



'''simple docstring'''



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, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import MobileViTImageProcessor





class _UpperCamelCase		(     unittest.TestCase		):





				'''simple docstring'''





				def __init__(							self  :  Tuple  ,       _lowerCAmelCase  :  Dict  ,       _lowerCAmelCase  :  str=7  ,       _lowerCAmelCase  :  Any=3  ,       _lowerCAmelCase  :  List[Any]=1_8  ,       _lowerCAmelCase  :  Any=3_0  ,       _lowerCAmelCase  :  Tuple=4_0_0  ,       _lowerCAmelCase  :  Union[str, Any]=True  ,       _lowerCAmelCase  :  Optional[int]=None  ,       _lowerCAmelCase  :  Optional[Any]=True  ,       _lowerCAmelCase  :  Tuple=None  ,       _lowerCAmelCase  :  str=True  ,       ):







											'''simple docstring'''

											__lowercase       	=size if size is not None else {'shortest_edge': 2_0}
											__lowercase       	=crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
											__lowercase       	=parent
											__lowercase       	=batch_size
											__lowercase       	=num_channels
											__lowercase       	=image_size
											__lowercase       	=min_resolution
											__lowercase       	=max_resolution
											__lowercase       	=do_resize
											__lowercase       	=size
											__lowercase       	=do_center_crop
											__lowercase       	=crop_size
											__lowercase       	=do_flip_channel_order

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_flip_channel_order": self.do_flip_channel_order,
											}





@require_torch
@require_vision
class _UpperCamelCase		(     A      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    MobileViTImageProcessor if is_vision_available() else None

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=MobileViTImageProcessingTester(self)

				@property
				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											return self.image_processor_tester.prepare_image_processor_dict()

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=self.image_processing_class(**self.image_processor_dict)
											self.assertTrue(hasattr(_lowerCAmelCase  ,       'do_resize'))
											self.assertTrue(hasattr(_lowerCAmelCase  ,       'size'))
											self.assertTrue(hasattr(_lowerCAmelCase  ,       'do_center_crop'))
											self.assertTrue(hasattr(_lowerCAmelCase  ,       'center_crop'))
											self.assertTrue(hasattr(_lowerCAmelCase  ,       'do_flip_channel_order'))

				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											__lowercase       	=self.image_processing_class.from_dict(self.image_processor_dict)
											self.assertEqual(image_processor.size  ,       {'shortest_edge': 2_0})
											self.assertEqual(image_processor.crop_size  ,       {'height': 1_8, 'width': 1_8})

											__lowercase       	=self.image_processing_class.from_dict(self.image_processor_dict  ,       size=4_2  ,       crop_size=8_4)
											self.assertEqual(image_processor.size  ,       {'shortest_edge': 4_2})
											self.assertEqual(image_processor.crop_size  ,       {'height': 8_4, 'width': 8_4})

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											pass

				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

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

											# Test not batched input
											__lowercase       	=image_processing(image_inputs[0]  ,       return_tensors='pt').pixel_values
											self.assertEqual(
											    encoded_images.shape  ,       (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )  ,       )

											# Test batched
											__lowercase       	=image_processing(_lowerCAmelCase  ,       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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )  ,       )

				def 				__lowerCamelCase     (							self  :  List[Any]):







											'''simple docstring'''

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

											# Test not batched input
											__lowercase       	=image_processing(image_inputs[0]  ,       return_tensors='pt').pixel_values
											self.assertEqual(
											    encoded_images.shape  ,       (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )  ,       )

											# Test batched
											__lowercase       	=image_processing(_lowerCAmelCase  ,       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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )  ,       )

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

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

											# Test not batched input
											__lowercase       	=image_processing(image_inputs[0]  ,       return_tensors='pt').pixel_values
											self.assertEqual(
											    encoded_images.shape  ,       (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )  ,       )

											# Test batched
											__lowercase       	=image_processing(_lowerCAmelCase  ,       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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    )  ,       )








'''simple docstring'''



from functools import lru_cache



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=2
							__lowercase       	=set()
							while i * i <= n:
														if n % i:
																					i += 1
														else:
																					n //= i
																					factors.add(_lowerCAmelCase					)
							if n > 1:
														factors.add(_lowerCAmelCase					)
							return factors



@lru_cache
def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							return len(unique_prime_factors(_lowerCAmelCase					)					)



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							return len(set(_lowerCAmelCase					)					) in (0, 1)



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=2

							while True:
														# Increment each value of a generated range
														__lowercase       	=[base + i for i in range(_lowerCAmelCase					)]

														# Run elements through out unique_prime_factors function
														# Append our target number to the end.
														__lowercase       	=[upf_len(_lowerCAmelCase					) for x in group]
														checker.append(_lowerCAmelCase					)

														# If all numbers in the list are equal, return the group variable.
														if equality(_lowerCAmelCase					):
																					return group

														# Increment our base variable by 1
														base += 1



def   _A       (   _lowerCAmelCase = 4					):



							"""simple docstring"""



							__lowercase       	=run(_lowerCAmelCase					)
							return results[0] if len(_lowerCAmelCase					) else None


if __name__ == "__main__":
		print(solution())



