code: 
  
  
  
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers import ScoreSdeVeScheduler
  
  
  
  
  
  class        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		(ScoreSdeVeScheduler,)
  							snake_case_        =		()
  
  
  							@property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		4
  													a_					=		3
  													a_					=		8
  													a_					=		8
  
  													a_					=		torch.rand((batch_size, num_channels, height, width)		)
  
  													return sample
  
  
  							@property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		4
  													a_					=		3
  													a_					=		8
  													a_					=		8
  
  													a_					=		batch_size * num_channels * height * width
  													a_					=		torch.arange(_UpperCAmelCase		)
  													a_					=		sample.reshape(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													a_					=		sample / num_elems
  													a_					=		sample.permute(3			,    0			,    1			,    2		)
  
  													return sample
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													def model(_UpperCAmelCase			,    _UpperCAmelCase			,    *_UpperCAmelCase		):
  																			return sample * t / (t + 1)
  
  													return model
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		{
  													    """num_train_timesteps""": 2_000,
  													    """snr""": 0.1_5,
  													    """sigma_min""": 0.0_1,
  													    """sigma_max""": 1_348,
  													    """sampling_eps""": 1e-5,
  													}
  
  													config.update(**_UpperCAmelCase		)
  													return config
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=0			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		dict(self.forward_default_kwargs		)
  
  													for scheduler_class in self.scheduler_classes:
  																			a_					=		self.dummy_sample
  																			a_					=		0.1 * sample
  
  																			a_					=		self.get_scheduler_config(**_UpperCAmelCase		)
  																			a_					=		scheduler_class(**_UpperCAmelCase		)
  
  																			with tempfile.TemporaryDirectory() as tmpdirname:
  																									scheduler.save_config(_UpperCAmelCase		)
  																									a_					=		scheduler_class.from_pretrained(_UpperCAmelCase		)
  
  																			a_					=		scheduler.step_pred(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  																			a_					=		new_scheduler.step_pred(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  
  																			assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
  
  																			a_					=		scheduler.step_correct(_UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  																			a_					=		new_scheduler.step_correct(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  
  																			assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=0			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		dict(self.forward_default_kwargs		)
  													kwargs.update(_UpperCAmelCase		)
  
  													for scheduler_class in self.scheduler_classes:
  																			a_					=		self.dummy_sample
  																			a_					=		0.1 * sample
  
  																			a_					=		self.get_scheduler_config()
  																			a_					=		scheduler_class(**_UpperCAmelCase		)
  
  																			with tempfile.TemporaryDirectory() as tmpdirname:
  																									scheduler.save_config(_UpperCAmelCase		)
  																									a_					=		scheduler_class.from_pretrained(_UpperCAmelCase		)
  
  																			a_					=		scheduler.step_pred(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  																			a_					=		new_scheduler.step_pred(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  
  																			assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
  
  																			a_					=		scheduler.step_correct(_UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  																			a_					=		new_scheduler.step_correct(
  																			    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  
  																			assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for timesteps in [10, 100, 1_000]:
  																			self.check_over_configs(num_train_timesteps=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]			,    [1, 100, 1_000]		):
  																			self.check_over_configs(sigma_min=_UpperCAmelCase			,    sigma_max=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for t in [0.1, 0.5, 0.7_5]:
  																			self.check_over_forward(time_step=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		dict(self.forward_default_kwargs		)
  
  													a_					=		self.scheduler_classes[0]
  													a_					=		self.get_scheduler_config()
  													a_					=		scheduler_class(**_UpperCAmelCase		)
  
  													a_					=		3
  
  													a_					=		self.dummy_model()
  													a_					=		self.dummy_sample_deter
  
  													scheduler.set_sigmas(_UpperCAmelCase		)
  													scheduler.set_timesteps(_UpperCAmelCase		)
  													a_					=		torch.manual_seed(0		)
  
  													for i, t in enumerate(scheduler.timesteps		):
  																			a_					=		scheduler.sigmas[i]
  
  																			for _ in range(scheduler.config.correct_steps		):
  																									with torch.no_grad():
  																															a_					=		model(_UpperCAmelCase			,    _UpperCAmelCase		)
  																									a_					=		scheduler.step_correct(_UpperCAmelCase			,    _UpperCAmelCase			,    generator=_UpperCAmelCase			,    **_UpperCAmelCase		).prev_sample
  
  																			with torch.no_grad():
  																									a_					=		model(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  																			a_					=		scheduler.step_pred(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    generator=_UpperCAmelCase			,    **_UpperCAmelCase		)
  																			a_			,   a_					=		output.prev_sample, output.prev_sample_mean
  
  													a_					=		torch.sum(torch.abs(_UpperCAmelCase		)		)
  													a_					=		torch.mean(torch.abs(_UpperCAmelCase		)		)
  
  													assert np.isclose(result_sum.item()			,    14_372_758_528.0		)
  													assert np.isclose(result_mean.item()			,    18_714_530.0		)
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		dict(self.forward_default_kwargs		)
  
  													a_					=		kwargs.pop("""num_inference_steps"""			,    _UpperCAmelCase		)
  
  													for scheduler_class in self.scheduler_classes:
  																			a_					=		self.get_scheduler_config()
  																			a_					=		scheduler_class(**_UpperCAmelCase		)
  
  																			a_					=		self.dummy_sample
  																			a_					=		0.1 * sample
  
  																			if num_inference_steps is not None and hasattr(_UpperCAmelCase			,    """set_timesteps"""		):
  																									scheduler.set_timesteps(_UpperCAmelCase		)
  																			elif num_inference_steps is not None and not hasattr(_UpperCAmelCase			,    """set_timesteps"""		):
  																									a_					=		num_inference_steps
  
  																			a_					=		scheduler.step_pred(_UpperCAmelCase			,    0			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  																			a_					=		scheduler.step_pred(_UpperCAmelCase			,    1			,    _UpperCAmelCase			,    generator=torch.manual_seed(0		)			,    **_UpperCAmelCase		).prev_sample
  
  																			self.assertEqual(output_a.shape			,    sample.shape		)
  																			self.assertEqual(output_a.shape			,    output_a.shape		)
style_context: 
  
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
  	import torch
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		['''image_processor''', '''tokenizer''']
  							snake_case_        =		'''OneFormerImageProcessor'''
  							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')
  
  
  							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if image_processor is None:
  																			raise ValueError("""You need to specify an `image_processor`."""		)
  													if tokenizer is None:
  																			raise ValueError("""You need to specify a `tokenizer`."""		)
  
  													a_					=		max_seq_length
  													a_					=		task_seq_length
  
  													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if text_list is None:
  																			raise ValueError("""tokens cannot be None."""		)
  
  													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)
  
  													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]
  
  													a_					=		[]
  													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
  																			token_inputs.append(token.unsqueeze(0		)		)
  
  													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  													return token_inputs
  
  
  							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)