

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   _a					(			unittest.TestCase				):




      """simple docstring"""

      def __init__(   self							:				List[Any]						,			UpperCAmelCase							:				List[str]						,			UpperCAmelCase							:				Any=7						,			UpperCAmelCase							:				Dict=3						,			UpperCAmelCase							:				Optional[Any]=18						,			UpperCAmelCase							:				List[str]=30						,			UpperCAmelCase							:				Optional[Any]=400						,			UpperCAmelCase							:				str=True						,			UpperCAmelCase							:				Optional[int]=32						,			UpperCAmelCase							:				Dict=True						,			):
            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 		__A	(   self							:				Tuple	):
            return {
                "do_resize": self.do_resize,
                "size_divisor": self.size_divisor,
                "do_rescale": self.do_rescale,
            }




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




      """simple docstring"""

      _lowerCamelCase       :			Any      		=       GLPNImageProcessor if is_vision_available() else None





      def 		__A	(   self							:				Any	):
            A_						  =				GLPNImageProcessingTester(self	)





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





      def 		__A	(   self							:				Optional[Any]	):
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"do_resize"	)	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"size_divisor"	)	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"resample"	)	)
            self.assertTrue(hasattr(UpperCAmelCase						,			"do_rescale"	)	)





      def 		__A	(   self							:				List[str]	):
            pass





      def 		__A	(   self							:				Optional[Any]	):
            # Initialize image_processing
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			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 		__A	(   self							:				str	):
            # Initialize image_processing
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase						,			numpify=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			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 		__A	(   self							:				str	):
            # Initialize image_processing
            A_						  =				self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            A_						  =				prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCAmelCase						,			torchify=UpperCAmelCase	)
            for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase						,			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	)

'''simple docstring'''

def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 return number & 1 == 0


if __name__ == "__main__":
     import doctest

     doctest.testmod()





