


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  (DDIMParallelScheduler,)
							__UpperCAmelCase   :							str					  =  (("eta", 0.0), ("num_inference_steps", 50))

							def    __snake_case (							self	:  List[Any]   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   Tuple:
														__snake_case		:      Optional[Any]										=       {
														    "num_train_timesteps": 1000,
														    "beta_start": 0.00_01,
														    "beta_end": 0.02,
														    "beta_schedule": "linear",
														    "clip_sample": True,
														}

														config.update(**lowerCamelCase    )
														return config

							def    __snake_case (							self	:  List[Any]   ,     **lowerCamelCase	:  int    )     ->   Dict:
														__snake_case		:      Any										=       self.scheduler_classes[0]
														__snake_case		:      Optional[Any]										=       self.get_scheduler_config(**lowerCamelCase    )
														__snake_case		:      Any										=       scheduler_class(**lowerCamelCase    )

														__snake_case		:      Dict										=       10, 0.0

														__snake_case		:      Tuple										=       self.dummy_model()
														__snake_case		:      Optional[Any]										=       self.dummy_sample_deter

														scheduler.set_timesteps(lowerCamelCase    )

														for t in scheduler.timesteps:
																					__snake_case		:      Dict										=       model(lowerCamelCase   ,     lowerCamelCase    )
																					__snake_case		:      List[str]										=       scheduler.step(lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    ).prev_sample

														return sample

							def    __snake_case (							self	:  str    )     ->   Tuple:
														for timesteps in [100, 500, 1000]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]    )     ->   Any:
														for steps_offset in [0, 1]:
																					self.check_over_configs(steps_offset=lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       self.scheduler_classes[0]
														__snake_case		:      List[Any]										=       self.get_scheduler_config(steps_offset=1    )
														__snake_case		:      List[str]										=       scheduler_class(**lowerCamelCase    )
														scheduler.set_timesteps(5    )
														assert torch.equal(scheduler.timesteps   ,     torch.LongTensor([801, 601, 401, 201, 1]    )    )

							def    __snake_case (							self	:  Dict    )     ->   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=lowerCamelCase   ,     beta_end=lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]    )     ->   Tuple:
														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=lowerCamelCase    )

							def    __snake_case (							self	:  int    )     ->   int:
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCamelCase    )

							def    __snake_case (							self	:  Any    )     ->   Dict:
														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCamelCase    )

							def    __snake_case (							self	:  Any    )     ->   List[str]:
														for timestep_spacing in ["trailing", "leading"]:
																					self.check_over_configs(timestep_spacing=lowerCamelCase    )

							def    __snake_case (							self	:  Any    )     ->   List[str]:
														for rescale_betas_zero_snr in [True, False]:
																					self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]    )     ->   List[Any]:
														self.check_over_configs(thresholding=lowerCamelCase    )
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=lowerCamelCase   ,     prediction_type=lowerCamelCase   ,     sample_max_value=lowerCamelCase   ,     )

							def    __snake_case (							self	:  List[Any]    )     ->   str:
														for t in [1, 10, 49]:
																					self.check_over_forward(time_step=lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]    )     ->   Optional[Any]:
														for t, num_inference_steps in zip([1, 10, 50]   ,     [10, 50, 500]    ):
																					self.check_over_forward(time_step=lowerCamelCase   ,     num_inference_steps=lowerCamelCase    )

							def    __snake_case (							self	:  Tuple    )     ->   Union[str, Any]:
														for t, eta in zip([1, 10, 49]   ,     [0.0, 0.5, 1.0]    ):
																					self.check_over_forward(time_step=lowerCamelCase   ,     eta=lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]    )     ->   Any:
														__snake_case		:      int										=       self.scheduler_classes[0]
														__snake_case		:      Any										=       self.get_scheduler_config()
														__snake_case		:      int										=       scheduler_class(**lowerCamelCase    )

														assert torch.sum(torch.abs(scheduler._get_variance(0   ,     0    ) - 0.0    )    ) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(420   ,     400    ) - 0.1_47_71    )    ) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(980   ,     960    ) - 0.3_24_60    )    ) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(0   ,     0    ) - 0.0    )    ) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(487   ,     486    ) - 0.0_09_79    )    ) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(999   ,     998    ) - 0.02    )    ) < 1E-5

							def    __snake_case (							self	:  List[Any]    )     ->   str:
														__snake_case		:      List[Any]										=       self.scheduler_classes[0]
														__snake_case		:      int										=       self.get_scheduler_config()
														__snake_case		:      Optional[int]										=       scheduler_class(**lowerCamelCase    )

														__snake_case		:      Any										=       10, 0.0
														scheduler.set_timesteps(lowerCamelCase    )

														__snake_case		:      Optional[int]										=       self.dummy_model()
														__snake_case		:      List[str]										=       self.dummy_sample_deter
														__snake_case		:      str										=       self.dummy_sample_deter + 0.1
														__snake_case		:      Tuple										=       self.dummy_sample_deter - 0.1

														__snake_case		:      Optional[Any]										=       samplea.shape[0]
														__snake_case		:      Any										=       torch.stack([samplea, samplea, samplea]   ,     dim=0    )
														__snake_case		:      Dict										=       torch.arange(lowerCamelCase    )[0:3, None].repeat(1   ,     lowerCamelCase    )

														__snake_case		:      int										=       model(samples.flatten(0   ,     1    )   ,     timesteps.flatten(0   ,     1    )    )
														__snake_case		:      Tuple										=       scheduler.batch_step_no_noise(lowerCamelCase   ,     timesteps.flatten(0   ,     1    )   ,     samples.flatten(0   ,     1    )   ,     lowerCamelCase    )

														__snake_case		:      Tuple										=       torch.sum(torch.abs(lowerCamelCase    )    )
														__snake_case		:      List[str]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_sum.item() - 1147.7904    ) < 1E-2
														assert abs(result_mean.item() - 0.49_82    ) < 1E-3

							def    __snake_case (							self	:  Tuple    )     ->   Optional[Any]:
														__snake_case		:      Optional[int]										=       self.full_loop()

														__snake_case		:      Any										=       torch.sum(torch.abs(lowerCamelCase    )    )
														__snake_case		:      Optional[int]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_sum.item() - 172.0067    ) < 1E-2
														assert abs(result_mean.item() - 0.22_39_67    ) < 1E-3

							def    __snake_case (							self	:  Optional[int]    )     ->   Any:
														__snake_case		:      int										=       self.full_loop(prediction_type="v_prediction"    )

														__snake_case		:      Union[str, Any]										=       torch.sum(torch.abs(lowerCamelCase    )    )
														__snake_case		:      Union[str, Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_sum.item() - 52.53_02    ) < 1E-2
														assert abs(result_mean.item() - 0.06_84    ) < 1E-3

							def    __snake_case (							self	:  List[Any]    )     ->   Union[str, Any]:
														# We specify different beta, so that the first alpha is 0.99
														__snake_case		:      List[str]										=       self.full_loop(set_alpha_to_one=lowerCamelCase   ,     beta_start=0.01    )
														__snake_case		:      List[str]										=       torch.sum(torch.abs(lowerCamelCase    )    )
														__snake_case		:      Union[str, Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_sum.item() - 149.8295    ) < 1E-2
														assert abs(result_mean.item() - 0.19_51    ) < 1E-3






							def    __snake_case (							self	:  List[Any]    )     ->   List[Any]:
														# We specify different beta, so that the first alpha is 0.99
														__snake_case		:      Dict										=       self.full_loop(set_alpha_to_one=lowerCamelCase   ,     beta_start=0.01    )
														__snake_case		:      List[Any]										=       torch.sum(torch.abs(lowerCamelCase    )    )
														__snake_case		:      Union[str, Any]										=       torch.mean(torch.abs(lowerCamelCase    )    )

														assert abs(result_sum.item() - 149.0784    ) < 1E-2
														assert abs(result_mean.item() - 0.19_41    ) < 1E-3





import logging

from transformers.configuration_utils import PretrainedConfig


_snake_case					:		Optional[Any]      =    logging.getLogger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "masked_bert"

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Any=30522   ,     lowerCamelCase	:  Tuple=768   ,     lowerCamelCase	:  str=12   ,     lowerCamelCase	:  Dict=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  List[str]="gelu"   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  List[Any]=512   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  str=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  Dict="topK"   ,     lowerCamelCase	:  List[Any]="constant"   ,     lowerCamelCase	:  Dict=0.0   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Dict:
														super().__init__(pad_token_id=lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       vocab_size
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      Union[str, Any]										=       num_attention_heads
														__snake_case		:      Optional[Any]										=       hidden_act
														__snake_case		:      str										=       intermediate_size
														__snake_case		:      Tuple										=       hidden_dropout_prob
														__snake_case		:      Dict										=       attention_probs_dropout_prob
														__snake_case		:      List[str]										=       max_position_embeddings
														__snake_case		:      Union[str, Any]										=       type_vocab_size
														__snake_case		:      Any										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps
														__snake_case		:      str										=       pruning_method
														__snake_case		:      int										=       mask_init
														__snake_case		:      Any										=       mask_scale

