



'''simple docstring'''

import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Optional[int]           = (DDPMParallelScheduler,)







							def 							__lowercase      (							self :	Optional[Any]     ,**_UpperCAmelCase :	Dict		):
									_a					:				int				=							{
									    'num_train_timesteps': 1000,
									    'beta_start': 0.00_01,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'variance_type': 'fixed_small',
									    'clip_sample': True,
									}

									config.update(**_UpperCAmelCase		)
									return config







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







							def 							__lowercase      (							self :	Union[str, Any]		):
									for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,[0.0_02, 0.02, 0.2, 2]		):
											self.check_over_configs(beta_start=_UpperCAmelCase     ,beta_end=_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[str]		):
									for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any		):
									for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=_UpperCAmelCase		)







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







							def 							__lowercase      (							self :	Tuple		):
									self.check_over_configs(thresholding=_UpperCAmelCase		)
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=_UpperCAmelCase     ,prediction_type=_UpperCAmelCase     ,sample_max_value=_UpperCAmelCase     ,)







							def 							__lowercase      (							self :	Union[str, Any]		):
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(prediction_type=_UpperCAmelCase		)







							def 							__lowercase      (							self :	List[str]		):
									for t in [0, 500, 999]:
											self.check_over_forward(time_step=_UpperCAmelCase		)







							def 							__lowercase      (							self :	Dict		):
									_a					:				Union[str, Any]				=							self.scheduler_classes[0]
									_a					:				List[str]				=							self.get_scheduler_config()
									_a					:				Union[str, Any]				=							scheduler_class(**_UpperCAmelCase		)

									assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_09_79		)		) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5







							def 							__lowercase      (							self :	str		):
									_a					:				Tuple				=							self.scheduler_classes[0]
									_a					:				List[Any]				=							self.get_scheduler_config()
									_a					:				str				=							scheduler_class(**_UpperCAmelCase		)

									_a					:				Dict				=							len(_UpperCAmelCase		)

									_a					:				Any				=							self.dummy_model()
									_a					:				str				=							self.dummy_sample_deter
									_a					:				Dict				=							self.dummy_sample_deter + 0.1
									_a					:				Tuple				=							self.dummy_sample_deter - 0.1

									_a					:				Dict				=							samplea.shape[0]
									_a					:				str				=							torch.stack([samplea, samplea, samplea]     ,dim=0		)
									_a					:				Dict				=							torch.arange(_UpperCAmelCase		)[0:3, None].repeat(1     ,_UpperCAmelCase		)

									_a					:				List[Any]				=							model(samples.flatten(0     ,1		)     ,timesteps.flatten(0     ,1		)		)
									_a					:				Any				=							scheduler.batch_step_no_noise(_UpperCAmelCase     ,timesteps.flatten(0     ,1		)     ,samples.flatten(0     ,1		)		)

									_a					:				Dict				=							torch.sum(torch.abs(_UpperCAmelCase		)		)
									_a					:				Optional[int]				=							torch.mean(torch.abs(_UpperCAmelCase		)		)

									assert abs(result_sum.item() - 11_53.18_33		) < 1E-2
									assert abs(result_mean.item() - 0.50_05		) < 1E-3







							def 							__lowercase      (							self :	Optional[int]		):
									_a					:				Union[str, Any]				=							self.scheduler_classes[0]
									_a					:				List[str]				=							self.get_scheduler_config()
									_a					:				Any				=							scheduler_class(**_UpperCAmelCase		)

									_a					:				Dict				=							len(_UpperCAmelCase		)

									_a					:				Union[str, Any]				=							self.dummy_model()
									_a					:				Union[str, Any]				=							self.dummy_sample_deter
									_a					:				Dict				=							torch.manual_seed(0		)

									for t in reversed(range(_UpperCAmelCase		)		):
											# 1. predict noise residual
											_a					:				Optional[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					:				int				=							pred_prev_sample

									_a					:				Tuple				=							torch.sum(torch.abs(_UpperCAmelCase		)		)
									_a					:				Optional[int]				=							torch.mean(torch.abs(_UpperCAmelCase		)		)

									assert abs(result_sum.item() - 2_58.96_06		) < 1E-2
									assert abs(result_mean.item() - 0.33_72		) < 1E-3







							def 							__lowercase      (							self :	str		):
									_a					:				List[Any]				=							self.scheduler_classes[0]
									_a					:				Any				=							self.get_scheduler_config(prediction_type='v_prediction'		)
									_a					:				Dict				=							scheduler_class(**_UpperCAmelCase		)

									_a					:				Tuple				=							len(_UpperCAmelCase		)

									_a					:				int				=							self.dummy_model()
									_a					:				str				=							self.dummy_sample_deter
									_a					:				Union[str, Any]				=							torch.manual_seed(0		)

									for t in reversed(range(_UpperCAmelCase		)		):
											# 1. predict noise residual
											_a					:				List[str]				=							model(_UpperCAmelCase     ,_UpperCAmelCase		)

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

											_a					:				Optional[Any]				=							pred_prev_sample

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

									assert abs(result_sum.item() - 2_02.02_96		) < 1E-2
									assert abs(result_mean.item() - 0.26_31		) < 1E-3







							def 							__lowercase      (							self :	int		):
									_a					:				Optional[Any]				=							self.scheduler_classes[0]
									_a					:				Dict				=							self.get_scheduler_config()
									_a					:				Dict				=							scheduler_class(**_UpperCAmelCase		)

									_a					:				Dict				=							[100, 87, 50, 1, 0]

									scheduler.set_timesteps(timesteps=_UpperCAmelCase		)

									_a					:				Dict				=							scheduler.timesteps

									for i, timestep in enumerate(_UpperCAmelCase		):
											if i == len(_UpperCAmelCase		) - 1:
													_a					:				Union[str, Any]				=							-1
											else:
													_a					:				List[str]				=							timesteps[i + 1]

											_a					:				str				=							scheduler.previous_timestep(_UpperCAmelCase		)
											_a					:				Optional[int]				=							prev_t.item()

											self.assertEqual(_UpperCAmelCase     ,_UpperCAmelCase		)







							def 							__lowercase      (							self :	Optional[Any]		):
									_a					:				str				=							self.scheduler_classes[0]
									_a					:				Tuple				=							self.get_scheduler_config()
									_a					:				Optional[int]				=							scheduler_class(**_UpperCAmelCase		)

									_a					:				str				=							[100, 87, 50, 51, 0]

									with self.assertRaises(_UpperCAmelCase     ,msg='`custom_timesteps` must be in descending order.'		):
											scheduler.set_timesteps(timesteps=_UpperCAmelCase		)







							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				Optional[int]				=							self.scheduler_classes[0]
									_a					:				int				=							self.get_scheduler_config()
									_a					:				Dict				=							scheduler_class(**_UpperCAmelCase		)

									_a					:				List[str]				=							[100, 87, 50, 1, 0]
									_a					:				Optional[Any]				=							len(_UpperCAmelCase		)

									with self.assertRaises(_UpperCAmelCase     ,msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'		):
											scheduler.set_timesteps(num_inference_steps=_UpperCAmelCase     ,timesteps=_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any		):
									_a					:				Tuple				=							self.scheduler_classes[0]
									_a					:				Optional[int]				=							self.get_scheduler_config()
									_a					:				str				=							scheduler_class(**_UpperCAmelCase		)

									_a					:				str				=							[scheduler.config.num_train_timesteps]

									with self.assertRaises(
									    _UpperCAmelCase     ,msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'     ,):
											scheduler.set_timesteps(timesteps=_UpperCAmelCase		)









'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		return "".join(sorted(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list[str]:
		return word_by_signature[signature(lowerCAmelCase_  )]


__lowerCAmelCase						      =       Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
__lowerCAmelCase						      =       sorted({word.strip().lower() for word in data.splitlines()})

__lowerCAmelCase						      =       collections.defaultdict(list)
for word in word_list:
							word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
							__lowerCAmelCase						      =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

							with open('''anagrams.txt''', '''w''') as file:
														file.write('''all_anagrams = \n ''')
														file.write(pprint.pformat(all_anagrams))




