]=7   ,							_UpperCAmelCase	:	Union[str, Any]=3   ,							_UpperCAmelCase	:	List[str]=30   ,							_UpperCAmelCase	:	Optional[Any]=400   ,							_UpperCAmelCase	:	Optional[Any]=True   ,							_UpperCAmelCase	:	Dict=None   ,							_UpperCAmelCase	:	List[Any]=True   ,							_UpperCAmelCase	:	Optional[int]=[0.5, 0.5, 0.5]   ,							_UpperCAmelCase	:	Optional[int]=[0.5, 0.5, 0.5]   ,							_UpperCAmelCase	:	Dict=True   ,							_UpperCAmelCase	:	List[Any]=1 / 255   ,							_UpperCAmelCase	:	Any=True   ,							):
       # 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': 1_333}
       _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       lowerCAmelCase_							(			self	:	int		):
       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	:	Any   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	Tuple=False		):
       if not batched:
             _A        =       image_inputs[0]
             if isinstance(_UpperCAmelCase   ,							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(_UpperCAmelCase   ,							key=lambda _UpperCAmelCase		: item[0]		)[0]
             _A        =       max(_UpperCAmelCase   ,							key=lambda _UpperCAmelCase		: item[1]		)[1]

       return expected_height, expected_width





@require_torch
@require_vision
class     lowercase_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					str				  =     ConditionalDetrImageProcessor if is_vision_available() else None

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       ConditionalDetrImageProcessingTester(self		)

 @property
 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       return self.image_processor_tester.prepare_image_processor_dict()

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       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   ,							'size'		)		)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       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		)

       _A        =       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	:	List[Any]		):
       pass

 def       lowerCAmelCase_							(			self	:	int		):
       # 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=_UpperCAmelCase		)
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase   ,							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(_UpperCAmelCase		)

       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(_UpperCAmelCase   ,							batched=_UpperCAmelCase		)

       _A        =       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	:	Tuple		):
       # 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=_UpperCAmelCase   ,							numpify=_UpperCAmelCase		)
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase   ,							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(_UpperCAmelCase		)

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

       # Test batched
       _A        =       image_processing(_UpperCAmelCase   ,							return_tensors='pt'		).pixel_values

       _A       ,   _A        =       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	:	Optional[int]		):
       # 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=_UpperCAmelCase   ,							torchify=_UpperCAmelCase		)
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase   ,							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(_UpperCAmelCase		)

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

       # Test batched
       _A        =       image_processing(_UpperCAmelCase   ,							return_tensors='pt'		).pixel_values

       _A       ,   _A        =       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	:	Tuple		):
       # 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': 39_769, 'annotations': target}

       # encode them
       _A        =       ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'		)
       _A        =       image_processing(images=_UpperCAmelCase   ,							annotations=_UpperCAmelCase   ,							return_tensors='pt'		)

       # verify pixel values
       _A        =       torch.Size([1, 3, 800, 1_066]		)
       self.assertEqual(encoding['pixel_values'].shape   ,							_UpperCAmelCase		)

       _A        =       torch.tensor([0.2796, 0.3138, 0.3481]		)
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,							_UpperCAmelCase   ,							atol=1E-4		)		)

       # verify area
       _A        =       torch.tensor([5887.9600, 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
       _A        =       torch.Size([6, 4]		)
       self.assertEqual(encoding['labels'][0]['boxes'].shape   ,							_UpperCAmelCase		)
       _A        =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,							_UpperCAmelCase   ,							atol=1E-3		)		)
       # verify image_id
       _A        =       torch.tensor([39_769]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,							_UpperCAmelCase		)		)
       # verify is_crowd
       _A        =       torch.tensor([0, 0, 0, 0, 0, 0]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,							_UpperCAmelCase		)		)
       # verify class_labels
       _A        =       torch.tensor([75, 75, 63, 65, 17, 17]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,							_UpperCAmelCase		)		)
       # verify orig_size
       _A        =       torch.tensor([480, 640]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,							_UpperCAmelCase		)		)
       # verify size
       _A        =       torch.tensor([800, 1_066]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,							_UpperCAmelCase		)		)



 @slow
 def       lowerCAmelCase_							(			self	:	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': 39_769, 'segments_info': target}

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

       # encode them
       _A        =       ConditionalDetrImageProcessor(format='coco_panoptic'		)
       _A        =       image_processing(images=_UpperCAmelCase   ,							annotations=_UpperCAmelCase   ,							masks_path=_UpperCAmelCase   ,							return_tensors='pt'		)

       # verify pixel values
       _A        =       torch.Size([1, 3, 800, 1_066]		)
       self.assertEqual(encoding['pixel_values'].shape   ,							_UpperCAmelCase		)

       _A        =       torch.tensor([0.2796, 0.3138, 0.3481]		)
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,							_UpperCAmelCase   ,							atol=1E-4		)		)

       # verify area
       _A        =       torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5879.6562, 7634.1147]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,							_UpperCAmelCase		)		)
       # verify boxes
       _A        =       torch.Size([6, 4]		)
       self.assertEqual(encoding['labels'][0]['boxes'].shape   ,							_UpperCAmelCase		)
       _A        =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,							_UpperCAmelCase   ,							atol=1E-3		)		)
       # verify image_id
       _A        =       torch.tensor([39_769]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,							_UpperCAmelCase		)		)
       # verify is_crowd
       _A        =       torch.tensor([0, 0, 0, 0, 0, 0]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,							_UpperCAmelCase		)		)
       # verify class_labels
       _A        =       torch.tensor([17, 17, 63, 75, 75, 93]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,							_UpperCAmelCase		)		)
       # verify masks
       _A        =       822_873
       self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,							_UpperCAmelCase		)
       # verify orig_size
       _A        =       torch.tensor([480, 640]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,							_UpperCAmelCase		)		)
       # verify size
       _A        =       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 						_snake_case					( _snake_case							: int = 8							)   -> str:






      '''simple docstring'''


      _A        =       ascii_letters + digits + punctuation
      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


      i -= len(_snake_case							)
      _A        =       i // 3
      _A        =       i % 3
      # chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
      #     random_number(digits, i / 3) + random_characters(punctuation, i / 3)
      _A        =       (
          chars_incl
          + random(_snake_case    ,  quotient + remainder							)
          + random(_snake_case    ,  _snake_case							)
          + random(_snake_case    ,  _snake_case							)
      )
      _A        =       list(_snake_case							)
      shuffle(_snake_case							)
      return "".join(_snake_case							)

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






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int							)   -> str:






      '''simple docstring'''


      return "".join(secrets.choice(_snake_case							) for _ in range(_snake_case							)							)






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: Optional[int]							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: str							)   -> Dict:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: int							)   -> int:






      '''simple docstring'''


      pass  # Put your code here...






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: int = 8							)   -> bool:






      '''simple docstring'''


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

      _A        =       any(char in ascii_uppercase for char in password							)
      _A        =       any(char in ascii_lowercase for char in password							)
      _A        =       any(char in digits for char in password							)
      _A        =       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 						_snake_case					( )   -> Optional[Any]:






      '''simple docstring'''


      _A        =       int(input('Please indicate the max length of your password: '							).strip()							)
      _A        =       input(
          'Please indicate the characters that must be in your password: '							).strip()
      print('Password generated:'    ,  password_generator(_snake_case							)							)
      print(
          'Alternative Password generated:'    ,  alternative_password_generator(_snake_case    ,  _snake_case							)    ,  )
      print('[If you are thinking of using this passsword, You better save it.]'							)


if __name__ == "__main__":
       main()

