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:
   UpperCAmelCase					=				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 					__snake_case					(			self       :		Optional[int]  ) ->							Tuple:
          _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 					__snake_case					(			self       :		Tuple       ,			**lowerCamelCase       :		int  ) ->							str:
          return AutoProcessor.from_pretrained(self.tmpdirname       ,			**lowerCamelCase  ).tokenizer
  
   def 					__snake_case					(			self       :		List[str]       ,			**lowerCamelCase       :		Optional[int]  ) ->							str:
          return AutoProcessor.from_pretrained(self.tmpdirname       ,			**lowerCamelCase  ).image_processor
  
   def 					__snake_case					(			self       :		Any  ) ->							Optional[Any]:
          shutil.rmtree(self.tmpdirname  )
  
   def 					__snake_case					(			self       :		Any  ) ->							Dict:
          _UpperCamelCase     :       Any  						=  [np.random.randint(2_5_5       ,			size=(3, 3_0, 4_0_0)       ,			dtype=np.uinta  )]
  
          _UpperCamelCase     :       List[str]  						=  [Image.fromarray(np.moveaxis(lowerCamelCase       ,			0       ,			-1  )  ) for x in image_inputs]
  
          return image_inputs
  
   def 					__snake_case					(			self       :		List[str]  ) ->							int:
          _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 					__snake_case					(			self       :		Any  ) ->							Dict:
          _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 					__snake_case					(			self       :		int  ) ->							Tuple:
          _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 					__snake_case					(			self       :		Any  ) ->							Optional[Any]:
          _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 					__snake_case					(			self       :		int  ) ->							Union[str, Any]:
          _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  						=  [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
          _UpperCamelCase     :       List[Any]  						=  [7_7_0, 7_7_0, 7_7_0, 7_7_0]
          # 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 					__snake_case					(			self       :		Union[str, Any]  ) ->							List[Any]:
          _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 					__snake_case					(			self       :		int  ) ->							Optional[int]:
          _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: import tempfile
  import unittest
  
  import torch
  
  from diffusers import UNetaDConditionModel
  from diffusers.training_utils import EMAModel
  from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device
  
  
  enable_full_determinism()
  
  
  
  
  class    __lowerCAmelCase  (       unittest.TestCase						):
  
  
  
   '''simple docstring'''
   _lowerCamelCase						:	str				    = 'hf-internal-testing/tiny-stable-diffusion-pipe'
   _lowerCamelCase						:	int				    = 1
   _lowerCamelCase						:	Any				    = 77
   _lowerCamelCase						:	List[Any]				    = 32
   _lowerCamelCase						:	List[str]				    = 4
   _lowerCamelCase						:	Dict				    = 64
   _lowerCamelCase						:	List[str]				    = torch.manual_seed(0						)
  
   def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		Union[str, Any]=0.9_9_9_9  ) ->							Dict:
          _UpperCamelCase     :       int  						=  UNetaDConditionModel.from_pretrained(self.model_id       ,			subfolder='unet'  )
          _UpperCamelCase     :       Any  						=  unet.to(lowerCamelCase  )
          _UpperCamelCase     :       Union[str, Any]  						=  EMAModel(unet.parameters()       ,			decay=lowerCamelCase       ,			model_cls=lowerCamelCase       ,			model_config=unet.config  )
          return unet, ema_unet
  
   def 					__snake_case					(			self       :		Tuple  ) ->							Union[str, Any]:
          _UpperCamelCase     :       Tuple  						=  torch.randn(
              self.batch_size       ,			self.num_in_channels       ,			self.latent_height       ,			self.latent_width       ,			generator=self.generator  ).to(lowerCamelCase  )
          _UpperCamelCase     :       Union[str, Any]  						=  torch.randint(0       ,			1_0_0_0       ,			size=(self.batch_size,)       ,			generator=self.generator  ).to(lowerCamelCase  )
          _UpperCamelCase     :       int  						=  torch.randn(
              self.batch_size       ,			self.prompt_length       ,			self.text_encoder_hidden_dim       ,			generator=self.generator  ).to(lowerCamelCase  )
          return noisy_latents, timesteps, encoder_hidden_states
  
   def 					__snake_case					(			self       :		str       ,			lowerCamelCase       :		List[Any]  ) ->							List[Any]:
          _UpperCamelCase     :       int  						=  {}
          for k, param in unet.state_dict().items():
                 _UpperCamelCase     :       Optional[Any]  						=  torch.randn_like(lowerCamelCase  ) + (param * torch.randn_like(lowerCamelCase  ))
                 updated_state_dict.update({k: updated_param}  )
          unet.load_state_dict(lowerCamelCase  )
          return unet
  
   def 					__snake_case					(			self       :		List[Any]  ) ->							Tuple:
          _UpperCamelCase						,      _UpperCamelCase     :       Union[str, Any]  						=  self.get_models()
          # Take the first (hypothetical) EMA step.
          ema_unet.step(unet.parameters()  )
          assert ema_unet.optimization_step == 1
  
          # Take two more.
          for _ in range(2  ):
                 ema_unet.step(unet.parameters()  )
          assert ema_unet.optimization_step == 3
  
   def 					__snake_case					(			self       :		int  ) ->							Any:
          _UpperCamelCase						,      _UpperCamelCase     :       Any  						=  self.get_models()
          # Since the `unet` is not being updated (i.e., backprop'd)
          # there won't be any difference between the `params` of `unet`
          # and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
          ema_unet.step(unet.parameters()  )
          _UpperCamelCase     :       List[Any]  						=  list(unet.parameters()  )
          for s_param, param in zip(ema_unet.shadow_params       ,			lowerCamelCase  ):
                 assert torch.allclose(lowerCamelCase       ,			lowerCamelCase  )
  
          # The above holds true even if we call `ema.step()` multiple times since
          # `unet` params are still not being updated.
          for _ in range(4  ):
                 ema_unet.step(unet.parameters()  )
          for s_param, param in zip(ema_unet.shadow_params       ,			lowerCamelCase  ):
                 assert torch.allclose(lowerCamelCase       ,			lowerCamelCase  )
  
   def 					__snake_case					(			self       :		Dict  ) ->							Dict:
          _UpperCamelCase						,      _UpperCamelCase     :       Dict  						=  self.get_models()
          # Here we simulate the parameter updates for `unet`. Since there might
          # be some parameters which are initialized to zero we take extra care to
          # initialize their values to something non-zero before the multiplication.
          _UpperCamelCase     :       str  						=  self.simulate_backprop(lowerCamelCase  )
  
          # Take the EMA step.
          ema_unet.step(unet_pseudo_updated_step_one.parameters()  )
  
          # Now the EMA'd parameters won't be equal to the original model parameters.
          _UpperCamelCase     :       Optional[Any]  						=  list(unet_pseudo_updated_step_one.parameters()  )
          for s_param, param in zip(ema_unet.shadow_params       ,			lowerCamelCase  ):
                 assert ~torch.allclose(lowerCamelCase       ,			lowerCamelCase  )
  
          # Ensure this is the case when we take multiple EMA steps.
          for _ in range(4  ):
                 ema_unet.step(unet.parameters()  )
          for s_param, param in zip(ema_unet.shadow_params       ,			lowerCamelCase  ):
                 assert ~torch.allclose(lowerCamelCase       ,			lowerCamelCase  )
  
   def 					__snake_case					(			self       :		Union[str, Any]  ) ->							Tuple:
          # If we call EMA step after a backpropagation consecutively for two times,
          # the shadow params from those two steps should be different.
          _UpperCamelCase						,      _UpperCamelCase     :       Dict  						=  self.get_models()
  
          # First backprop + EMA
          _UpperCamelCase     :       Optional[Any]  						=  self.simulate_backprop(lowerCamelCase  )
          ema_unet.step(unet_step_one.parameters()  )
          _UpperCamelCase     :       Optional[Any]  						=  ema_unet.shadow_params
  
          # Second backprop + EMA
          _UpperCamelCase     :       Optional[int]  						=  self.simulate_backprop(lowerCamelCase  )
          ema_unet.step(unet_step_two.parameters()  )
          _UpperCamelCase     :       List[str]  						=  ema_unet.shadow_params
  
          for step_one, step_two in zip(lowerCamelCase       ,			lowerCamelCase  ):
                 assert ~torch.allclose(lowerCamelCase       ,			lowerCamelCase  )
  
   def 					__snake_case					(			self       :		str  ) ->							int:
          # If there's no decay even if there are backprops, EMA steps
          # won't take any effect i.e., the shadow params would remain the
          # same.
          _UpperCamelCase						,      _UpperCamelCase     :       List[str]  						=  self.get_models(decay=0.0  )
          _UpperCamelCase     :       Optional[Any]  						=  self.simulate_backprop(lowerCamelCase  )
          ema_unet.step(unet_step_one.parameters()  )
          _UpperCamelCase     :       Dict  						=  ema_unet.shadow_params
  
          _UpperCamelCase     :       List[str]  						=  self.simulate_backprop(lowerCamelCase  )
          ema_unet.step(unet_step_two.parameters()  )
          _UpperCamelCase     :       Union[str, Any]  						=  ema_unet.shadow_params
  
          for step_one, step_two in zip(lowerCamelCase       ,			lowerCamelCase  ):
                 assert torch.allclose(lowerCamelCase       ,			lowerCamelCase  )
  
   @skip_mps
   def 					__snake_case					(			self       :		Dict  ) ->							str:
          _UpperCamelCase						,      _UpperCamelCase     :       Optional[Any]  						=  self.get_models()
          _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase     :       Any  						=  self.get_dummy_inputs()
  
          with tempfile.TemporaryDirectory() as tmpdir:
                 ema_unet.save_pretrained(lowerCamelCase  )
                 _UpperCamelCase     :       List[str]  						=  UNetaDConditionModel.from_pretrained(lowerCamelCase       ,			model_cls=lowerCamelCase  )
                 _UpperCamelCase     :       Dict  						=  loaded_unet.to(unet.device  )
  
          # Since no EMA step has been performed the outputs should match.
          _UpperCamelCase     :       Optional[Any]  						=  unet(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  ).sample
          _UpperCamelCase     :       List[str]  						=  loaded_unet(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  ).sample
  
          assert torch.allclose(lowerCamelCase       ,			lowerCamelCase       ,			atol=1e-4  )
  
  
  
  