






"""simple docstring"""
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				List[str]    =				(UnCLIPScheduler,)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		A_		:						Any	    =   {
		    """num_train_timesteps""": 1000,
		    """variance_type""": """fixed_small_log""",
		    """clip_sample""": True,
		    """clip_sample_range""": 1.0,
		    """prediction_type""": """epsilon""",
		}

		config.update(**lowerCAmelCase_							)
		return config





	def    lowerCamelCase(self							):
		for timesteps in [1, 5, 100, 1000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase_							)





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





	def    lowerCamelCase(self							):
		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		for clip_sample_range in [1, 5, 10, 20]:
			self.check_over_configs(clip_sample_range=lowerCAmelCase_							)





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





	def    lowerCamelCase(self							):
		for time_step in [0, 500, 999]:
			for prev_timestep in [None, 5, 100, 250, 500, 750]:
				if prev_timestep is not None and prev_timestep >= time_step:
					continue

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





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   self.scheduler_classes[0]
		A_		:						str	    =   self.get_scheduler_config(variance_type="""fixed_small_log"""							)
		A_		:						List[str]	    =   scheduler_class(**lowerCAmelCase_							)

		assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0_0_0_0e-1_0							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0549625							)							) < 1e-5
		assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.9994987							)							) < 1e-5





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   self.scheduler_classes[0]
		A_		:						Union[str, Any]	    =   self.get_scheduler_config(variance_type="""learned_range"""							)
		A_		:						Any	    =   scheduler_class(**lowerCAmelCase_							)

		A_		:						str	    =   0.5

		assert scheduler._get_variance(1     ,     predicted_variance=lowerCAmelCase_							) - -10.1712790 < 1e-5
		assert scheduler._get_variance(487     ,     predicted_variance=lowerCAmelCase_							) - -5.7998052 < 1e-5
		assert scheduler._get_variance(999     ,     predicted_variance=lowerCAmelCase_							) - -0.0010011 < 1e-5





	def    lowerCamelCase(self							):
		A_		:						str	    =   self.scheduler_classes[0]
		A_		:						Union[str, Any]	    =   self.get_scheduler_config()
		A_		:						Tuple	    =   scheduler_class(**lowerCAmelCase_							)

		A_		:						int	    =   scheduler.timesteps

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

		for i, t in enumerate(lowerCAmelCase_							):
			# 1. predict noise residual
			A_		:						Optional[Any]	    =   model(lowerCAmelCase_     ,     lowerCAmelCase_							)

			# 2. predict previous mean of sample x_t-1
			A_		:						str	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     generator=lowerCAmelCase_							).prev_sample

			A_		:						Any	    =   pred_prev_sample

		A_		:						Optional[Any]	    =   torch.sum(torch.abs(lowerCAmelCase_							)							)
		A_		:						List[Any]	    =   torch.mean(torch.abs(lowerCAmelCase_							)							)

		assert abs(result_sum.item() - 252.2682495							) < 1e-2
		assert abs(result_mean.item() - 0.3284743							) < 1e-3





	def    lowerCamelCase(self							):
		A_		:						List[str]	    =   self.scheduler_classes[0]
		A_		:						Union[str, Any]	    =   self.get_scheduler_config()
		A_		:						str	    =   scheduler_class(**lowerCAmelCase_							)

		scheduler.set_timesteps(25							)

		A_		:						List[str]	    =   scheduler.timesteps

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

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

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

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

			A_		:						Optional[Any]	    =   pred_prev_sample

		A_		:						Tuple	    =   torch.sum(torch.abs(lowerCAmelCase_							)							)
		A_		:						Any	    =   torch.mean(torch.abs(lowerCAmelCase_							)							)

		assert abs(result_sum.item() - 258.2044983							) < 1e-2
		assert abs(result_mean.item() - 0.3362038							) < 1e-3





	def    lowerCamelCase(self							):
		pass





	def    lowerCamelCase(self							):
		pass









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase         =							{
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase         =							[
							    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LukeForEntityClassification",
							    "LukeForEntityPairClassification",
							    "LukeForEntitySpanClassification",
							    "LukeForMultipleChoice",
							    "LukeForQuestionAnswering",
							    "LukeForSequenceClassification",
							    "LukeForTokenClassification",
							    "LukeForMaskedLM",
							    "LukeModel",
							    "LukePreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
							from .tokenization_luke import LukeTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_luke import (
														    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LukeForEntityClassification,
														    LukeForEntityPairClassification,
														    LukeForEntitySpanClassification,
														    LukeForMaskedLM,
														    LukeForMultipleChoice,
														    LukeForQuestionAnswering,
														    LukeForSequenceClassification,
														    LukeForTokenClassification,
														    LukeModel,
														    LukePreTrainedModel,
														)

else:
							import sys

							_lowerCAmelCase         =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

