

'''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 ViTImageProcessor

class 			SCREAMING_SNAKE_CASE					(							unittest.TestCase					):






						'''simple docstring'''






						def __init__(					self :		str     ,					snake_case :		Any     ,					snake_case :		List[str]=7     ,					snake_case :		Optional[Any]=3     ,					snake_case :		int=18     ,					snake_case :		Optional[Any]=30     ,					snake_case :		Dict=400     ,					snake_case :		List[str]=True     ,					snake_case :		List[Any]=None     ,					snake_case :		List[Any]=True     ,					snake_case :		str=[0.5, 0.5, 0.5]     ,					snake_case :		Optional[int]=[0.5, 0.5, 0.5]     ,					):



										"""simple docstring"""





										_snake_case : Optional[int]												=		size if size is not None else {'height': 18, 'width': 18}
										_snake_case : List[Any]												=		parent
										_snake_case : Dict												=		batch_size
										_snake_case : Dict												=		num_channels
										_snake_case : Optional[Any]												=		image_size
										_snake_case : Optional[int]												=		min_resolution
										_snake_case : List[Any]												=		max_resolution
										_snake_case : List[Any]												=		do_resize
										_snake_case : int												=		size
										_snake_case : List[Any]												=		do_normalize
										_snake_case : Optional[Any]												=		image_mean
										_snake_case : Any												=		image_std




						def 	__UpperCAmelCase			(					self :		List[Any]   ):



										"""simple docstring"""





										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}



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






						'''simple docstring'''






						SCREAMING_SNAKE_CASE__ :				str    =				ViTImageProcessor if is_vision_available() else None




						def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



										"""simple docstring"""





										_snake_case : int												=		ViTImageProcessingTester(self   )




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



										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 	__UpperCAmelCase			(					self :		List[Any]   ):



										"""simple docstring"""





										_snake_case : Dict												=		self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(snake_case     ,					'image_mean'   )   )
										self.assertTrue(hasattr(snake_case     ,					'image_std'   )   )
										self.assertTrue(hasattr(snake_case     ,					'do_normalize'   )   )
										self.assertTrue(hasattr(snake_case     ,					'do_resize'   )   )
										self.assertTrue(hasattr(snake_case     ,					'size'   )   )




						def 	__UpperCAmelCase			(					self :		str   ):



										"""simple docstring"""





										_snake_case : Tuple												=		self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size     ,					{'height': 18, 'width': 18}   )

										_snake_case : int												=		self.image_processing_class.from_dict(self.image_processor_dict     ,					size=42   )
										self.assertEqual(image_processor.size     ,					{'height': 42, 'width': 42}   )




						def 	__UpperCAmelCase			(					self :		List[Any]   ):



										"""simple docstring"""





										pass




						def 	__UpperCAmelCase			(					self :		List[str]   ):



										"""simple docstring"""





										_snake_case : Tuple												=		self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										_snake_case : Union[str, Any]												=		prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=snake_case   )
										for image in image_inputs:
														self.assertIsInstance(snake_case     ,					Image.Image   )

										# Test not batched input
										_snake_case : Any												=		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.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,					)

										# Test batched
										_snake_case : List[str]												=		image_processing(snake_case     ,					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 :		str   ):



										"""simple docstring"""





										_snake_case : Union[str, Any]												=		self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										_snake_case : Any												=		prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=snake_case     ,					numpify=snake_case   )
										for image in image_inputs:
														self.assertIsInstance(snake_case     ,					np.ndarray   )

										# Test not batched input
										_snake_case : List[str]												=		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.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,					)

										# Test batched
										_snake_case : Optional[int]												=		image_processing(snake_case     ,					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 :		Tuple   ):



										"""simple docstring"""





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

										# Test not batched input
										_snake_case : str												=		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.size['height'],
										        self.image_processor_tester.size['width'],
										    )     ,					)

										# Test batched
										_snake_case : Tuple												=		image_processing(snake_case     ,					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'],
										    )     ,					)






'''simple docstring'''



def   lowerCamelCase__       (      a__			,      a__)		->    int:



    """simple docstring"""







    _snake_case : Tuple												=		0
    for ele in arr:
        total_sum += product_sum(a__			,      depth + 1) if isinstance(a__			,      a__) else ele
    return total_sum * depth

def   lowerCamelCase__       (      a__)		->    int:



    """simple docstring"""







    return product_sum(a__			,      1)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



