





"""simple docstring"""






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	(UnCLIPScheduler,)







					def 			_lowerCamelCase		(    self						,      **_UpperCAmelCase  ):
									__a			:    List[Any]      							=	{
									    '''num_train_timesteps''': 1000,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**_UpperCAmelCase  )
									return config







					def 			_lowerCamelCase		(    self  ):
									for timesteps in [1, 5, 100, 1000]:
													self.check_over_configs(num_train_timesteps=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for variance in ["fixed_small_log", "learned_range"]:
													self.check_over_configs(variance_type=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for clip_sample_range in [1, 5, 10, 20]:
													self.check_over_configs(clip_sample_range=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(prediction_type=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									for time_step in [0, 500, 999]:
													for prev_timestep in [None, 5, 100, 250, 500, 750]:
																	if prev_timestep is not None and prev_timestep >= time_step:
																					continue

																	self.check_over_forward(time_step=_UpperCAmelCase						,      prev_timestep=_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Dict      							=	self.scheduler_classes[0]
									__a			:    Any      							=	self.get_scheduler_config(variance_type='''fixed_small_log'''  )
									__a			:    Optional[Any]      							=	scheduler_class(**_UpperCAmelCase  )

									assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 1.0_0_0_0e-1_0  )  ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.0_5_4_9_6_2_5  )  ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.9_9_9_4_9_8_7  )  ) < 1e-5







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[str]      							=	self.scheduler_classes[0]
									__a			:    Any      							=	self.get_scheduler_config(variance_type='''learned_range'''  )
									__a			:    Optional[int]      							=	scheduler_class(**_UpperCAmelCase  )

									__a			:    int      							=	0.5

									assert scheduler._get_variance(1						,      predicted_variance=_UpperCAmelCase  ) - -1_0.1_7_1_2_7_9_0 < 1e-5
									assert scheduler._get_variance(487						,      predicted_variance=_UpperCAmelCase  ) - -5.7_9_9_8_0_5_2 < 1e-5
									assert scheduler._get_variance(999						,      predicted_variance=_UpperCAmelCase  ) - -0.0_0_1_0_0_1_1 < 1e-5







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[int]      							=	self.scheduler_classes[0]
									__a			:    Dict      							=	self.get_scheduler_config()
									__a			:    List[str]      							=	scheduler_class(**_UpperCAmelCase  )

									__a			:    Dict      							=	scheduler.timesteps

									__a			:    Optional[int]      							=	self.dummy_model()
									__a			:    Any      							=	self.dummy_sample_deter
									__a			:    Optional[int]      							=	torch.manual_seed(0  )

									for i, t in enumerate(_UpperCAmelCase  ):
													# 1. predict noise residual
													__a			:    List[Any]      							=	model(_UpperCAmelCase						,      _UpperCAmelCase  )

													# 2. predict previous mean of sample x_t-1
													__a			:    Dict      							=	scheduler.step(_UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      generator=_UpperCAmelCase  ).prev_sample

													__a			:    Tuple      							=	pred_prev_sample

									__a			:    Optional[Any]      							=	torch.sum(torch.abs(_UpperCAmelCase  )  )
									__a			:    Any      							=	torch.mean(torch.abs(_UpperCAmelCase  )  )

									assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5  ) < 1e-2
									assert abs(result_mean.item() - 0.3_2_8_4_7_4_3  ) < 1e-3







					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	self.scheduler_classes[0]
									__a			:    str      							=	self.get_scheduler_config()
									__a			:    Union[str, Any]      							=	scheduler_class(**_UpperCAmelCase  )

									scheduler.set_timesteps(25  )

									__a			:    Tuple      							=	scheduler.timesteps

									__a			:    Any      							=	self.dummy_model()
									__a			:    Optional[Any]      							=	self.dummy_sample_deter
									__a			:    Optional[int]      							=	torch.manual_seed(0  )

									for i, t in enumerate(_UpperCAmelCase  ):
													# 1. predict noise residual
													__a			:    List[Any]      							=	model(_UpperCAmelCase						,      _UpperCAmelCase  )

													if i + 1 == timesteps.shape[0]:
																	__a			:    List[Any]      							=	None
													else:
																	__a			:    str      							=	timesteps[i + 1]

													# 2. predict previous mean of sample x_t-1
													__a			:    List[str]      							=	scheduler.step(
													    _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase						,      prev_timestep=_UpperCAmelCase						,      generator=_UpperCAmelCase  ).prev_sample

													__a			:    Tuple      							=	pred_prev_sample

									__a			:    Any      							=	torch.sum(torch.abs(_UpperCAmelCase  )  )
									__a			:    Dict      							=	torch.mean(torch.abs(_UpperCAmelCase  )  )

									assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3  ) < 1e-2
									assert abs(result_mean.item() - 0.3_3_6_2_0_3_8  ) < 1e-3







					def 			_lowerCamelCase		(    self  ):
									pass







					def 			_lowerCamelCase		(    self  ):
									pass
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCAmelCase       =		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCAmelCase       =		"    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"
class     lowerCAmelCase_     (       unittest.TestCase ):







							def  UpperCamelCase_						(   self      :     List[Any]		):
												_UpperCamelCase			     =     tempfile.mkdtemp()
												os.makedirs(os.path.join(self.diffusers_dir ,			'''schedulers/'''		)		)
												_UpperCamelCase			     =     self.diffusers_dir
												shutil.copy(
												    os.path.join(_A ,			'''src/diffusers/schedulers/scheduling_ddpm.py'''		) ,			os.path.join(self.diffusers_dir ,			'''schedulers/scheduling_ddpm.py'''		) ,			)







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     '''src/diffusers'''
												shutil.rmtree(self.diffusers_dir		)







							def  UpperCamelCase_						(   self      :     str ,			_A      :     List[str] ,			_A      :     Optional[Any] ,			_A      :     List[str] ,			_A      :     Optional[int]=None		):
												_UpperCamelCase			     =     comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
												if overwrite_result is not None:
																	_UpperCamelCase			     =     comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
												_UpperCamelCase			     =     black.Mode(target_versions={black.TargetVersion.PYaa} ,			line_length=119		)
												_UpperCamelCase			     =     black.format_str(_A ,			mode=_A		)
												_UpperCamelCase			     =     os.path.join(self.diffusers_dir ,			'''new_code.py'''		)
												with open(_A ,			'''w''' ,			newline='''\n'''		) as f:
																	f.write(_A		)
												if overwrite_result is None:
																	self.assertTrue(len(check_copies.is_copy_consistent(_A		)		) == 0		)
												else:
																	check_copies.is_copy_consistent(f.name ,			overwrite=_A		)
																	with open(_A ,			'''r'''		) as f:
																						self.assertTrue(f.read() ,			_A		)







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''		)
												self.assertEqual(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     List[str]		):
												# Base copy consistency
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,			'''DDPMSchedulerOutput''' ,			REFERENCE_CODE + '''\n''' ,			)

												# With no empty line at the end
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput''' ,			'''DDPMSchedulerOutput''' ,			_A ,			)

												# Copy consistency with rename
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,			'''TestSchedulerOutput''' ,			re.sub('''DDPM''' ,			'''Test''' ,			_A		) ,			)

												# Copy consistency with a really long name
												_UpperCamelCase			     =     '''TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
												self.check_copy_consistency(
												    F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" ,			F"""{long_class_name}SchedulerOutput""" ,			re.sub('''Bert''' ,			_A ,			_A		) ,			)

												# Copy consistency with overwrite
												self.check_copy_consistency(
												    '''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test''' ,			'''TestSchedulerOutput''' ,			_A ,			overwrite_result=re.sub('''DDPM''' ,			'''Test''' ,			_A		) ,			)
