code: 
  
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  
  if is_torch_available():
    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
  else:
    __a   				=      False
  
  if is_vision_available():
    from PIL import Image
  
    from transformers import (
        AutoProcessor,
        PixaStructImageProcessor,
        PixaStructProcessor,
        PreTrainedTokenizerFast,
        TaTokenizer,
    )
  
  
  
  
  
  
  
  @unittest.skipIf(
      not is_torch_greater_or_equal_than_1_11  ,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'  ,						)
  @require_vision
  @require_torch
  class 				__SCREAMING_SNAKE_CASE   (			unittest.TestCase   ):
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   Any					   =		tempfile.mkdtemp()
  
              lowercase			:   Tuple					   =		PixaStructImageProcessor()
              lowercase			:   Optional[Any]					   =		TaTokenizer.from_pretrained('''t5-small'''		)
  
              lowercase			:   Any					   =		PixaStructProcessor(_lowerCamelCase							,						_lowerCamelCase		)
  
              processor.save_pretrained(self.tmpdirname		)
  
  
        def        __lowerCamelCase			(							self							,						**SCREAMING_SNAKE_CASE__		):
              return AutoProcessor.from_pretrained(self.tmpdirname							,						**_lowerCamelCase		).tokenizer
  
  
        def        __lowerCamelCase			(							self							,						**SCREAMING_SNAKE_CASE__		):
              return AutoProcessor.from_pretrained(self.tmpdirname							,						**_lowerCamelCase		).image_processor
  
  
        def        __lowerCamelCase			(							self		):
              shutil.rmtree(self.tmpdirname		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   Optional[Any]					   =		[np.random.randint(255							,						size=(3, 30, 400)							,						dtype=np.uinta		)]
  
              lowercase			:   Dict					   =		[Image.fromarray(np.moveaxis(_lowerCamelCase							,						0							,						-1		)		) for x in image_inputs]
  
              return image_inputs
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   int					   =		PixaStructProcessor(tokenizer=self.get_tokenizer()							,						image_processor=self.get_image_processor()		)
              processor.save_pretrained(self.tmpdirname		)
  
              lowercase			:   str					   =		self.get_tokenizer(bos_token='''(BOS)'''							,						eos_token='''(EOS)'''		)
              lowercase			:   int					   =		self.get_image_processor(do_normalize=_lowerCamelCase							,						padding_value=1.0		)
  
              lowercase			:   Any					   =		PixaStructProcessor.from_pretrained(
                  self.tmpdirname							,						bos_token='''(BOS)'''							,						eos_token='''(EOS)'''							,						do_normalize=_lowerCamelCase							,						padding_value=1.0		)
  
              self.assertEqual(processor.tokenizer.get_vocab()							,						tokenizer_add_kwargs.get_vocab()		)
              self.assertIsInstance(processor.tokenizer							,						_lowerCamelCase		)
  
              self.assertEqual(processor.image_processor.to_json_string()							,						image_processor_add_kwargs.to_json_string()		)
              self.assertIsInstance(processor.image_processor							,						_lowerCamelCase		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   str					   =		self.get_image_processor()
              lowercase			:   List[str]					   =		self.get_tokenizer()
  
              lowercase			:   str					   =		PixaStructProcessor(tokenizer=_lowerCamelCase							,						image_processor=_lowerCamelCase		)
  
              lowercase			:   List[Any]					   =		self.prepare_image_inputs()
  
              lowercase			:   List[str]					   =		image_processor(_lowerCamelCase							,						return_tensors='''np'''		)
              lowercase			:   List[str]					   =		processor(images=_lowerCamelCase							,						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        __lowerCamelCase			(							self		):
              lowercase			:   str					   =		self.get_image_processor()
              lowercase			:   Optional[Any]					   =		self.get_tokenizer()
  
              lowercase			:   Union[str, Any]					   =		PixaStructProcessor(tokenizer=_lowerCamelCase							,						image_processor=_lowerCamelCase		)
  
              lowercase			:   Optional[Any]					   =		"""lower newer"""
  
              lowercase			:   List[Any]					   =		processor(text=_lowerCamelCase		)
  
              lowercase			:   int					   =		tokenizer(_lowerCamelCase							,						return_token_type_ids=_lowerCamelCase							,						add_special_tokens=_lowerCamelCase		)
  
              for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]							,						encoded_processor[key]		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   Any					   =		self.get_image_processor()
              lowercase			:   Optional[Any]					   =		self.get_tokenizer()
  
              lowercase			:   int					   =		PixaStructProcessor(tokenizer=_lowerCamelCase							,						image_processor=_lowerCamelCase		)
  
              lowercase			:   Optional[int]					   =		"""lower newer"""
              lowercase			:   Union[str, Any]					   =		self.prepare_image_inputs()
  
              lowercase			:   str					   =		processor(text=_lowerCamelCase							,						images=_lowerCamelCase		)
  
              self.assertListEqual(
                  list(inputs.keys()		)							,						['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']		)
  
              # test if it raises when no input is passed
              with pytest.raises(_lowerCamelCase		):
                    processor()
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   Any					   =		self.get_image_processor()
              lowercase			:   Dict					   =		self.get_tokenizer()
  
              lowercase			:   Dict					   =		PixaStructProcessor(tokenizer=_lowerCamelCase							,						image_processor=_lowerCamelCase		)
  
              lowercase			:   Optional[Any]					   =		"""lower newer"""
              lowercase			:   Optional[int]					   =		self.prepare_image_inputs()
  
              lowercase			:   Tuple					   =		processor(text=_lowerCamelCase							,						images=_lowerCamelCase		)
  
              lowercase			:   Optional[Any]					   =		[512, 1024, 2048, 4096]
              lowercase			:   List[str]					   =		[770, 770, 770, 770]
              # with text
              for i, max_patch in enumerate(_lowerCamelCase		):
                    lowercase			:   List[Any]					   =		processor(text=_lowerCamelCase							,						images=_lowerCamelCase							,						max_patches=_lowerCamelCase		)
                    self.assertEqual(inputs['''flattened_patches'''][0].shape[0]							,						_lowerCamelCase		)
                    self.assertEqual(inputs['''flattened_patches'''][0].shape[1]							,						expected_hidden_size[i]		)
  
              # without text input
              for i, max_patch in enumerate(_lowerCamelCase		):
                    lowercase			:   Dict					   =		processor(images=_lowerCamelCase							,						max_patches=_lowerCamelCase		)
                    self.assertEqual(inputs['''flattened_patches'''][0].shape[0]							,						_lowerCamelCase		)
                    self.assertEqual(inputs['''flattened_patches'''][0].shape[1]							,						expected_hidden_size[i]		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   int					   =		self.get_image_processor()
              lowercase			:   List[Any]					   =		self.get_tokenizer()
  
              lowercase			:   Tuple					   =		PixaStructProcessor(tokenizer=_lowerCamelCase							,						image_processor=_lowerCamelCase		)
  
              lowercase			:   Optional[int]					   =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
              lowercase			:   int					   =		processor.batch_decode(_lowerCamelCase		)
              lowercase			:   Dict					   =		tokenizer.batch_decode(_lowerCamelCase		)
  
              self.assertListEqual(_lowerCamelCase							,						_lowerCamelCase		)
  
  
  
  
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   List[str]					   =		self.get_image_processor()
              lowercase			:   Any					   =		self.get_tokenizer()
  
              lowercase			:   Optional[Any]					   =		PixaStructProcessor(tokenizer=_lowerCamelCase							,						image_processor=_lowerCamelCase		)
  
              lowercase			:   Dict					   =		"""lower newer"""
              lowercase			:   Dict					   =		self.prepare_image_inputs()
  
              lowercase			:   Dict					   =		processor(text=_lowerCamelCase							,						images=_lowerCamelCase		)
  
              # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
              self.assertListEqual(
                  list(inputs.keys()		)							,						['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids''']		)
  
              lowercase			:   Optional[int]					   =		processor(text=_lowerCamelCase		)
  
              # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
              self.assertListEqual(list(inputs.keys()		)							,						['''input_ids''', '''attention_mask''']		)
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase     )    ->list[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        assert (
            isinstance(_UpperCamelCase,    _UpperCamelCase     ) and mask > 0
        ), f"""mask needs to be positive integer, your input {mask}"""
        lowercase			:   Optional[int]					   =		[]
        lowercase			:   Optional[int]					   =		mask
  
        while submask:
              all_submasks.append(_UpperCamelCase     )
              lowercase			:   Dict					   =		(submask - 1) & mask
  
        return all_submasks
  
  
  if __name__ == "__main__":
    import doctest
  
    doctest.testmod()
  
  