




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	:	Optional[int]						,__A	:	Tuple						,__A	:	Optional[Any]=7						,__A	:	Dict=3						,__A	:	Optional[int]=18						,__A	:	str=30						,__A	:	Dict=400						,__A	:	List[Any]=True						,__A	:	Union[str, Any]=32						,__A	:	Optional[Any]=True						,)						->					Optional[int]:
         _lowercase							= parent
         _lowercase							= batch_size
         _lowercase							= num_channels
         _lowercase							= image_size
         _lowercase							= min_resolution
         _lowercase							= max_resolution
         _lowercase							= do_resize
         _lowercase							= size_divisor
         _lowercase							= do_rescale







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Optional[int]:
         return {
             "do_resize": self.do_resize,
             "size_divisor": self.size_divisor,
             "do_rescale": self.do_rescale,
         }





@require_torch
@require_vision
class 				A_				(      __A					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Any      =  GLPNImageProcessor if is_vision_available() else None







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Optional[int]:
         _lowercase							= GLPNImageProcessingTester(self  )







       @property
       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[int]:
         return self.image_processor_tester.prepare_image_processor_dict()







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Any:
         _lowercase							= 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    __UpperCAmelCase (    self	:	int  )						->					str:
         pass







       def    __UpperCAmelCase (    self	:	str  )						->					Tuple:
         _lowercase							= self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         _lowercase							= 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)
         _lowercase							= 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]  )						->					Any:
         _lowercase							= self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         _lowercase							= 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)
         _lowercase							= 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]:
         _lowercase							= self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         _lowercase							= 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)
         _lowercase							= 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 ....utils import logging


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Union[str, Any]						,__A	:	int						,__A	:	List[Any]=None						,__A	:	List[str]=2048  )						->					List[Any]:
         _lowercase							= config.__dict__
         _lowercase							= modal_hidden_size
         if num_labels:
           _lowercase							= num_labels