code: 
  
  
  '''simple docstring'''
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  
  
  
  
  
  
  @require_vision
  class  UpperCAmelCase (       unittest.TestCase):
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						tempfile.mkdtemp()
  
      UpperCamelCase__ 						=						CLIPImageProcessor()
      UpperCamelCase__ 						=						BertTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-BertModel",					model_input_names=["input_ids", "attention_mask"]					)
  
      UpperCamelCase__ 						=						GitProcessor(a_,					a_					)
  
      processor.save_pretrained(self.tmpdirname					)
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).tokenizer
  
   def 		lowercase_						( self :	Union[str, Any],					**a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).image_processor
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      shutil.rmtree(self.tmpdirname					)
  
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta					)]
  
      UpperCamelCase__ 						=						[Image.fromarray(np.moveaxis(a_,					0,					-1					)					) for x in image_inputs]
  
      return image_inputs
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()					)
      processor.save_pretrained(self.tmpdirname					)
  
      UpperCamelCase__ 						=						self.get_tokenizer(bos_token="(BOS)",					eos_token="(EOS)"					)
      UpperCamelCase__ 						=						self.get_image_processor(do_normalize=a_,					padding_value=1.0					)
  
      UpperCamelCase__ 						=						GitProcessor.from_pretrained(
          self.tmpdirname,					bos_token="(BOS)",					eos_token="(EOS)",					do_normalize=a_,					padding_value=1.0					)
  
      self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()					)
      self.assertIsInstance(processor.tokenizer,					a_					)
  
      self.assertEqual(processor.image_processor.to_json_string(),					image_processor_add_kwargs.to_json_string()					)
      self.assertIsInstance(processor.image_processor,					a_					)
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						image_processor(a_,					return_tensors="np"					)
      UpperCamelCase__ 						=						processor(images=a_,					return_tensors="np"					)
  
      for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
  
      UpperCamelCase__ 						=						processor(text=a_					)
  
      UpperCamelCase__ 						=						tokenizer(a_,					return_token_type_ids=a_					)
  
      for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key],					encoded_processor[key]					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)
  
      # test if it raises when no input is passed
      with pytest.raises(a_					):
         processor()
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
      UpperCamelCase__ 						=						processor.batch_decode(a_					)
      UpperCamelCase__ 						=						tokenizer.batch_decode(a_					)
  
      self.assertListEqual(a_,					a_					)
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.get_image_processor()
      UpperCamelCase__ 						=						self.get_tokenizer()
  
      UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)
  
      UpperCamelCase__ 						=						"lower newer"
      UpperCamelCase__ 						=						self.prepare_image_inputs()
  
      UpperCamelCase__ 						=						processor(text=a_,					images=a_					)
  
      # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
      self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)
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_					)