


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 _lowerCamelCase(						unittest.TestCase   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase=7,      lowerCamelCase=3,      lowerCamelCase=18,      lowerCamelCase=30,      lowerCamelCase=4_00,      lowerCamelCase=True,      lowerCamelCase=32,      lowerCamelCase=True,      )       ->   Any:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		parent
           _lowercase       :							Any      		=		batch_size
           _lowercase       :							str      		=		num_channels
           _lowercase       :							Optional[int]      		=		image_size
           _lowercase       :							Tuple      		=		min_resolution
           _lowercase       :							Tuple      		=		max_resolution
           _lowercase       :							str      		=		do_resize
           _lowercase       :							Optional[Any]      		=		size_divisor
           _lowercase       :							Any      		=		do_rescale




       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           return {
               "do_resize": self.do_resize,
               "size_divisor": self.size_divisor,
               "do_rescale": self.do_rescale,
           }






@require_torch
@require_vision
class _lowerCamelCase(						_a, unittest.TestCase   ):
       lowercase_ :	Dict     =			GLPNImageProcessor if is_vision_available() else None
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		GLPNImageProcessingTester(self)
       @property
       def 			UpperCamelCase     (					self)       ->   Union[str, Any]:




           """simple docstring"""

           return self.image_processor_tester.prepare_image_processor_dict()
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							List[str]      		=		self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(lowerCamelCase,      'do_resize'))
           self.assertTrue(hasattr(lowerCamelCase,      'size_divisor'))
           self.assertTrue(hasattr(lowerCamelCase,      'resample'))
           self.assertTrue(hasattr(lowerCamelCase,      'do_rescale'))
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           pass
       def 			UpperCamelCase     (					self)       ->   List[str]:




           """simple docstring"""

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

           # Test not batched input (GLPNImageProcessor doesn't support batching)
           _lowercase       :							Optional[int]      		=		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 			UpperCamelCase     (					self)       ->   Dict:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           _lowercase       :							Any      		=		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 (GLPNImageProcessor doesn't support batching)
           _lowercase       :							List[Any]      		=		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 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							List[str]      		=		self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           _lowercase       :							List[Any]      		=		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 (GLPNImageProcessor doesn't support batching)
           _lowercase       :							Tuple      		=		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 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )    ->						float:
    _lowercase       :							Tuple      		=		(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
    # formula for sum of series
    return total



def 				UpperCamelCase_(      )    ->						Optional[int]:
    print(sum_of_series(1							, 1							, 10 ) )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


