code: import torch
  
  from diffusers import HeunDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					(HeunDiscreteScheduler,)
  							lowerCAmelCase__       =					10
  
  
  
  							def     _UpperCAmelCase       (    self     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	{
  														    """num_train_timesteps""": 1100,
  														    """beta_start""": 0.0_001,
  														    """beta_end""": 0.02,
  														    """beta_schedule""": """linear""",
  														}
  
  														config.update(**UpperCAmelCase  )
  														return config
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for timesteps in [10, 50, 100, 1000]:
  																					self.check_over_configs(num_train_timesteps=UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]     ,   [0.0_002, 0.002, 0.02]  ):
  																					self.check_over_configs(beta_start=UpperCAmelCase     ,   beta_end=UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for schedule in ["linear", "scaled_linear", "exp"]:
  																					self.check_over_configs(beta_schedule=UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for clip_sample_range in [1.0, 2.0, 3.0]:
  																					self.check_over_configs(clip_sample_range=UpperCAmelCase     ,   clip_sample=UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														for prediction_type in ["epsilon", "v_prediction", "sample"]:
  																					self.check_over_configs(prediction_type=UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.scheduler_classes[0]
  														_lowercase				=	self.get_scheduler_config()
  														_lowercase				=	scheduler_class(**UpperCAmelCase  )
  
  														scheduler.set_timesteps(self.num_inference_steps  )
  
  														_lowercase				=	self.dummy_model()
  														_lowercase				=	self.dummy_sample_deter * scheduler.init_noise_sigma
  														_lowercase				=	sample.to(UpperCAmelCase  )
  
  														for i, t in enumerate(scheduler.timesteps  ):
  																					_lowercase				=	scheduler.scale_model_input(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	model(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	scheduler.step(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  )
  																					_lowercase				=	output.prev_sample
  
  														_lowercase				=	torch.sum(torch.abs(UpperCAmelCase  )  )
  														_lowercase				=	torch.mean(torch.abs(UpperCAmelCase  )  )
  
  														if torch_device in ["cpu", "mps"]:
  																					assert abs(result_sum.item() - 0.1_233  ) < 1e-2
  																					assert abs(result_mean.item() - 0.0_002  ) < 1e-3
  														else:
  																					# CUDA
  																					assert abs(result_sum.item() - 0.1_233  ) < 1e-2
  																					assert abs(result_mean.item() - 0.0_002  ) < 1e-3
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.scheduler_classes[0]
  														_lowercase				=	self.get_scheduler_config(prediction_type="""v_prediction"""  )
  														_lowercase				=	scheduler_class(**UpperCAmelCase  )
  
  														scheduler.set_timesteps(self.num_inference_steps  )
  
  														_lowercase				=	self.dummy_model()
  														_lowercase				=	self.dummy_sample_deter * scheduler.init_noise_sigma
  														_lowercase				=	sample.to(UpperCAmelCase  )
  
  														for i, t in enumerate(scheduler.timesteps  ):
  																					_lowercase				=	scheduler.scale_model_input(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	model(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	scheduler.step(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  )
  																					_lowercase				=	output.prev_sample
  
  														_lowercase				=	torch.sum(torch.abs(UpperCAmelCase  )  )
  														_lowercase				=	torch.mean(torch.abs(UpperCAmelCase  )  )
  
  														if torch_device in ["cpu", "mps"]:
  																					assert abs(result_sum.item() - 4.6934e-07  ) < 1e-2
  																					assert abs(result_mean.item() - 6.1112e-10  ) < 1e-3
  														else:
  																					# CUDA
  																					assert abs(result_sum.item() - 4.693_4286_5017_0972e-07  ) < 1e-2
  																					assert abs(result_mean.item() - 0.0_002  ) < 1e-3
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.scheduler_classes[0]
  														_lowercase				=	self.get_scheduler_config()
  														_lowercase				=	scheduler_class(**UpperCAmelCase  )
  
  														scheduler.set_timesteps(self.num_inference_steps     ,   device=UpperCAmelCase  )
  
  														_lowercase				=	self.dummy_model()
  														_lowercase				=	self.dummy_sample_deter.to(UpperCAmelCase  ) * scheduler.init_noise_sigma
  
  														for t in scheduler.timesteps:
  																					_lowercase				=	scheduler.scale_model_input(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	model(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	scheduler.step(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  )
  																					_lowercase				=	output.prev_sample
  
  														_lowercase				=	torch.sum(torch.abs(UpperCAmelCase  )  )
  														_lowercase				=	torch.mean(torch.abs(UpperCAmelCase  )  )
  
  														if str(UpperCAmelCase  ).startswith("""cpu"""  ):
  																					# The following sum varies between 148 and 156 on mps. Why?
  																					assert abs(result_sum.item() - 0.1_233  ) < 1e-2
  																					assert abs(result_mean.item() - 0.0_002  ) < 1e-3
  														elif str(UpperCAmelCase  ).startswith("""mps"""  ):
  																					# Larger tolerance on mps
  																					assert abs(result_mean.item() - 0.0_002  ) < 1e-2
  														else:
  																					# CUDA
  																					assert abs(result_sum.item() - 0.1_233  ) < 1e-2
  																					assert abs(result_mean.item() - 0.0_002  ) < 1e-3
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	self.scheduler_classes[0]
  														_lowercase				=	self.get_scheduler_config()
  														_lowercase				=	scheduler_class(**UpperCAmelCase     ,   use_karras_sigmas=UpperCAmelCase  )
  
  														scheduler.set_timesteps(self.num_inference_steps     ,   device=UpperCAmelCase  )
  
  														_lowercase				=	self.dummy_model()
  														_lowercase				=	self.dummy_sample_deter.to(UpperCAmelCase  ) * scheduler.init_noise_sigma
  														_lowercase				=	sample.to(UpperCAmelCase  )
  
  														for t in scheduler.timesteps:
  																					_lowercase				=	scheduler.scale_model_input(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	model(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	scheduler.step(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  )
  																					_lowercase				=	output.prev_sample
  
  														_lowercase				=	torch.sum(torch.abs(UpperCAmelCase  )  )
  														_lowercase				=	torch.mean(torch.abs(UpperCAmelCase  )  )
  
  														assert abs(result_sum.item() - 0.00_015  ) < 1e-2
  														assert abs(result_mean.item() - 1.9869_5545_3503_4695e-07  ) < 1e-2
  
  
  
style_context: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
  				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					c_float
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return sizeof(self.value  )
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A ,_A ,_A = 0					):
  
  
  
  							"""simple docstring"""
  
  
  							if dimension <= 0:
  														_lowercase				=	fixed_dimension
  
  							dimension -= num_token_to_add
  							return dimension
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  							try:
  														return AutoProcessor.from_pretrained(_A					)
  							except (ValueError, OSError, KeyError):
  														_lowercase				,       _lowercase				=	None, None
  														try:
  																					_lowercase				=	AutoTokenizer.from_pretrained(_A					)
  														except (OSError, KeyError):
  																					pass
  														try:
  																					_lowercase				=	AutoFeatureExtractor.from_pretrained(_A					)
  														except (OSError, KeyError):
  																					pass
  
  														if tokenizer is not None and feature_extractor is not None:
  																					raise ValueError(
  																					    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''					)
  														elif tokenizer is None and feature_extractor is None:
  																					return None
  														elif tokenizer is not None:
  																					return tokenizer
  														else:
  																					return feature_extractor
  
  
  