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      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						tempfile.mkdtemp()
  
          UpperCAmelCase__ :       Tuple						=						PixaStructImageProcessor()
          UpperCAmelCase__ :       Optional[Any]						=						TaTokenizer.from_pretrained("""t5-small"""  )
  
          UpperCAmelCase__ :       Any						=						PixaStructProcessor(_lowerCamelCase       ,					_lowerCamelCase  )
  
          processor.save_pretrained(self.tmpdirname  )
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return AutoProcessor.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  ).tokenizer
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return AutoProcessor.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  ).image_processor
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          shutil.rmtree(self.tmpdirname  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						[np.random.randint(255       ,					size=(3, 30, 400)       ,					dtype=np.uinta  )]
  
          UpperCAmelCase__ :       Dict						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,					0       ,					-1  )  ) for x in image_inputs]
  
          return image_inputs
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						PixaStructProcessor(tokenizer=self.get_tokenizer()       ,					image_processor=self.get_image_processor()  )
          processor.save_pretrained(self.tmpdirname  )
  
          UpperCAmelCase__ :       str						=						self.get_tokenizer(bos_token="""(BOS)"""       ,					eos_token="""(EOS)"""  )
          UpperCAmelCase__ :       int						=						self.get_image_processor(do_normalize=_lowerCamelCase       ,					padding_value=1.0  )
  
          UpperCAmelCase__ :       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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						self.get_image_processor()
          UpperCAmelCase__ :       List[str]						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       str						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[Any]						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       List[str]						=						image_processor(_lowerCamelCase       ,					return_tensors="""np"""  )
          UpperCAmelCase__ :       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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						self.get_image_processor()
          UpperCAmelCase__ :       Optional[Any]						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Union[str, Any]						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[Any]						=						"""lower newer"""
  
          UpperCAmelCase__ :       List[Any]						=						processor(text=_lowerCamelCase  )
  
          UpperCAmelCase__ :       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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						self.get_image_processor()
          UpperCAmelCase__ :       Optional[Any]						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       int						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[int]						=						"""lower newer"""
          UpperCAmelCase__ :       Union[str, Any]						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						self.get_image_processor()
          UpperCAmelCase__ :       Dict						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Dict						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[Any]						=						"""lower newer"""
          UpperCAmelCase__ :       Optional[int]						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       Tuple						=						processor(text=_lowerCamelCase       ,					images=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[Any]						=						[512, 1024, 2048, 4096]
          UpperCAmelCase__ :       List[str]						=						[770, 770, 770, 770]
          # with text
          for i, max_patch in enumerate(_lowerCamelCase  ):
                UpperCAmelCase__ :       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  ):
                UpperCAmelCase__ :       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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						self.get_image_processor()
          UpperCAmelCase__ :       List[Any]						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Tuple						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Optional[int]						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
          UpperCAmelCase__ :       int						=						processor.batch_decode(_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						tokenizer.batch_decode(_lowerCamelCase  )
  
          self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						self.get_image_processor()
          UpperCAmelCase__ :       Any						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Optional[Any]						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Dict						=						"""lower newer"""
          UpperCAmelCase__ :       Dict						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       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"""]  )
  
          UpperCAmelCase__ :       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: 
  
  """simple docstring"""
  import requests
  
  try:
      from django.contrib.auth import authenticate, login
      from django.shortcuts import redirect, render
  except ImportError:
      _A						=     _A						=     _A						=     _A						=     print
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   str:
        # Enter your recaptcha secret key here
        UpperCAmelCase__ :       int						=						"""secretKey"""
        UpperCAmelCase__ :       Any						=						"""https://www.google.com/recaptcha/api/siteverify"""
  
        # when method is not POST, direct user to login page
        if request.method != "POST":
              return render(lowerCAmelCase ,  """login.html"""						)
  
        # from the frontend, get username, password, and client_key
        UpperCAmelCase__ :       List[Any]						=						request.POST.get("""username"""						)
        UpperCAmelCase__ :       Optional[Any]						=						request.POST.get("""password"""						)
        UpperCAmelCase__ :       List[str]						=						request.POST.get("""g-recaptcha-response"""						)
  
        # post recaptcha response to Google's recaptcha api
        UpperCAmelCase__ :       Any						=						requests.post(lowerCAmelCase ,  data={"""secret""": secret_key, """response""": client_key}						)
        # if the recaptcha api verified our keys
        if response.json().get("""success""" ,  lowerCAmelCase						):
              # authenticate the user
              UpperCAmelCase__ :       List[str]						=						authenticate(lowerCAmelCase ,  username=lowerCAmelCase ,  password=lowerCAmelCase						)
              if user_in_database:
                    login(lowerCAmelCase ,  lowerCAmelCase						)
                    return redirect("""/your-webpage"""						)
        return render(lowerCAmelCase ,  """login.html"""						)
  
  