


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 GLPNImageProcessor
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def __init__(					self:Union[str, Any]     ,		_a:Optional[int]     ,		_a:Tuple=7     ,		_a:Dict=3     ,		_a:Optional[Any]=18     ,		_a:Optional[Any]=30     ,		_a:Union[str, Any]=4_00     ,		_a:str=True     ,		_a:Optional[Any]=32     ,		_a:Tuple=True     ,		):
       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_divisor
       snake_case__						= do_rescale







     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }




@require_torch
@require_vision
class 		__magic_name__						(snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Optional[Any]	   =       GLPNImageProcessor if is_vision_available() else None

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= GLPNImageProcessingTester(self   )

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return self.image_processor_tester.prepare_image_processor_dict()

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(_a     ,		'''do_resize'''   )   )
       self.assertTrue(hasattr(_a     ,		'''size_divisor'''   )   )
       self.assertTrue(hasattr(_a     ,		'''resample'''   )   )
       self.assertTrue(hasattr(_a     ,		'''do_rescale'''   )   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       pass

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # 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=_a   )
       for image in image_inputs:
         self.assertIsInstance(_a     ,		Image.Image   )

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       snake_case__						= image_processing(image_inputs[0]     ,		return_tensors='''pt'''   ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       # 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=_a     ,		numpify=_a   )
       for image in image_inputs:
         self.assertIsInstance(_a     ,		np.ndarray   )

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       snake_case__						= image_processing(image_inputs[0]     ,		return_tensors='''pt'''   ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       # 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=_a     ,		torchify=_a   )
       for image in image_inputs:
         self.assertIsInstance(_a     ,		torch.Tensor   )

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       snake_case__						= image_processing(image_inputs[0]     ,		return_tensors='''pt'''   ).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )





from __future__ import annotations

import math


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[Any]				     =						u
		for i in range(1     , a ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						temp * (u - i)
		return temp


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						int(input("enter the numbers of values: " ) )
		SCREAMING_SNAKE_CASE_				:list[list[float]]				     =						[]
		for _ in range(a ):
				y.append([] )
		for i in range(a ):
				for j in range(a ):
						y[i].append(a )
						SCREAMING_SNAKE_CASE_				:Any				     =						0

		print("enter the values of parameters in a list: " )
		SCREAMING_SNAKE_CASE_				:Dict				     =						list(map(a     , input().split() ) )

		print("enter the values of corresponding parameters: " )
		for i in range(a ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						float(input() )

		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						int(input("enter the value to interpolate: " ) )
		SCREAMING_SNAKE_CASE_				:str				     =						(value - x[0]) / (x[1] - x[0])

		# for calculating forward difference table

		for i in range(1     , a ):
				for j in range(n - i ):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						y[j + 1][i - 1] - y[j][i - 1]

		SCREAMING_SNAKE_CASE_				:Tuple				     =						y[0][0]
		for i in range(1     , a ):
				summ += (ucal(a     , a ) * y[0][i]) / math.factorial(a )

		print(F"the value at {value} is {summ}" )


if __name__ == "__main__":
		main()






