

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    _lowercase				(					unittest.TestCase			):

   def __init__(				self	:    Dict			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]=7			,						lowerCamelCase__	:    Any=3			,						lowerCamelCase__	:    List[str]=1_8			,						lowerCamelCase__	:    Optional[Any]=3_0			,						lowerCamelCase__	:    Tuple=4_0_0			,						lowerCamelCase__	:    Tuple=True			,						lowerCamelCase__	:    Optional[int]=3_2			,						lowerCamelCase__	:    Optional[Any]=True			,						) ->			int:
         """simple docstring"""





         A_							       =		parent
         A_							       =		batch_size
         A_							       =		num_channels
         A_							       =		image_size
         A_							       =		min_resolution
         A_							       =		max_resolution
         A_							       =		do_resize
         A_							       =		size_divisor
         A_							       =		do_rescale





   def     UpperCamelCase					(				self	:    List[Any]       ) ->			List[str]:
         """simple docstring"""





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



@require_torch
@require_vision
class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[Any]  					=    GLPNImageProcessor if is_vision_available() else None

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		GLPNImageProcessingTester(self       )

   @property
   def     UpperCamelCase					(				self	:    List[str]       ) ->			Optional[int]:
         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()

   def     UpperCamelCase					(				self	:    str       ) ->			Any:
         """simple docstring"""





         A_							       =		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	:    int       ) ->			Dict:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         A_							       =		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)
         A_							       =		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	:    List[str]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         A_							       =		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)
         A_							       =		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	:    Optional[Any]       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         A_							       =		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)
         A_							       =		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 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 1000   ):
      '''simple docstring'''







      return sum(2 * a * ((a - 1) // 2) for a in range(3		,				n + 1   )   )


if __name__ == "__main__":
      print(solution())





