code: 
  
  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 DeiTImageProcessor
  
  
  
  
  
  
  class    __A				(							unittest.TestCase ):
  
  
  
  
  
  
      def __init__(	self			:    List[Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    List[Any]=7    ,				UpperCAmelCase_			:    List[str]=3    ,				UpperCAmelCase_			:    int=18    ,				UpperCAmelCase_			:    str=30    ,				UpperCAmelCase_			:    Any=400    ,				UpperCAmelCase_			:    int=True    ,				UpperCAmelCase_			:    List[str]=None    ,				UpperCAmelCase_			:    Tuple=True    ,				UpperCAmelCase_			:    List[str]=None    ,				UpperCAmelCase_			:    List[Any]=True    ,				UpperCAmelCase_			:    List[str]=[0.5, 0.5, 0.5]    ,				UpperCAmelCase_			:    List[str]=[0.5, 0.5, 0.5]    ,				):
          lowerCAmelCase				:			Optional[Any]    			=      size if size is not None else {"""height""": 20, """width""": 20}
          lowerCAmelCase				:			Optional[Any]    			=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
  
          lowerCAmelCase				:			str    			=      parent
          lowerCAmelCase				:			Union[str, Any]    			=      batch_size
          lowerCAmelCase				:			Any    			=      num_channels
          lowerCAmelCase				:			List[Any]    			=      image_size
          lowerCAmelCase				:			Union[str, Any]    			=      min_resolution
          lowerCAmelCase				:			Union[str, Any]    			=      max_resolution
          lowerCAmelCase				:			Optional[int]    			=      do_resize
          lowerCAmelCase				:			int    			=      size
          lowerCAmelCase				:			List[Any]    			=      do_center_crop
          lowerCAmelCase				:			Any    			=      crop_size
          lowerCAmelCase				:			List[Any]    			=      do_normalize
          lowerCAmelCase				:			Dict    			=      image_mean
          lowerCAmelCase				:			str    			=      image_std
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Union[str, Any]    ):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
          }
  
  
  
  
  
  
  @require_torch
  @require_vision
  class    __A				(							__lowerCamelCase						,						unittest.TestCase ):
      lowerCAmelCase_							:		int						=							DeiTImageProcessor if is_vision_available() else None
      lowerCAmelCase_							:		Dict						=							True
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          lowerCAmelCase				:			Union[str, Any]    			=      DeiTImageProcessingTester(self    )
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    str    ):
          return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[Any]    ):
          lowerCAmelCase				:			str    			=      self.image_processing_class(**self.image_processor_dict    )
          self.assertTrue(hasattr(_lowerCAmelCase    ,				'do_resize'    )    )
          self.assertTrue(hasattr(_lowerCAmelCase    ,				'size'    )    )
          self.assertTrue(hasattr(_lowerCAmelCase    ,				'do_center_crop'    )    )
          self.assertTrue(hasattr(_lowerCAmelCase    ,				'center_crop'    )    )
          self.assertTrue(hasattr(_lowerCAmelCase    ,				'do_normalize'    )    )
          self.assertTrue(hasattr(_lowerCAmelCase    ,				'image_mean'    )    )
          self.assertTrue(hasattr(_lowerCAmelCase    ,				'image_std'    )    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			Optional[int]    			=      self.image_processing_class.from_dict(self.image_processor_dict    )
          self.assertEqual(image_processor.size    ,				{'height': 20, 'width': 20}    )
          self.assertEqual(image_processor.crop_size    ,				{'height': 18, 'width': 18}    )
  
          lowerCAmelCase				:			Tuple    			=      self.image_processing_class.from_dict(self.image_processor_dict    ,				size=42    ,				crop_size=84    )
          self.assertEqual(image_processor.size    ,				{'height': 42, 'width': 42}    )
          self.assertEqual(image_processor.crop_size    ,				{'height': 84, 'width': 84}    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ):
          pass
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          # Initialize image_processing
          lowerCAmelCase				:			Tuple    			=      self.image_processing_class(**self.image_processor_dict    )
          # create random PIL images
          lowerCAmelCase				:			Optional[Any]    			=      prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=_lowerCAmelCase    )
          for image in image_inputs:
              self.assertIsInstance(_lowerCAmelCase    ,				Image.Image    )
  
          # Test not batched input
          lowerCAmelCase				:			Union[str, Any]    			=      image_processing(image_inputs[0]    ,				return_tensors='pt'    ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )    ,				)
  
          # Test batched
          lowerCAmelCase				:			List[Any]    			=      image_processing(_lowerCAmelCase    ,				return_tensors='pt'    ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          # Initialize image_processing
          lowerCAmelCase				:			Dict    			=      self.image_processing_class(**self.image_processor_dict    )
          # create random numpy tensors
          lowerCAmelCase				:			Dict    			=      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
          lowerCAmelCase				:			List[str]    			=      image_processing(image_inputs[0]    ,				return_tensors='pt'    ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )    ,				)
  
          # Test batched
          lowerCAmelCase				:			Optional[Any]    			=      image_processing(_lowerCAmelCase    ,				return_tensors='pt'    ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ):
          # Initialize image_processing
          lowerCAmelCase				:			Optional[int]    			=      self.image_processing_class(**self.image_processor_dict    )
          # create random PyTorch tensors
          lowerCAmelCase				:			Optional[int]    			=      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
          lowerCAmelCase				:			str    			=      image_processing(image_inputs[0]    ,				return_tensors='pt'    ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )    ,				)
  
          # Test batched
          lowerCAmelCase				:			Dict    			=      image_processing(_lowerCAmelCase    ,				return_tensors='pt'    ).pixel_values
          self.assertEqual(
              encoded_images.shape    ,				(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              )    ,				)
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __A   :     List[Any]      =			{'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Tuple      =			[
       '''UMT5EncoderModel''',
       '''UMT5ForConditionalGeneration''',
       '''UMT5ForQuestionAnswering''',
       '''UMT5Model''',
       '''UMT5PreTrainedModel''',
   ]
  
  if TYPE_CHECKING:
   from .configuration_umta import UMTaConfig, UMTaOnnxConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_umta import (
        UMTaEncoderModel,
        UMTaForConditionalGeneration,
        UMTaForQuestionAnswering,
        UMTaModel,
        UMTaPreTrainedModel,
    )
  else:
   import sys
  
   __A   :     Any      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  