



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Tuple      =					(UnCLIPScheduler,)

						def 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				str:
									"""simple docstring"""




									a__       :						int							=  {
									    """num_train_timesteps""": 1_0_0_0,
									    """variance_type""": """fixed_small_log""",
									    """clip_sample""": True,
									    """clip_sample_range""": 1.0,
									    """prediction_type""": """epsilon""",
									}

									config.update(**__lowercase  )
									return config

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
												self.check_over_configs(num_train_timesteps=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									for clip_sample_range in [1, 5, 1_0, 2_0]:
												self.check_over_configs(clip_sample_range=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									for time_step in [0, 5_0_0, 9_9_9]:
												for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
															if prev_timestep is not None and prev_timestep >= time_step:
																		continue

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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Tuple							=  self.scheduler_classes[0]
									a__       :						Union[str, Any]							=  self.get_scheduler_config(variance_type="""fixed_small_log"""  )
									a__       :						str							=  scheduler_class(**__lowercase  )

									assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 1.0000E-10  )  ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ) - 0.0_5_4_9_6_2_5  )  ) < 1E-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ) - 0.9_9_9_4_9_8_7  )  ) < 1E-5

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Optional[int]							=  self.scheduler_classes[0]
									a__       :						Optional[int]							=  self.get_scheduler_config(variance_type="""learned_range"""  )
									a__       :						List[str]							=  scheduler_class(**__lowercase  )

									a__       :						int							=  0.5

									assert scheduler._get_variance(1 ,       predicted_variance=__lowercase  ) - -1_0.1_7_1_2_7_9_0 < 1E-5
									assert scheduler._get_variance(4_8_7 ,       predicted_variance=__lowercase  ) - -5.7_9_9_8_0_5_2 < 1E-5
									assert scheduler._get_variance(9_9_9 ,       predicted_variance=__lowercase  ) - -0.0_0_1_0_0_1_1 < 1E-5

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									a__       :						Dict							=  self.scheduler_classes[0]
									a__       :						Optional[Any]							=  self.get_scheduler_config()
									a__       :						Tuple							=  scheduler_class(**__lowercase  )

									a__       :						str							=  scheduler.timesteps

									a__       :						List[Any]							=  self.dummy_model()
									a__       :						Optional[Any]							=  self.dummy_sample_deter
									a__       :						Tuple							=  torch.manual_seed(0  )

									for i, t in enumerate(__lowercase  ):
												# 1. predict noise residual
												a__       :						int							=  model(__lowercase ,       __lowercase  )

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

												a__       :						List[str]							=  pred_prev_sample

									a__       :						Union[str, Any]							=  torch.sum(torch.abs(__lowercase  )  )
									a__       :						str							=  torch.mean(torch.abs(__lowercase  )  )

									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 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




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

									scheduler.set_timesteps(2_5  )

									a__       :						Tuple							=  scheduler.timesteps

									a__       :						Any							=  self.dummy_model()
									a__       :						Any							=  self.dummy_sample_deter
									a__       :						Any							=  torch.manual_seed(0  )

									for i, t in enumerate(__lowercase  ):
												# 1. predict noise residual
												a__       :						str							=  model(__lowercase ,       __lowercase  )

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

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

												a__       :						Dict							=  pred_prev_sample

									a__       :						Tuple							=  torch.sum(torch.abs(__lowercase  )  )
									a__       :						int							=  torch.mean(torch.abs(__lowercase  )  )

									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 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									pass









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[float]      =					field(
						    default=0.0 ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "Whether to SortishSamler or not."}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "whether to use adafactor"}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(default=A__ ,     metadata={"help": "Dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Attention dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default="linear" ,     metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}"} ,     )




