


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        snake_case_   (unittest.TestCase      ):
					def __init__(      self   :Optional[int]			,__snake_case   :Optional[int]			,__snake_case   :int=7			,__snake_case   :Any=3			,__snake_case   :Optional[Any]=18			,__snake_case   :str=30			,__snake_case   :Dict=4_00			,__snake_case   :Optional[int]=True			,__snake_case   :Dict=32			,__snake_case   :Union[str, Any]=True			,)					->    Optional[Any]:
												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  lowerCamelCase__(      self   :List[Any]      )					->    str:
												return {
												    "do_resize": self.do_resize,
												    "size_divisor": self.size_divisor,
												    "do_rescale": self.do_rescale,
												}







@require_torch
@require_vision
class        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Tuple          =			GLPNImageProcessor if is_vision_available() else None
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[Any]:
												a__             =     GLPNImageProcessingTester(self      )
					@property
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Optional[int]:
												return self.image_processor_tester.prepare_image_processor_dict()
					def  lowerCamelCase__(      self   :List[Any]      )					->    Dict:
												a__             =     self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(__snake_case			,'do_resize'      )      )
												self.assertTrue(hasattr(__snake_case			,'size_divisor'      )      )
												self.assertTrue(hasattr(__snake_case			,'resample'      )      )
												self.assertTrue(hasattr(__snake_case			,'do_rescale'      )      )
					def  lowerCamelCase__(      self   :Any      )					->    Optional[int]:
												pass
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Dict:
												# 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=__snake_case      )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case			,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  lowerCamelCase__(      self   :Dict      )					->    int:
												# 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=__snake_case			,numpify=__snake_case      )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case			,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  lowerCamelCase__(      self   :List[Any]      )					->    List[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=__snake_case			,torchify=__snake_case      )
												for image in image_inputs:
																			self.assertIsInstance(__snake_case			,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      )








from abc import ABC, abstractmethod
from argparse import ArgumentParser







class        snake_case_   (lowerCamelCase_      ):
					@staticmethod
					@abstractmethod
					def  lowerCamelCase__(      __snake_case   :ArgumentParser      )					->    Dict:
												raise NotImplementedError()



					@abstractmethod
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Dict:
												raise NotImplementedError()




