






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase     :							Optional[int]    ={
    '''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase     :							str    =[
     '''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GraphormerForGraphClassification''',
     '''GraphormerModel''',
     '''GraphormerPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_graphormer import (
      GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      GraphormerForGraphClassification,
      GraphormerModel,
      GraphormerPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase     :							Union[str, Any]    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







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	):







 def __init__(		self :       Tuple							,       SCREAMING_SNAKE_CASE :       Optional[int]							,       SCREAMING_SNAKE_CASE :       List[Any]=7							,       SCREAMING_SNAKE_CASE :       str=3							,       SCREAMING_SNAKE_CASE :       Dict=18							,       SCREAMING_SNAKE_CASE :       str=30							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=4_00							,       SCREAMING_SNAKE_CASE :       int=True							,       SCREAMING_SNAKE_CASE :       Any=32							,       SCREAMING_SNAKE_CASE :       int=True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  parent
      UpperCamelCase__      :       Dict	    =  batch_size
      UpperCamelCase__      :       Optional[Any]	    =  num_channels
      UpperCamelCase__      :       int	    =  image_size
      UpperCamelCase__      :       Dict	    =  min_resolution
      UpperCamelCase__      :       List[str]	    =  max_resolution
      UpperCamelCase__      :       Union[str, Any]	    =  do_resize
      UpperCamelCase__      :       Optional[Any]	    =  size_divisor
      UpperCamelCase__      :       int	    =  do_rescale







 def  __lowercase (		self :       Union[str, Any]						):


      '''simple docstring'''






      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	):
 _lowerCAmelCase	:	List[Any]				=			GLPNImageProcessor if is_vision_available() else None







 def  __lowercase (		self :       Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__      :       Tuple	    =  GLPNImageProcessingTester(self						)







 @property
 def  __lowercase (		self :       int						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def  __lowercase (		self :       List[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_resize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "size_divisor"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "resample"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,       "do_rescale"						)						)







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






      pass







 def  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       Any	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       Image.Image						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       List[Any]	    =  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  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__      :       Optional[Any]	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       numpify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       np.ndarray						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       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  __lowercase (		self :       Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__      :       Tuple	    =  prepare_image_inputs(self.image_processor_tester							,       equal_resolution=SCREAMING_SNAKE_CASE							,       torchify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,       torch.Tensor						)

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      UpperCamelCase__      :       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						)