






"""simple docstring"""

from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader

class 						A_							(      snake_case_  ):







				def __init__(							self							:						Optional[Any]						,    __lowerCamelCase							:						pyspark.sql.DataFrame						,    __lowerCamelCase							:						Optional[NamedSplit] = None						,    __lowerCamelCase							:						Optional[Features] = None						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						str = None						,    __lowerCamelCase							:						bool = False						,    __lowerCamelCase							:						str = None						,    __lowerCamelCase							:						bool = True						,    __lowerCamelCase							:						str = "arrow"						,    **__lowerCamelCase							:						List[Any]						,    )				->					Optional[int]:
										super().__init__(
										    split=__lowerCamelCase						,    features=__lowerCamelCase						,    cache_dir=__lowerCamelCase						,    keep_in_memory=__lowerCamelCase						,    streaming=__lowerCamelCase						,    **__lowerCamelCase						,    )
										__magic_name__            =     load_from_cache_file
										__magic_name__            =     file_format
										__magic_name__            =     Spark(
										    df=__lowerCamelCase						,    features=__lowerCamelCase						,    cache_dir=__lowerCamelCase						,    working_dir=__lowerCamelCase						,    **__lowerCamelCase						,    )







				def     _snake_case					(							self							:						Optional[int]	)				->					List[Any]:
										if self.streaming:
																return self.builder.as_streaming_dataset(split=self.split	)
										__magic_name__            =     None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
										self.builder.download_and_prepare(
										    download_mode=__lowerCamelCase						,    file_format=self._file_format						,    )
										return self.builder.as_dataset(split=self.split	)











"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				(UnCLIPScheduler,)







				def     _snake_case					(							self							:						Any						,    **__lowerCamelCase							:						List[Any]	)				->					str:
										__magic_name__            =     {
										    "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(**__lowerCamelCase	)
										return config







				def     _snake_case					(							self							:						int	)				->					Tuple:
										for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																self.check_over_configs(num_train_timesteps=__lowerCamelCase	)







				def     _snake_case					(							self							:						int	)				->					Any:
										for variance in ["fixed_small_log", "learned_range"]:
																self.check_over_configs(variance_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Tuple:
										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=__lowerCamelCase	)







				def     _snake_case					(							self							:						Tuple	)				->					Optional[int]:
										for clip_sample_range in [1, 5, 1_0, 2_0]:
																self.check_over_configs(clip_sample_range=__lowerCamelCase	)







				def     _snake_case					(							self							:						List[Any]	)				->					Any:
										for prediction_type in ["epsilon", "sample"]:
																self.check_over_configs(prediction_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					List[str]:
										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=__lowerCamelCase						,    prev_timestep=__lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Tuple:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(variance_type="fixed_small_log"	)
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.00_00e-10	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(4_8_7	) - 0.054_9625	)	) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(9_9_9	) - 0.999_4987	)	) < 1e-5







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[Any]:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config(variance_type="learned_range"	)
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										__magic_name__            =     0.5

										assert scheduler._get_variance(1						,    predicted_variance=__lowerCamelCase	) - -10.171_2790 < 1e-5
										assert scheduler._get_variance(4_8_7						,    predicted_variance=__lowerCamelCase	) - -5.799_8052 < 1e-5
										assert scheduler._get_variance(9_9_9						,    predicted_variance=__lowerCamelCase	) - -0.001_0011 < 1e-5







				def     _snake_case					(							self							:						Optional[Any]	)				->					str:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										__magic_name__            =     scheduler.timesteps

										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										__magic_name__            =     torch.manual_seed(0	)

										for i, t in enumerate(__lowerCamelCase	):
																# 1. predict noise residual
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

																# 2. predict previous mean of sample x_t-1
																__magic_name__            =     scheduler.step(__lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    generator=__lowerCamelCase	).prev_sample

																__magic_name__            =     pred_prev_sample

										__magic_name__            =     torch.sum(torch.abs(__lowerCamelCase	)	)
										__magic_name__            =     torch.mean(torch.abs(__lowerCamelCase	)	)

										assert abs(result_sum.item() - 252.268_2495	) < 1e-2
										assert abs(result_mean.item() - 0.328_4743	) < 1e-3







				def     _snake_case					(							self							:						int	)				->					int:
										__magic_name__            =     self.scheduler_classes[0]
										__magic_name__            =     self.get_scheduler_config()
										__magic_name__            =     scheduler_class(**__lowerCamelCase	)

										scheduler.set_timesteps(2_5	)

										__magic_name__            =     scheduler.timesteps

										__magic_name__            =     self.dummy_model()
										__magic_name__            =     self.dummy_sample_deter
										__magic_name__            =     torch.manual_seed(0	)

										for i, t in enumerate(__lowerCamelCase	):
																# 1. predict noise residual
																__magic_name__            =     model(__lowerCamelCase						,    __lowerCamelCase	)

																if i + 1 == timesteps.shape[0]:
																						__magic_name__            =     None
																else:
																						__magic_name__            =     timesteps[i + 1]

																# 2. predict previous mean of sample x_t-1
																__magic_name__            =     scheduler.step(
																    __lowerCamelCase						,    __lowerCamelCase						,    __lowerCamelCase						,    prev_timestep=__lowerCamelCase						,    generator=__lowerCamelCase	).prev_sample

																__magic_name__            =     pred_prev_sample

										__magic_name__            =     torch.sum(torch.abs(__lowerCamelCase	)	)
										__magic_name__            =     torch.mean(torch.abs(__lowerCamelCase	)	)

										assert abs(result_sum.item() - 258.204_4983	) < 1e-2
										assert abs(result_mean.item() - 0.336_2038	) < 1e-3







				def     _snake_case					(							self							:						List[str]	)				->					Any:
										pass







				def     _snake_case					(							self							:						int	)				->					Optional[int]:
										pass



