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__ :       int						=						tempfile.mkdtemp()
  
          UpperCAmelCase__ :       Any						=						PixaStructImageProcessor()
          UpperCAmelCase__ :       Dict						=						TaTokenizer.from_pretrained("""t5-small"""  )
  
          UpperCAmelCase__ :       str						=						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__ :       Any						=						[np.random.randint(255       ,					size=(3, 30, 400)       ,					dtype=np.uinta  )]
  
          UpperCAmelCase__ :       List[str]						=						[Image.fromarray(np.moveaxis(_lowerCamelCase       ,					0       ,					-1  )  ) for x in image_inputs]
  
          return image_inputs
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						PixaStructProcessor(tokenizer=self.get_tokenizer()       ,					image_processor=self.get_image_processor()  )
          processor.save_pretrained(self.tmpdirname  )
  
          UpperCAmelCase__ :       List[str]						=						self.get_tokenizer(bos_token="""(BOS)"""       ,					eos_token="""(EOS)"""  )
          UpperCAmelCase__ :       int						=						self.get_image_processor(do_normalize=_lowerCamelCase       ,					padding_value=1.0  )
  
          UpperCAmelCase__ :       str						=						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__ :       Optional[int]						=						self.get_image_processor()
          UpperCAmelCase__ :       str						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Tuple						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Dict						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       Dict						=						image_processor(_lowerCamelCase       ,					return_tensors="""np"""  )
          UpperCAmelCase__ :       Dict						=						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__ :       Union[str, Any]						=						self.get_image_processor()
          UpperCAmelCase__ :       Dict						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Dict						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[Any]						=						'lower newer'
  
          UpperCAmelCase__ :       List[str]						=						processor(text=_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						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__ :       Union[str, Any]						=						self.get_image_processor()
          UpperCAmelCase__ :       Union[str, Any]						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Optional[Any]						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       int						=						'lower newer'
          UpperCAmelCase__ :       Optional[Any]						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       List[Any]						=						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__ :       Dict						=						self.get_image_processor()
          UpperCAmelCase__ :       Dict						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       List[Any]						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       str						=						'lower newer'
          UpperCAmelCase__ :       Dict						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       int						=						processor(text=_lowerCamelCase       ,					images=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Dict						=						[512, 1024, 2048, 4096]
          UpperCAmelCase__ :       List[Any]						=						[770, 770, 770, 770]
          # with text
          for i, max_patch in enumerate(_lowerCamelCase  ):
                UpperCAmelCase__ :       str						=						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__ :       Union[str, Any]						=						self.get_image_processor()
          UpperCAmelCase__ :       Optional[Any]						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Union[str, Any]						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       Tuple						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
          UpperCAmelCase__ :       Any						=						processor.batch_decode(_lowerCamelCase  )
          UpperCAmelCase__ :       List[Any]						=						tokenizer.batch_decode(_lowerCamelCase  )
  
          self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.get_image_processor()
          UpperCAmelCase__ :       Optional[Any]						=						self.get_tokenizer()
  
          UpperCAmelCase__ :       Optional[int]						=						PixaStructProcessor(tokenizer=_lowerCamelCase       ,					image_processor=_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						'lower newer'
          UpperCAmelCase__ :       Dict						=						self.prepare_image_inputs()
  
          UpperCAmelCase__ :       int						=						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 tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (ScoreSdeVeScheduler,)
    SCREAMING_SNAKE_CASE							=     ()
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						4
          UpperCAmelCase__ :       Tuple						=						3
          UpperCAmelCase__ :       Optional[Any]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[Any]						=						torch.rand((batch_size, num_channels, height, width)  )
  
          return sample
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						4
          UpperCAmelCase__ :       Optional[Any]						=						3
          UpperCAmelCase__ :       List[str]						=						8
          UpperCAmelCase__ :       Dict						=						8
  
          UpperCAmelCase__ :       List[str]						=						batch_size * num_channels * height * width
          UpperCAmelCase__ :       Any						=						torch.arange(_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						sample.reshape(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
          UpperCAmelCase__ :       Dict						=						sample / num_elems
          UpperCAmelCase__ :       Optional[int]						=						sample.permute(3       ,					0       ,					1       ,					2  )
  
          return sample
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          def model(_lowerCamelCase       ,					_lowerCamelCase       ,					*_lowerCamelCase  ):
                return sample * t / (t + 1)
  
          return model
  
  
  
    def    _a			(self       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						{
              """num_train_timesteps""": 2000,
              """snr""": 0.15,
              """sigma_min""": 0.01,
              """sigma_max""": 1348,
              """sampling_eps""": 1e-5,
          }
  
          config.update(**_lowerCamelCase  )
          return config
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						dict(self.forward_default_kwargs  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       int						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                UpperCAmelCase__ :       int						=						self.get_scheduler_config(**_lowerCamelCase  )
                UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Tuple						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Optional[int]						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Union[str, Any]						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self       ,					_lowerCamelCase=0       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
          kwargs.update(_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       List[str]						=						self.dummy_sample
                UpperCAmelCase__ :       List[str]						=						0.1 * sample
  
                UpperCAmelCase__ :       Optional[Any]						=						self.get_scheduler_config()
                UpperCAmelCase__ :       Optional[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						scheduler_class.from_pretrained(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Optional[int]						=						scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Any						=						new_scheduler.step_pred(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"
  
                UpperCAmelCase__ :       Optional[Any]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       Dict						=						new_scheduler.step_correct(
                    _lowerCamelCase       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler correction are not identical"
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for timesteps in [10, 100, 1000]:
                self.check_over_configs(num_train_timesteps=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,					[1, 100, 1000]  ):
                self.check_over_configs(sigma_min=_lowerCamelCase       ,					sigma_max=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for t in [0.1, 0.5, 0.75]:
                self.check_over_forward(time_step=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       int						=						self.scheduler_classes[0]
          UpperCAmelCase__ :       int						=						self.get_scheduler_config()
          UpperCAmelCase__ :       Dict						=						scheduler_class(**_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[str]						=						3
  
          UpperCAmelCase__ :       List[Any]						=						self.dummy_model()
          UpperCAmelCase__ :       int						=						self.dummy_sample_deter
  
          scheduler.set_sigmas(_lowerCamelCase  )
          scheduler.set_timesteps(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						torch.manual_seed(0  )
  
          for i, t in enumerate(scheduler.timesteps  ):
                UpperCAmelCase__ :       Tuple						=						scheduler.sigmas[i]
  
                for _ in range(scheduler.config.correct_steps  ):
                      with torch.no_grad():
                            UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
                      UpperCAmelCase__ :       List[str]						=						scheduler.step_correct(_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  ).prev_sample
  
                with torch.no_grad():
                      UpperCAmelCase__ :       Tuple						=						model(_lowerCamelCase       ,					_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					generator=_lowerCamelCase       ,					**_lowerCamelCase  )
                UpperCAmelCase__				,		UpperCAmelCase__ :       List[str]						=						output.prev_sample, output.prev_sample_mean
  
          UpperCAmelCase__ :       int						=						torch.sum(torch.abs(_lowerCamelCase  )  )
          UpperCAmelCase__ :       str						=						torch.mean(torch.abs(_lowerCamelCase  )  )
  
          assert np.isclose(result_sum.item()       ,					14_372_758_528.0  )
          assert np.isclose(result_mean.item()       ,					18_714_530.0  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						dict(self.forward_default_kwargs  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						kwargs.pop("""num_inference_steps"""       ,					_lowerCamelCase  )
  
          for scheduler_class in self.scheduler_classes:
                UpperCAmelCase__ :       Dict						=						self.get_scheduler_config()
                UpperCAmelCase__ :       List[Any]						=						scheduler_class(**_lowerCamelCase  )
  
                UpperCAmelCase__ :       str						=						self.dummy_sample
                UpperCAmelCase__ :       Tuple						=						0.1 * sample
  
                if num_inference_steps is not None and hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      scheduler.set_timesteps(_lowerCamelCase  )
                elif num_inference_steps is not None and not hasattr(_lowerCamelCase       ,					"""set_timesteps"""  ):
                      UpperCAmelCase__ :       Dict						=						num_inference_steps
  
                UpperCAmelCase__ :       str						=						scheduler.step_pred(_lowerCamelCase       ,					0       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
                UpperCAmelCase__ :       int						=						scheduler.step_pred(_lowerCamelCase       ,					1       ,					_lowerCamelCase       ,					generator=torch.manual_seed(0  )       ,					**_lowerCamelCase  ).prev_sample
  
                self.assertEqual(output_a.shape       ,					sample.shape  )
                self.assertEqual(output_a.shape       ,					output_a.shape  )
  
  