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 BlipImageProcessor
  
  
  
  
  
  
  
  class  UpperCAmelCase (       unittest.TestCase):
  
   def __init__( self :	Union[str, Any],					a_ :	Optional[Any],					a_ :	Optional[Any]=7,					a_ :	str=3,					a_ :	Dict=18,					a_ :	Tuple=30,					a_ :	Tuple=400,					a_ :	Any=True,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=True,					a_ :	int=False,					a_ :	Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073],					a_ :	Any=[0.26_862_954, 0.26_130_258, 0.27_577_711],					a_ :	str=True,					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						size if size is not None else {"height": 20, "width": 20}
      UpperCamelCase__ 						=						parent
      UpperCamelCase__ 						=						batch_size
      UpperCamelCase__ 						=						num_channels
      UpperCamelCase__ 						=						image_size
      UpperCamelCase__ 						=						min_resolution
      UpperCamelCase__ 						=						max_resolution
      UpperCamelCase__ 						=						do_resize
      UpperCamelCase__ 						=						size
      UpperCamelCase__ 						=						do_normalize
      UpperCamelCase__ 						=						image_mean
      UpperCamelCase__ 						=						image_std
      UpperCamelCase__ 						=						do_pad
      UpperCamelCase__ 						=						do_convert_rgb
  
   def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
          "do_pad": self.do_pad,
      }
  
   def 		lowercase_						( self :	Dict,					a_ :	Any=False,					a_ :	Optional[Any]=False,					a_ :	Optional[Any]=False					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
      if equal_resolution:
         UpperCamelCase__ 						=						[]
         for i in range(self.batch_size					):
            image_inputs.append(
                np.random.randint(
                    255,					size=(self.num_channels, self.max_resolution, self.max_resolution),					dtype=np.uinta					)					)
      else:
         UpperCamelCase__ 						=						[]
         for i in range(self.batch_size					):
            UpperCamelCase__						,		UpperCamelCase__ 						=						np.random.choice(np.arange(self.min_resolution,					self.max_resolution					),					2					)
            image_inputs.append(np.random.randint(255,					size=(self.num_channels, width, height),					dtype=np.uinta					)					)
  
      if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         UpperCamelCase__ 						=						[Image.fromarray(np.moveaxis(a_,					0,					-1					)					) for x in image_inputs]
  
      if torchify:
         UpperCamelCase__ 						=						[torch.from_numpy(a_					) for x in image_inputs]
  
      return image_inputs
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__		,			unittest.TestCase):
   _lowerCamelCase :					str    					=					BlipImageProcessor if is_vision_available() else None
  
   def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						BlipImageProcessingTester(self					)
  
   @property
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor_tester.prepare_image_processor_dict()
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(a_,					"do_resize"					)					)
      self.assertTrue(hasattr(a_,					"size"					)					)
      self.assertTrue(hasattr(a_,					"do_normalize"					)					)
      self.assertTrue(hasattr(a_,					"image_mean"					)					)
      self.assertTrue(hasattr(a_,					"image_std"					)					)
      self.assertTrue(hasattr(a_,					"do_convert_rgb"					)					)
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      pass
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      UpperCamelCase__ 						=						self.image_processor_tester.prepare_inputs(equal_resolution=a_					)
      for image in image_inputs:
         self.assertIsInstance(a_,					Image.Image					)
  
      # Test not batched input
      UpperCamelCase__ 						=						image_processor(image_inputs[0],					return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape,					(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          ),					)
  
      # Test batched
      UpperCamelCase__ 						=						image_processor(a_,					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.size["height"],
              self.image_processor_tester.size["width"],
          ),					)
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.image_processing_class(**self.image_processor_dict					)
      # create random numpy tensors
      UpperCamelCase__ 						=						self.image_processor_tester.prepare_inputs(equal_resolution=a_,					numpify=a_					)
      for image in image_inputs:
         self.assertIsInstance(a_,					np.ndarray					)
  
      # Test not batched input
      UpperCamelCase__ 						=						image_processor(image_inputs[0],					return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape,					(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          ),					)
  
      # Test batched
      UpperCamelCase__ 						=						image_processor(a_,					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.size["height"],
              self.image_processor_tester.size["width"],
          ),					)
  
   def 		lowercase_						( self :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.image_processing_class(**self.image_processor_dict					)
      # create random PyTorch tensors
      UpperCamelCase__ 						=						self.image_processor_tester.prepare_inputs(equal_resolution=a_,					torchify=a_					)
      for image in image_inputs:
         self.assertIsInstance(a_,					torch.Tensor					)
  
      # Test not batched input
      UpperCamelCase__ 						=						image_processor(image_inputs[0],					return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape,					(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          ),					)
  
      # Test batched
      UpperCamelCase__ 						=						image_processor(a_,					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.size["height"],
              self.image_processor_tester.size["width"],
          ),					)
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__		,			unittest.TestCase):
   _lowerCamelCase :					int    					=					BlipImageProcessor if is_vision_available() else None
  
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						BlipImageProcessingTester(self,					num_channels=4					)
      UpperCamelCase__ 						=						3
  
   @property
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor_tester.prepare_image_processor_dict()
  
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.image_processing_class(**self.image_processor_dict					)
      self.assertTrue(hasattr(a_,					"do_resize"					)					)
      self.assertTrue(hasattr(a_,					"size"					)					)
      self.assertTrue(hasattr(a_,					"do_normalize"					)					)
      self.assertTrue(hasattr(a_,					"image_mean"					)					)
      self.assertTrue(hasattr(a_,					"image_std"					)					)
      self.assertTrue(hasattr(a_,					"do_convert_rgb"					)					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      pass
  
   def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.image_processing_class(**self.image_processor_dict					)
      # create random PIL images
      UpperCamelCase__ 						=						self.image_processor_tester.prepare_inputs(equal_resolution=a_					)
      for image in image_inputs:
         self.assertIsInstance(a_,					Image.Image					)
  
      # Test not batched input
      UpperCamelCase__ 						=						image_processor(image_inputs[0],					return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape,					(
              1,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          ),					)
  
      # Test batched
      UpperCamelCase__ 						=						image_processor(a_,					return_tensors="pt"					).pixel_values
      self.assertEqual(
          encoded_images.shape,					(
              self.image_processor_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          ),					)
style_context: 
  
  
  '''simple docstring'''
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
         import torch
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
   _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
   _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')
  
   def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."					)
      if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."					)
  
      UpperCamelCase__ 						=						max_seq_length
      UpperCamelCase__ 						=						task_seq_length
  
      super().__init__(a_,					a_					)
  
   def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if text_list is None:
         raise ValueError("tokens cannot be None."					)
  
      UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)
  
      UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]
  
      UpperCamelCase__ 						=						[]
      for attn_mask, input_id in zip(a_,					a_					):
         UpperCamelCase__ 						=						torch.tensor(a_					) * torch.tensor(a_					)
         token_inputs.append(token.unsqueeze(0					)					)
  
      UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
      return token_inputs
  
   def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_semantic_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_instance_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_panoptic_segmentation(*a_,					**a_					)