


'''simple docstring'''

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			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=7			,  UpperCamelCase=3			,  UpperCamelCase=18			,  UpperCamelCase=30			,  UpperCamelCase=400			,  UpperCamelCase=True			,  UpperCamelCase=32			,  UpperCamelCase=True			,  ):







   """simple docstring"""
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  batch_size
   lowerCamelCase_      =  num_channels
   lowerCamelCase_      =  image_size
   lowerCamelCase_      =  min_resolution
   lowerCamelCase_      =  max_resolution
   lowerCamelCase_      =  do_resize
   lowerCamelCase_      =  size_divisor
   lowerCamelCase_      =  do_rescale







  def   snake_case		(    self     ):







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





@require_torch
@require_vision
class      snake_case    (    lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  GLPNImageProcessor if is_vision_available() else None






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  GLPNImageProcessingTester(self     )






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   return self.image_processor_tester.prepare_image_processor_dict()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    self     ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_      =  self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   lowerCamelCase_      =  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)
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_      =  self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   lowerCamelCase_      =  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)
   lowerCamelCase_      =  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   snake_case		(    self     ):







   """simple docstring"""
   # Initialize image_processing
   lowerCamelCase_      =  self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   lowerCamelCase_      =  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)
   lowerCamelCase_      =  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     )









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:    List[Any]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict=7				,    _lowerCAmelCase:    Optional[Any]=3				,    _lowerCAmelCase:    List[Any]=18				,    _lowerCAmelCase:    int=30				,    _lowerCAmelCase:    Union[str, Any]=4_00				,    _lowerCAmelCase:    Any=True				,    _lowerCAmelCase:    Union[str, Any]=32				,    _lowerCAmelCase:    int=True				,    ):
         lowercase			:Any							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:Any							=  num_channels
         lowercase			:Optional[Any]							=  image_size
         lowercase			:Optional[Any]							=  min_resolution
         lowercase			:str							=  max_resolution
         lowercase			:str							=  do_resize
         lowercase			:Optional[int]							=  size_divisor
         lowercase			:Tuple							=  do_rescale






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return {
             "do_resize": self.do_resize,
             "size_divisor": self.size_divisor,
             "do_rescale": self.do_rescale,
         }







@require_torch
@require_vision
class       __lowerCAmelCase				(							lowerCAmelCase					,       unittest.TestCase):
       _a           =  GLPNImageProcessor if is_vision_available() else None






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:int							=  GLPNImageProcessingTester(self )






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return self.image_processor_tester.prepare_image_processor_dict()






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:Optional[Any]							=  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     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         pass






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         # Initialize image_processing
         lowercase			:Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         lowercase			:List[str]							=  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			:str							=  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     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         # Initialize image_processing
         lowercase			:Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         lowercase			:Union[str, 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			: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     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         # Initialize image_processing
         lowercase			:Optional[Any]							=  self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         lowercase			:List[str]							=  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			:Dict							=  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 )



