code: 
  '''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
  
  
  if is_torch_available():
  					import torch
  
  if is_vision_available():
  					from PIL import Image
  
  					from transformers import SwinaSRImageProcessor
  					from transformers.image_transforms import get_image_size
  
  
  class   a_  (unittest.TestCase    ):
  
  
  
  
  							def __init__(     self   ,   snake_case_   ,   snake_case_=7   ,   snake_case_=3   ,   snake_case_=1_8   ,   snake_case_=3_0   ,   snake_case_=4_0_0   ,   snake_case_=True   ,   snake_case_=1 / 2_5_5   ,   snake_case_=True   ,   snake_case_=8   ,   ):
  									_lowerCAmelCase       :  int   				=					parent
  									_lowerCAmelCase       :  Any   				=					batch_size
  									_lowerCAmelCase       :  List[str]   				=					num_channels
  									_lowerCAmelCase       :  Any   				=					image_size
  									_lowerCAmelCase       :  Optional[Any]   				=					min_resolution
  									_lowerCAmelCase       :  Optional[int]   				=					max_resolution
  									_lowerCAmelCase       :  Union[str, Any]   				=					do_rescale
  									_lowerCAmelCase       :  Optional[int]   				=					rescale_factor
  									_lowerCAmelCase       :  Optional[Any]   				=					do_pad
  									_lowerCAmelCase       :  List[Any]   				=					pad_size
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									return {
  									    "do_rescale": self.do_rescale,
  									    "rescale_factor": self.rescale_factor,
  									    "do_pad": self.do_pad,
  									    "pad_size": self.pad_size,
  									}
  
  
  
  
  
  
  
  							def 							__UpperCamelCase       (     self   ,   snake_case_=False   ,   snake_case_=False   ,   snake_case_=False  ):
  									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
  									if equal_resolution:
  											_lowerCAmelCase       :  int   				=					[]
  											for i in range(self.batch_size  ):
  													image_inputs.append(
  													    np.random.randint(
  													        2_5_5   ,   size=(self.num_channels, self.max_resolution, self.max_resolution)   ,   dtype=np.uinta  )  )
  									else:
  											_lowerCAmelCase       :  List[str]   				=					[]
  											for i in range(self.batch_size  ):
  													_lowerCAmelCase				,					_lowerCAmelCase       :  Optional[int]   				=					np.random.choice(np.arange(self.min_resolution   ,   self.max_resolution  )   ,   2  )
  													image_inputs.append(np.random.randint(2_5_5   ,   size=(self.num_channels, width, height)   ,   dtype=np.uinta  )  )
  
  									if not numpify and not torchify:
  											# PIL expects the channel dimension as last dimension
  											_lowerCAmelCase       :  List[str]   				=					[Image.fromarray(np.moveaxis(snake_case_   ,   0   ,   -1  )  ) for x in image_inputs]
  
  									if torchify:
  											_lowerCAmelCase       :  str   				=					[torch.from_numpy(snake_case_  ) for x in image_inputs]
  
  									return image_inputs
  
  
  
  @require_torch
  @require_vision
  class   a_  (_a							,			unittest.TestCase    ):
  							__lowerCAmelCase    :				List[Any]    =      SwinaSRImageProcessor if is_vision_available() else None
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  int   				=					SwinaSRImageProcessingTester(self  )
  
  
  
  
  							@property
  							def 							__UpperCamelCase       (     self  ):
  									return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									_lowerCAmelCase       :  Dict   				=					self.image_processing_class(**self.image_processor_dict  )
  									self.assertTrue(hasattr(snake_case_   ,   """do_rescale"""  )  )
  									self.assertTrue(hasattr(snake_case_   ,   """rescale_factor"""  )  )
  									self.assertTrue(hasattr(snake_case_   ,   """do_pad"""  )  )
  									self.assertTrue(hasattr(snake_case_   ,   """pad_size"""  )  )
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									pass
  
  
  
  
  							def 							__UpperCamelCase       (     self   ,   snake_case_  ):
  									_lowerCAmelCase				,					_lowerCAmelCase       :  Optional[Any]   				=					get_image_size(snake_case_  )
  									_lowerCAmelCase       :  Tuple   				=					self.image_processor_tester.pad_size
  
  									_lowerCAmelCase       :  Any   				=					(old_height // size + 1) * size - old_height
  									_lowerCAmelCase       :  List[Any]   				=					(old_width // size + 1) * size - old_width
  									return old_height + pad_height, old_width + pad_width
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									# Initialize image_processor
  									_lowerCAmelCase       :  Optional[int]   				=					self.image_processing_class(**self.image_processor_dict  )
  									# create random PIL images
  									_lowerCAmelCase       :  Dict   				=					self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_  )
  									for image in image_inputs:
  											self.assertIsInstance(snake_case_   ,   Image.Image  )
  
  									# Test not batched input
  									_lowerCAmelCase       :  int   				=					image_processor(image_inputs[0]   ,   return_tensors="""pt"""  ).pixel_values
  									_lowerCAmelCase				,					_lowerCAmelCase       :  Optional[int]   				=					self.calculate_expected_size(np.array(image_inputs[0]  )  )
  									self.assertEqual(
  									    encoded_images.shape   ,   (
  									        1,
  									        self.image_processor_tester.num_channels,
  									        expected_height,
  									        expected_width,
  									    )   ,   )
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									# Initialize image_processor
  									_lowerCAmelCase       :  str   				=					self.image_processing_class(**self.image_processor_dict  )
  									# create random numpy tensors
  									_lowerCAmelCase       :  Union[str, Any]   				=					self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_   ,   numpify=snake_case_  )
  									for image in image_inputs:
  											self.assertIsInstance(snake_case_   ,   np.ndarray  )
  
  									# Test not batched input
  									_lowerCAmelCase       :  Union[str, Any]   				=					image_processor(image_inputs[0]   ,   return_tensors="""pt"""  ).pixel_values
  									_lowerCAmelCase				,					_lowerCAmelCase       :  Optional[Any]   				=					self.calculate_expected_size(image_inputs[0]  )
  									self.assertEqual(
  									    encoded_images.shape   ,   (
  									        1,
  									        self.image_processor_tester.num_channels,
  									        expected_height,
  									        expected_width,
  									    )   ,   )
  
  
  
  
  
  
  
  							def 							__UpperCamelCase       (     self  ):
  									# Initialize image_processor
  									_lowerCAmelCase       :  Tuple   				=					self.image_processing_class(**self.image_processor_dict  )
  									# create random PyTorch tensors
  									_lowerCAmelCase       :  str   				=					self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_   ,   torchify=snake_case_  )
  									for image in image_inputs:
  											self.assertIsInstance(snake_case_   ,   torch.Tensor  )
  
  									# Test not batched input
  									_lowerCAmelCase       :  Tuple   				=					image_processor(image_inputs[0]   ,   return_tensors="""pt"""  ).pixel_values
  									_lowerCAmelCase				,					_lowerCAmelCase       :  List[Any]   				=					self.calculate_expected_size(image_inputs[0]  )
  									self.assertEqual(
  									    encoded_images.shape   ,   (
  									        1,
  									        self.image_processor_tester.num_channels,
  									        expected_height,
  									        expected_width,
  									    )   ,   )
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  UpperCamelCase_     			=       {
      """configuration_deformable_detr""": ["""DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeformableDetrConfig"""],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       ["""DeformableDetrFeatureExtractor"""]
       UpperCamelCase_     			=       ["""DeformableDetrImageProcessor"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       UpperCamelCase_     			=       [
           """DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """DeformableDetrForObjectDetection""",
           """DeformableDetrModel""",
           """DeformableDetrPreTrainedModel""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
            from .image_processing_deformable_detr import DeformableDetrImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deformable_detr import (
                DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                DeformableDetrForObjectDetection,
                DeformableDetrModel,
                DeformableDetrPreTrainedModel,
            )
  
  else:
       import sys
  
       UpperCamelCase_     			=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  