code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  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 							_lowerCAmelCase		(     unittest.TestCase	):
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =tempfile.mkdtemp()
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =PixaStructImageProcessor()
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =TaTokenizer.from_pretrained('''t5-small'''							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =PixaStructProcessor(snake_case_		,		snake_case_							)
  
  											processor.save_pretrained(self.tmpdirname							)
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      Optional[int]:
  											return AutoProcessor.from_pretrained(self.tmpdirname		,		**snake_case_							).tokenizer
  
  
  
  
  				def     __a		( self		,		**snake_case_							)    ->      Any:
  											return AutoProcessor.from_pretrained(self.tmpdirname		,		**snake_case_							).image_processor
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											shutil.rmtree(self.tmpdirname							)
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[np.random.randint(255		,		size=(3, 30, 400)		,		dtype=np.uinta							)]
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =[Image.fromarray(np.moveaxis(snake_case_		,		0		,		-1							)							) for x in image_inputs]
  
  											return image_inputs
  
  
  
  
  				def     __a		( self							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: int	     =PixaStructProcessor(tokenizer=self.get_tokenizer()		,		image_processor=self.get_image_processor()							)
  											processor.save_pretrained(self.tmpdirname							)
  
  											SCREAMING_SNAKE_CASE			: str	     =self.get_tokenizer(bos_token='''(BOS)'''		,		eos_token='''(EOS)'''							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.get_image_processor(do_normalize=snake_case_		,		padding_value=1.0							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =PixaStructProcessor.from_pretrained(
  											    self.tmpdirname		,		bos_token='''(BOS)'''		,		eos_token='''(EOS)'''		,		do_normalize=snake_case_		,		padding_value=1.0							)
  
  											self.assertEqual(processor.tokenizer.get_vocab()		,		tokenizer_add_kwargs.get_vocab()							)
  											self.assertIsInstance(processor.tokenizer		,		snake_case_							)
  
  											self.assertEqual(processor.image_processor.to_json_string()		,		image_processor_add_kwargs.to_json_string()							)
  											self.assertIsInstance(processor.image_processor		,		snake_case_							)
  
  
  
  
  				def     __a		( self							)    ->      Optional[Any]:
  											SCREAMING_SNAKE_CASE			: int	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     =self.prepare_image_inputs()
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =image_processor(snake_case_		,		return_tensors='''np'''							)
  											SCREAMING_SNAKE_CASE			: str	     =processor(images=snake_case_		,		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     __a		( self							)    ->      str:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: str	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: str	     ='''lower newer'''
  
  											SCREAMING_SNAKE_CASE			: str	     =processor(text=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =tokenizer(snake_case_		,		return_token_type_ids=snake_case_		,		add_special_tokens=snake_case_							)
  
  											for key in encoded_tok.keys():
  																		self.assertListEqual(encoded_tok[key]		,		encoded_processor[key]							)
  
  
  
  
  				def     __a		( self							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: str	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Dict	     ='''lower newer'''
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.prepare_image_inputs()
  
  											SCREAMING_SNAKE_CASE			: Dict	     =processor(text=snake_case_		,		images=snake_case_							)
  
  											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(snake_case_							):
  																		processor()
  
  
  
  
  				def     __a		( self							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: str	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: str	     ='''lower newer'''
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.prepare_image_inputs()
  
  											SCREAMING_SNAKE_CASE			: int	     =processor(text=snake_case_		,		images=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: str	     =[512, 1_024, 2_048, 4_096]
  											SCREAMING_SNAKE_CASE			: List[Any]	     =[770, 770, 770, 770]
  											# with text
  											for i, max_patch in enumerate(snake_case_							):
  																		SCREAMING_SNAKE_CASE			: Any	     =processor(text=snake_case_		,		images=snake_case_		,		max_patches=snake_case_							)
  																		self.assertEqual(inputs['''flattened_patches'''][0].shape[0]		,		snake_case_							)
  																		self.assertEqual(inputs['''flattened_patches'''][0].shape[1]		,		expected_hidden_size[i]							)
  
  											# without text input
  											for i, max_patch in enumerate(snake_case_							):
  																		SCREAMING_SNAKE_CASE			: Any	     =processor(images=snake_case_		,		max_patches=snake_case_							)
  																		self.assertEqual(inputs['''flattened_patches'''][0].shape[0]		,		snake_case_							)
  																		self.assertEqual(inputs['''flattened_patches'''][0].shape[1]		,		expected_hidden_size[i]							)
  
  
  
  
  				def     __a		( self							)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: str	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: str	     =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =processor.batch_decode(snake_case_							)
  											SCREAMING_SNAKE_CASE			: str	     =tokenizer.batch_decode(snake_case_							)
  
  											self.assertListEqual(snake_case_		,		snake_case_							)
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      str:
  											SCREAMING_SNAKE_CASE			: Any	     =self.get_image_processor()
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.get_tokenizer()
  
  											SCREAMING_SNAKE_CASE			: Any	     =PixaStructProcessor(tokenizer=snake_case_		,		image_processor=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     ='''lower newer'''
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.prepare_image_inputs()
  
  											SCREAMING_SNAKE_CASE			: str	     =processor(text=snake_case_		,		images=snake_case_							)
  
  											# 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''']							)
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =processor(text=snake_case_							)
  
  											# 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: 
  
  
  
  
  import sys
  
  sys.set_int_max_str_digits(0)  # type: ignore
  def 						lowerCAmelCase_       (	__a )       ->			bool:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =[0] * 11
  							SCREAMING_SNAKE_CASE			: str	     =[0] * 11
  
  							# mark last 9 numbers
  							for _ in range(9 ):
  														SCREAMING_SNAKE_CASE			: List[str]	     =1
  														SCREAMING_SNAKE_CASE			: List[Any]	     =number // 10
  							# flag
  							SCREAMING_SNAKE_CASE			: str	     =True
  
  							# check last 9 numbers for pandigitality
  
  							for x in range(9 ):
  														if not check_last[x + 1]:
  																					SCREAMING_SNAKE_CASE			: Dict	     =False
  							if not f:
  														return f
  
  							# mark first 9 numbers
  							SCREAMING_SNAKE_CASE			: Dict	     =int(str(__a )[:9] )
  
  							for _ in range(9 ):
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =1
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =number // 10
  
  							# check first 9 numbers for pandigitality
  
  							for x in range(9 ):
  														if not check_front[x + 1]:
  																					SCREAMING_SNAKE_CASE			: Any	     =False
  							return f
  def 						lowerCAmelCase_       (	__a )       ->			bool:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =[0] * 11
  
  							# mark last 9 numbers
  							for _ in range(9 ):
  														SCREAMING_SNAKE_CASE			: int	     =1
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =number // 10
  							# flag
  							SCREAMING_SNAKE_CASE			: Any	     =True
  
  							# check last 9 numbers for pandigitality
  
  							for x in range(9 ):
  														if not check_last[x + 1]:
  																					SCREAMING_SNAKE_CASE			: Any	     =False
  							return f
  def 						lowerCAmelCase_       (	)       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =1
  							SCREAMING_SNAKE_CASE			: List[Any]	     =1
  							SCREAMING_SNAKE_CASE			: List[Any]	     =2
  							# temporary Fibonacci numbers
  
  							SCREAMING_SNAKE_CASE			: Dict	     =1
  							SCREAMING_SNAKE_CASE			: Dict	     =1
  							SCREAMING_SNAKE_CASE			: Tuple	     =2
  							# temporary Fibonacci numbers mod 1e9
  
  							# mod m=1e9, done for fast optimisation
  							SCREAMING_SNAKE_CASE			: str	     =[0] * 1000000
  							SCREAMING_SNAKE_CASE			: Any	     =1000000000
  
  							for x in range(1000000 ):
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =(aa + ba) % m
  														SCREAMING_SNAKE_CASE			: int	     =ba % m
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =ca % m
  														if checka(__a ):
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =1
  
  							for x in range(1000000 ):
  														SCREAMING_SNAKE_CASE			: Tuple	     =a + b
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =b
  														SCREAMING_SNAKE_CASE			: List[str]	     =c
  														# perform check only if in tocheck
  														if tocheck[x + 3] and check(__a ):
  																					return x + 3  # first 2 already done
  							return -1
  
  
  if __name__ == "__main__":
  							print(F'''{solution() = }''')
  
  
  
  
  
  
  