






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 LevitImageProcessor




class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=7       ,			a=3       ,			a=18       ,			a=30       ,			a=4_00       ,			a=True       ,			a=None       ,			a=True       ,			a=None       ,			a=True       ,			a=[0.5, 0.5, 0.5]       ,			a=[0.5, 0.5, 0.5]       ,			)       ->    str:
								snake_case_				=							size if size is not None else {'shortest_edge': 18}
								snake_case_				=							crop_size if crop_size is not None else {'height': 18, 'width': 18}
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							num_channels
								snake_case_				=							image_size
								snake_case_				=							min_resolution
								snake_case_				=							max_resolution
								snake_case_				=							do_resize
								snake_case_				=							size
								snake_case_				=							do_center_crop
								snake_case_				=							crop_size
								snake_case_				=							do_normalize
								snake_case_				=							image_mean
								snake_case_				=							image_std






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}






@require_torch
@require_vision
class  UpperCamelCase_      (			lowerCAmelCase_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= LevitImageProcessor if is_vision_available() else None




	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							LevitImageProcessingTester(self					)




	@property
	def 							_UpperCamelCase			(       self					)       ->    Dict:
								return self.image_processor_tester.prepare_image_processor_dict()




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							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       ,			'do_center_crop'					)					)
								self.assertTrue(hasattr(_snake_case       ,			'size'					)					)




	def 							_UpperCamelCase			(       self					)       ->    str:
								snake_case_				=							self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size       ,			{'shortest_edge': 18}					)
								self.assertEqual(image_processor.crop_size       ,			{'height': 18, 'width': 18}					)

								snake_case_				=							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 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								pass




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								# Initialize image_processing
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								snake_case_				=							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_				=							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
								snake_case_				=							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.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )       ,			)




	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								# Initialize image_processing
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								snake_case_				=							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_				=							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
								snake_case_				=							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.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )       ,			)






	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								# Initialize image_processing
								snake_case_				=							self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								snake_case_				=							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_				=							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
								snake_case_				=							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.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )       ,			)










from math import factorial
def        lowercase_							(A : int		,					A : int		,					A : float      ):
       if successes > trials:
              raise ValueError('successes must be lower or equal to trials'      )
       if trials < 0 or successes < 0:
              raise ValueError('the function is defined for non-negative integers'      )
       if not isinstance(A		,					A      ) or not isinstance(A		,					A      ):
              raise ValueError('the function is defined for non-negative integers'      )
       if not 0 < prob < 1:
              raise ValueError('prob has to be in range of 1 - 0'      )
       snake_case__							:	List[Any]   	=	(prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case__							:	List[str]   	=	float(factorial(A      )      )
       coefficient /= factorial(A      ) * factorial(trials - successes      )
       return probability * coefficient


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 print("Probability of 2 successes out of 4 trails")
 print("with probability of 0.75 is:", end=" ")
 print(binomial_distribution(2, 4, 0.75))






