





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					List[Any]	      =				(EulerDiscreteScheduler,)
			lowerCamelCase_					:					Optional[Any]	      =				10






			def       lowerCamelCase  (self ,							**__magic_name__   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  {
							    '''num_train_timesteps''': 1100,
							    '''beta_start''': 0.0_001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**__magic_name__   )
							return config






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=__magic_name__   )






			def       lowerCamelCase  (self   )					->       Tuple:


							'''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=__magic_name__ ,							beta_end=__magic_name__   )






			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=__magic_name__   )






			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=__magic_name__   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self.scheduler_classes[0]
							snake_case_						:   Any           =  self.get_scheduler_config()
							snake_case_						:   Tuple           =  scheduler_class(**__magic_name__   )

							scheduler.set_timesteps(self.num_inference_steps   )

							snake_case_						:   List[str]           =  torch.manual_seed(0   )

							snake_case_						:   str           =  self.dummy_model()
							snake_case_						:   List[Any]           =  self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_						:   Optional[Any]           =  sample.to(__magic_name__   )

							for i, t in enumerate(scheduler.timesteps   ):
											snake_case_						:   Optional[Any]           =  scheduler.scale_model_input(__magic_name__ ,							__magic_name__   )

											snake_case_						:   Any           =  model(__magic_name__ ,							__magic_name__   )

											snake_case_						:   Optional[Any]           =  scheduler.step(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							generator=__magic_name__   )
											snake_case_						:   Union[str, Any]           =  output.prev_sample

							snake_case_						:   Any           =  torch.sum(torch.abs(__magic_name__   )   )
							snake_case_						:   str           =  torch.mean(torch.abs(__magic_name__   )   )

							assert abs(result_sum.item() - 10.0_807   ) < 1e-2
							assert abs(result_mean.item() - 0.0_131   ) < 1e-3






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  self.scheduler_classes[0]
							snake_case_						:   Optional[Any]           =  self.get_scheduler_config(prediction_type='''v_prediction'''   )
							snake_case_						:   Tuple           =  scheduler_class(**__magic_name__   )

							scheduler.set_timesteps(self.num_inference_steps   )

							snake_case_						:   str           =  torch.manual_seed(0   )

							snake_case_						:   Tuple           =  self.dummy_model()
							snake_case_						:   int           =  self.dummy_sample_deter * scheduler.init_noise_sigma
							snake_case_						:   List[str]           =  sample.to(__magic_name__   )

							for i, t in enumerate(scheduler.timesteps   ):
											snake_case_						:   Union[str, Any]           =  scheduler.scale_model_input(__magic_name__ ,							__magic_name__   )

											snake_case_						:   str           =  model(__magic_name__ ,							__magic_name__   )

											snake_case_						:   Optional[Any]           =  scheduler.step(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							generator=__magic_name__   )
											snake_case_						:   Optional[int]           =  output.prev_sample

							snake_case_						:   Optional[int]           =  torch.sum(torch.abs(__magic_name__   )   )
							snake_case_						:   List[str]           =  torch.mean(torch.abs(__magic_name__   )   )

							assert abs(result_sum.item() - 0.0_002   ) < 1e-2
							assert abs(result_mean.item() - 2.2676e-06   ) < 1e-3






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   str           =  self.scheduler_classes[0]
							snake_case_						:   Union[str, Any]           =  self.get_scheduler_config()
							snake_case_						:   Any           =  scheduler_class(**__magic_name__   )

							scheduler.set_timesteps(self.num_inference_steps ,							device=__magic_name__   )

							snake_case_						:   Optional[Any]           =  torch.manual_seed(0   )

							snake_case_						:   Optional[Any]           =  self.dummy_model()
							snake_case_						:   Dict           =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_						:   Union[str, Any]           =  sample.to(__magic_name__   )

							for t in scheduler.timesteps:
											snake_case_						:   Optional[int]           =  scheduler.scale_model_input(__magic_name__ ,							__magic_name__   )

											snake_case_						:   Union[str, Any]           =  model(__magic_name__ ,							__magic_name__   )

											snake_case_						:   Dict           =  scheduler.step(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							generator=__magic_name__   )
											snake_case_						:   Tuple           =  output.prev_sample

							snake_case_						:   Optional[Any]           =  torch.sum(torch.abs(__magic_name__   )   )
							snake_case_						:   Optional[int]           =  torch.mean(torch.abs(__magic_name__   )   )

							assert abs(result_sum.item() - 10.0_807   ) < 1e-2
							assert abs(result_mean.item() - 0.0_131   ) < 1e-3






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self.scheduler_classes[0]
							snake_case_						:   Optional[int]           =  self.get_scheduler_config()
							snake_case_						:   Any           =  scheduler_class(**__magic_name__ ,							use_karras_sigmas=__magic_name__   )

							scheduler.set_timesteps(self.num_inference_steps ,							device=__magic_name__   )

							snake_case_						:   List[Any]           =  torch.manual_seed(0   )

							snake_case_						:   List[str]           =  self.dummy_model()
							snake_case_						:   Dict           =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
							snake_case_						:   Tuple           =  sample.to(__magic_name__   )

							for t in scheduler.timesteps:
											snake_case_						:   Optional[int]           =  scheduler.scale_model_input(__magic_name__ ,							__magic_name__   )

											snake_case_						:   Union[str, Any]           =  model(__magic_name__ ,							__magic_name__   )

											snake_case_						:   int           =  scheduler.step(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							generator=__magic_name__   )
											snake_case_						:   List[str]           =  output.prev_sample

							snake_case_						:   Tuple           =  torch.sum(torch.abs(__magic_name__   )   )
							snake_case_						:   List[str]           =  torch.mean(torch.abs(__magic_name__   )   )

							assert abs(result_sum.item() - 124.52_299_499_511_719   ) < 1e-2
							assert abs(result_mean.item() - 0.16_213_932_633_399_963   ) < 1e-3







from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCAmelCase_    =    TypeVar('''KEY''')
lowerCAmelCase_    =    TypeVar('''VAL''')



@dataclass(frozen=_a,					slots=_a					)
class  __lowerCAmelCase	(      Generic[KEY, VAL]					):
			lowerCamelCase_					:					KEY
			lowerCamelCase_					:					VAL




class  __lowerCAmelCase	(      _Item					):






			def __init__(self   )					->       None:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )






			def __bool__(self   )					->       bool:


							'''simple docstring'''



							return False




lowerCAmelCase_    =    _DeletedItem()



class  __lowerCAmelCase	(      MutableMapping[KEY, VAL]					):






			def __init__(self ,							__magic_name__ = 8 ,							__magic_name__ = 0.75   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  initial_block_size
							snake_case_						:   list[_Item | None]           =  [None] * initial_block_size
							assert 0.0 < capacity_factor < 1.0
							snake_case_						:   List[str]           =  capacity_factor
							snake_case_						:   int           =  0






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return hash(__magic_name__   ) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return (ind + 1) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self._buckets[ind]
							if not stored:
											snake_case_						:   Optional[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											self._len += 1
											return True
							elif stored.key == key:
											snake_case_						:   List[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											return True
							else:
											return False






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Any           =  len(self._buckets   ) * self._capacity_factor
							return len(self   ) >= int(__magic_name__   )






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							if len(self._buckets   ) <= self._initial_block_size:
											return False
							snake_case_						:   int           =  len(self._buckets   ) * self._capacity_factor / 2
							return len(self   ) < limit






			def       lowerCamelCase  (self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self._buckets
							snake_case_						:   int           =  [None] * new_size
							snake_case_						:   Optional[int]           =  0
							for item in old_buckets:
											if item:
															self._add_item(item.key ,							item.val   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) * 2   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) // 2   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Iterator[int]:


							'''simple docstring'''



							snake_case_						:   Dict           =  self._get_bucket_index(__magic_name__   )
							for _ in range(len(self._buckets   )   ):
											yield ind
											snake_case_						:   Tuple           =  self._get_next_ind(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											if self._try_set(__magic_name__ ,							__magic_name__ ,							__magic_name__   ):
															break






			def __setitem__(self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							if self._is_full():
											self._size_up()

							self._add_item(__magic_name__ ,							__magic_name__   )






			def __delitem__(self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   Optional[Any]           =  self._buckets[ind]
											if item is None:
															raise KeyError(__magic_name__   )
											if item is _deleted:
															continue
											if item.key == key:
															snake_case_						:   Union[str, Any]           =  _deleted
															self._len -= 1
															break
							if self._is_sparse():
											self._size_down()






			def __getitem__(self ,							__magic_name__   )					->       VAL:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   List[Any]           =  self._buckets[ind]
											if item is None:
															break
											if item is _deleted:
															continue
											if item.key == key:
															return item.val
							raise KeyError(__magic_name__   )






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self._len






			def __iter__(self   )					->       Iterator[KEY]:


							'''simple docstring'''



							yield from (item.key for item in self._buckets if item)






			def __repr__(self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[str]           =  ''' ,'''.join(
							    F'''{item.key}: {item.val}''' for item in self._buckets if item   )
							return F'''HashMap({val_string})'''
