def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       float:



			"""simple docstring"""







			if digit_amount > 0:
						return round(number - int(UpperCAmelCase       ),		UpperCAmelCase       )
			return number - int(UpperCAmelCase       )


if __name__ == "__main__":
						print(decimal_isolate(1.53, 0))
						print(decimal_isolate(35.345, 1))
						print(decimal_isolate(35.345, 2))
						print(decimal_isolate(35.345, 3))
						print(decimal_isolate(-14.789, 3))
						print(decimal_isolate(0, 2))
						print(decimal_isolate(-14.123, 1))
						print(decimal_isolate(-14.123, 2))
						print(decimal_isolate(-14.123, 3))






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      __lowercase					(    unittest.TestCase    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int=7							,							__lowerCamelCase				:      Dict=3							,							__lowerCamelCase				:      Optional[int]=18							,							__lowerCamelCase				:      List[Any]=30							,							__lowerCamelCase				:      Union[str, Any]=4_00							,							__lowerCamelCase				:      Optional[int]=True							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      Dict=True							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      Tuple=True							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		size if size is not None else {'''shortest_edge''': 20}
									lowercase							=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									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        __a		(	self				:      Optional[int]   )							->		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      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     MobileViTImageProcessor if is_vision_available() else None

						def        __a		(	self				:      Dict   )							->		Dict:


									'''simple docstring'''

									lowercase							=		MobileViTImageProcessingTester(self   )

						@property
						def        __a		(	self				:      Tuple   )							->		Dict:


									'''simple docstring'''

									return self.image_processor_tester.prepare_image_processor_dict()

						def        __a		(	self				:      Dict   )							->		List[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        __a		(	self				:      List[str]   )							->		str:


									'''simple docstring'''

									lowercase							=		self.image_processing_class.from_dict(self.image_processor_dict   )
									self.assertEqual(image_processor.size							,							{'''shortest_edge''': 20}   )
									self.assertEqual(image_processor.crop_size							,							{'''height''': 18, '''width''': 18}   )

									lowercase							=		self.image_processing_class.from_dict(self.image_processor_dict							,							size=42							,							crop_size=84   )
									self.assertEqual(image_processor.size							,							{'''shortest_edge''': 42}   )
									self.assertEqual(image_processor.crop_size							,							{'''height''': 84, '''width''': 84}   )

						def        __a		(	self				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Any   )							->		List[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        __a		(	self				:      int   )							->		str:


									'''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        __a		(	self				:      Dict   )							->		Any:


									'''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'''],
									    )							,							)





