





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class    _a     (				a__							):






							"""simple docstring"""

							snake_case_							 =						(UnCLIPScheduler,)

							def      A_  (      self				:     Dict							,	**a				:     Optional[Any]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					{
													    "num_train_timesteps": 10_00,
													    "variance_type": "fixed_small_log",
													    "clip_sample": True,
													    "clip_sample_range": 1.0,
													    "prediction_type": "epsilon",
													}

													config.update(**lowerCamelCase_    )
													return config

							def      A_  (      self				:     Any    )	->Tuple:
													for timesteps in [1, 5, 1_00, 10_00]:
																			self.check_over_configs(num_train_timesteps=lowerCamelCase_    )

							def      A_  (      self				:     Tuple    )	->Any:
													for variance in ["fixed_small_log", "learned_range"]:
																			self.check_over_configs(variance_type=lowerCamelCase_    )

							def      A_  (      self				:     List[Any]    )	->Union[str, Any]:
													for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=lowerCamelCase_    )

							def      A_  (      self				:     Tuple    )	->List[str]:
													for clip_sample_range in [1, 5, 10, 20]:
																			self.check_over_configs(clip_sample_range=lowerCamelCase_    )

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

							def      A_  (      self				:     Optional[int]    )	->List[Any]:
													for time_step in [0, 5_00, 9_99]:
																			for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																															continue

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

							def      A_  (      self				:     Any    )	->str:
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.get_scheduler_config(variance_type="fixed_small_log"    )
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					scheduler_class(**lowerCamelCase_    )

													assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 1.0000E-10    )    ) < 1E-5
													assert torch.sum(torch.abs(scheduler._get_variance(4_87    ) - 0.054_9625    )    ) < 1E-5
													assert torch.sum(torch.abs(scheduler._get_variance(9_99    ) - 0.999_4987    )    ) < 1E-5

							def      A_  (      self				:     Union[str, Any]    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.get_scheduler_config(variance_type="learned_range"    )
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					scheduler_class(**lowerCamelCase_    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					0.5

													assert scheduler._get_variance(1							,	predicted_variance=lowerCamelCase_    ) - -10.171_2790 < 1E-5
													assert scheduler._get_variance(4_87							,	predicted_variance=lowerCamelCase_    ) - -5.799_8052 < 1E-5
													assert scheduler._get_variance(9_99							,	predicted_variance=lowerCamelCase_    ) - -0.001_0011 < 1E-5

							def      A_  (      self				:     Optional[int]    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__     :				Any   =					self.get_scheduler_config()
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					scheduler_class(**lowerCamelCase_    )

													SCREAMING_SNAKE_CASE__     :				int   =					scheduler.timesteps

													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.dummy_model()
													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.dummy_sample_deter
													SCREAMING_SNAKE_CASE__     :				List[str]   =					torch.manual_seed(0    )

													for i, t in enumerate(lowerCamelCase_    ):
																			# 1. predict noise residual
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					model(lowerCamelCase_							,	lowerCamelCase_    )

																			# 2. predict previous mean of sample x_t-1
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					scheduler.step(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	generator=lowerCamelCase_    ).prev_sample

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					pred_prev_sample

													SCREAMING_SNAKE_CASE__     :				Any   =					torch.sum(torch.abs(lowerCamelCase_    )    )
													SCREAMING_SNAKE_CASE__     :				int   =					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      A_  (      self				:     Any    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.get_scheduler_config()
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					scheduler_class(**lowerCamelCase_    )

													scheduler.set_timesteps(25    )

													SCREAMING_SNAKE_CASE__     :				int   =					scheduler.timesteps

													SCREAMING_SNAKE_CASE__     :				int   =					self.dummy_model()
													SCREAMING_SNAKE_CASE__     :				str   =					self.dummy_sample_deter
													SCREAMING_SNAKE_CASE__     :				str   =					torch.manual_seed(0    )

													for i, t in enumerate(lowerCamelCase_    ):
																			# 1. predict noise residual
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					model(lowerCamelCase_							,	lowerCamelCase_    )

																			if i + 1 == timesteps.shape[0]:
																									SCREAMING_SNAKE_CASE__     :				List[str]   =					None
																			else:
																									SCREAMING_SNAKE_CASE__     :				Dict   =					timesteps[i + 1]

																			# 2. predict previous mean of sample x_t-1
																			SCREAMING_SNAKE_CASE__     :				Dict   =					scheduler.step(
																			    lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	prev_timestep=lowerCamelCase_							,	generator=lowerCamelCase_    ).prev_sample

																			SCREAMING_SNAKE_CASE__     :				Dict   =					pred_prev_sample

													SCREAMING_SNAKE_CASE__     :				List[str]   =					torch.sum(torch.abs(lowerCamelCase_    )    )
													SCREAMING_SNAKE_CASE__     :				int   =					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      A_  (      self				:     str    )	->List[Any]:
													pass






							def      A_  (      self				:     Tuple    )	->Optional[Any]:
													pass






import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__lowercase       :List[Any]       =  "python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
				pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
				pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
				if pkg in deps:
								if pkg == "tokenizers":
												# must be loaded here, or else tqdm check may fail
												from .utils import is_tokenizers_available

												if not is_tokenizers_available():
																continue  # not required, check version only if installed

								require_version_core(deps[pkg])
				else:
								raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")







def       UpperCAmelCase							(     _lowerCamelCase      :  Optional[Any]			,   _lowerCamelCase      :  Optional[Any]=None     ):





						'''simple docstring'''


						require_version(deps[pkg]			,   _lowerCamelCase     )