





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import BlipImageProcessor





class 						_A( unittest.TestCase       ):


    """simple docstring"""


    def __init__(      self					, _A					, _A=7					, _A=3					, _A=18					, _A=30					, _A=400					, _A=True					, _A=None					, _A=True					, _A=False					, _A=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]					, _A=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]					, _A=True					, ):
         __A     :						str          =    size if size is not None else {'height': 20, 'width': 20}
         __A     :						Dict          =    parent
         __A     :						Optional[Any]          =    batch_size
         __A     :						Tuple          =    num_channels
         __A     :						Optional[int]          =    image_size
         __A     :						Optional[Any]          =    min_resolution
         __A     :						Tuple          =    max_resolution
         __A     :						List[str]          =    do_resize
         __A     :						Any          =    size
         __A     :						Any          =    do_normalize
         __A     :						int          =    image_mean
         __A     :						Union[str, Any]          =    image_std
         __A     :						List[str]          =    do_pad
         __A     :						List[str]          =    do_convert_rgb



    def  UpperCAmelCase_		(      self					):
         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_convert_rgb": self.do_convert_rgb,
             "do_pad": self.do_pad,
         }





    def  UpperCAmelCase_		(      self					, _A=False					, _A=False					, _A=False					):
         assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

         if equal_resolution:
              __A     :						Tuple          =    []
              for i in range(self.batch_size					):
                   image_inputs.append(
                       np.random.randint(
                           255					, size=(self.num_channels, self.max_resolution, self.max_resolution)					, dtype=np.uinta					)					)
         else:
              __A     :						str          =    []
              for i in range(self.batch_size					):
                   __A     :						Any          =    np.random.choice(np.arange(self.min_resolution					, self.max_resolution					)					, 2					)
                   image_inputs.append(np.random.randint(255					, size=(self.num_channels, width, height)					, dtype=np.uinta					)					)

         if not numpify and not torchify:
              # PIL expects the channel dimension as last dimension
              __A     :						List[str]          =    [Image.fromarray(np.moveaxis(_A					, 0					, -1					)					) for x in image_inputs]

         if torchify:
              __A     :						Optional[Any]          =    [torch.from_numpy(_A					) for x in image_inputs]

         return image_inputs





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


    """simple docstring"""


    UpperCamelCase   :  Tuple    =     BlipImageProcessor if is_vision_available() else None



    def  UpperCAmelCase_		(      self					):
         __A     :						Optional[int]          =    BlipImageProcessingTester(self					)



    @property
    def  UpperCAmelCase_		(      self					):
         return self.image_processor_tester.prepare_image_processor_dict()



    def  UpperCAmelCase_		(      self					):
         __A     :						int          =    self.image_processing_class(**self.image_processor_dict					)
         self.assertTrue(hasattr(_A					, 'do_resize'					)					)
         self.assertTrue(hasattr(_A					, 'size'					)					)
         self.assertTrue(hasattr(_A					, 'do_normalize'					)					)
         self.assertTrue(hasattr(_A					, 'image_mean'					)					)
         self.assertTrue(hasattr(_A					, 'image_std'					)					)
         self.assertTrue(hasattr(_A					, 'do_convert_rgb'					)					)



    def  UpperCAmelCase_		(      self					):
         pass



    def  UpperCAmelCase_		(      self					):
         # Initialize image_processor
         __A     :						str          =    self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         __A     :						List[Any]          =    self.image_processor_tester.prepare_inputs(equal_resolution=_A					)
         for image in image_inputs:
              self.assertIsInstance(_A					, Image.Image					)

         # Test not batched input
         __A     :						Any          =    image_processor(image_inputs[0]					, return_tensors='pt'					).pixel_values
         self.assertEqual(
             encoded_images.shape					, (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )					, )

         # Test batched
         __A     :						Optional[int]          =    image_processor(_A					, return_tensors='pt'					).pixel_values
         self.assertEqual(
             encoded_images.shape					, (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )					, )



    def  UpperCAmelCase_		(      self					):
         # Initialize image_processor
         __A     :						Optional[Any]          =    self.image_processing_class(**self.image_processor_dict					)
         # create random numpy tensors
         __A     :						str          =    self.image_processor_tester.prepare_inputs(equal_resolution=_A					, numpify=_A					)
         for image in image_inputs:
              self.assertIsInstance(_A					, np.ndarray					)

         # Test not batched input
         __A     :						List[str]          =    image_processor(image_inputs[0]					, return_tensors='pt'					).pixel_values
         self.assertEqual(
             encoded_images.shape					, (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )					, )

         # Test batched
         __A     :						str          =    image_processor(_A					, return_tensors='pt'					).pixel_values
         self.assertEqual(
             encoded_images.shape					, (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )					, )





    def  UpperCAmelCase_		(      self					):
         # Initialize image_processor
         __A     :						Tuple          =    self.image_processing_class(**self.image_processor_dict					)
         # create random PyTorch tensors
         __A     :						Union[str, Any]          =    self.image_processor_tester.prepare_inputs(equal_resolution=_A					, torchify=_A					)
         for image in image_inputs:
              self.assertIsInstance(_A					, torch.Tensor					)

         # Test not batched input
         __A     :						str          =    image_processor(image_inputs[0]					, return_tensors='pt'					).pixel_values
         self.assertEqual(
             encoded_images.shape					, (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )					, )

         # Test batched
         __A     :						Optional[Any]          =    image_processor(_A					, return_tensors='pt'					).pixel_values
         self.assertEqual(
             encoded_images.shape					, (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )					, )





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


    """simple docstring"""


    UpperCamelCase   :  str    =     BlipImageProcessor if is_vision_available() else None



    def  UpperCAmelCase_		(      self					):
         __A     :						int          =    BlipImageProcessingTester(self					, num_channels=4					)
         __A     :						Any          =    3



    @property
    def  UpperCAmelCase_		(      self					):
         return self.image_processor_tester.prepare_image_processor_dict()



    def  UpperCAmelCase_		(      self					):
         __A     :						List[str]          =    self.image_processing_class(**self.image_processor_dict					)
         self.assertTrue(hasattr(_A					, 'do_resize'					)					)
         self.assertTrue(hasattr(_A					, 'size'					)					)
         self.assertTrue(hasattr(_A					, 'do_normalize'					)					)
         self.assertTrue(hasattr(_A					, 'image_mean'					)					)
         self.assertTrue(hasattr(_A					, 'image_std'					)					)
         self.assertTrue(hasattr(_A					, 'do_convert_rgb'					)					)



    def  UpperCAmelCase_		(      self					):
         pass





    def  UpperCAmelCase_		(      self					):
         # Initialize image_processor
         __A     :						int          =    self.image_processing_class(**self.image_processor_dict					)
         # create random PIL images
         __A     :						Union[str, Any]          =    self.image_processor_tester.prepare_inputs(equal_resolution=_A					)
         for image in image_inputs:
              self.assertIsInstance(_A					, Image.Image					)

         # Test not batched input
         __A     :						Optional[Any]          =    image_processor(image_inputs[0]					, return_tensors='pt'					).pixel_values
         self.assertEqual(
             encoded_images.shape					, (
                 1,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )					, )

         # Test batched
         __A     :						Dict          =    image_processor(_A					, return_tensors='pt'					).pixel_values
         self.assertEqual(
             encoded_images.shape					, (
                 self.image_processor_tester.batch_size,
                 self.expected_encoded_image_num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )					, )











from __future__ import annotations

import math
import random







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				list[int]:
					__A     :						int          =    d * e - 1
					__A     :						List[str]          =    0
					__A     :						Optional[Any]          =    0
					while p == 0:
										__A     :						List[Any]          =    random.randint(2 ,						n - 1  )
										__A     :						Optional[int]          =    k
										while True:
															if t % 2 == 0:
																				__A     :						Tuple          =    t // 2
																				__A     :						List[str]          =    (g**t) % n
																				__A     :						List[Any]          =    math.gcd(x - 1 ,						a  )
																				if x > 1 and y > 1:
																									__A     :						List[str]          =    y
																									__A     :						str          =    n // y
																									break  # find the correct factors
															else:
																				break  # t is not divisible by 2, break and choose another g
					return sorted([p, q]  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




