





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase            =       {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       ["""RoFormerTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RoFormerForCausalLM""",
							    """RoFormerForMaskedLM""",
							    """RoFormerForMultipleChoice""",
							    """RoFormerForQuestionAnswering""",
							    """RoFormerForSequenceClassification""",
							    """RoFormerForTokenClassification""",
							    """RoFormerLayer""",
							    """RoFormerModel""",
							    """RoFormerPreTrainedModel""",
							    """load_tf_weights_in_roformer""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRoFormerForCausalLM""",
							    """TFRoFormerForMaskedLM""",
							    """TFRoFormerForMultipleChoice""",
							    """TFRoFormerForQuestionAnswering""",
							    """TFRoFormerForSequenceClassification""",
							    """TFRoFormerForTokenClassification""",
							    """TFRoFormerLayer""",
							    """TFRoFormerModel""",
							    """TFRoFormerPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase            =       [
							    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FlaxRoFormerForMaskedLM""",
							    """FlaxRoFormerForMultipleChoice""",
							    """FlaxRoFormerForQuestionAnswering""",
							    """FlaxRoFormerForSequenceClassification""",
							    """FlaxRoFormerForTokenClassification""",
							    """FlaxRoFormerModel""",
							    """FlaxRoFormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
							from .tokenization_roformer import RoFormerTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roformer_fast import RoFormerTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roformer import (
														    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RoFormerForCausalLM,
														    RoFormerForMaskedLM,
														    RoFormerForMultipleChoice,
														    RoFormerForQuestionAnswering,
														    RoFormerForSequenceClassification,
														    RoFormerForTokenClassification,
														    RoFormerLayer,
														    RoFormerModel,
														    RoFormerPreTrainedModel,
														    load_tf_weights_in_roformer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roformer import (
														    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRoFormerForCausalLM,
														    TFRoFormerForMaskedLM,
														    TFRoFormerForMultipleChoice,
														    TFRoFormerForQuestionAnswering,
														    TFRoFormerForSequenceClassification,
														    TFRoFormerForTokenClassification,
														    TFRoFormerLayer,
														    TFRoFormerModel,
														    TFRoFormerPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roformer import (
														    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxRoFormerForMaskedLM,
														    FlaxRoFormerForMultipleChoice,
														    FlaxRoFormerForQuestionAnswering,
														    FlaxRoFormerForSequenceClassification,
														    FlaxRoFormerForTokenClassification,
														    FlaxRoFormerModel,
														    FlaxRoFormerPreTrainedModel,
														)


else:
							import sys

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










'''simple docstring'''




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(DPMSolverSinglestepScheduler,)
					SCREAMING_SNAKE_CASE__				=	(('''num_inference_steps''', 25),)


					def lowerCamelCase_			(						self				:			Any      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			{
							    """num_train_timesteps""": 10_00,
							    """beta_start""": 0.0_001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							    """solver_order""": 2,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							    """sample_max_value""": 1.0,
							    """algorithm_type""": """dpmsolver++""",
							    """solver_type""": """midpoint""",
							    """lambda_min_clipped""": -float("""inf"""						),
							    """variance_type""": None,
							}

							config.update(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			List[str]=0      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			dict(self.forward_default_kwargs						)
							SCREAMING_SNAKE_CASE :				List[str]		=			kwargs.pop("""num_inference_steps"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.dummy_sample
							SCREAMING_SNAKE_CASE :				str		=			0.1 * sample
							SCREAMING_SNAKE_CASE :				Any		=			[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE :				Tuple		=			self.get_scheduler_config(**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler_class(**lowerCamelCase_						)
									scheduler.set_timesteps(lowerCamelCase_						)
									# copy over dummy past residuals
									SCREAMING_SNAKE_CASE :				Any		=			dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				int		=			scheduler_class.from_pretrained(lowerCamelCase_						)
											new_scheduler.set_timesteps(lowerCamelCase_						)
											# copy over dummy past residuals
											SCREAMING_SNAKE_CASE :				Optional[int]		=			dummy_past_residuals[: new_scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				str		=			sample, sample
									for t in range(lowerCamelCase_      ,						time_step + scheduler.config.solver_order + 1						):
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample
											SCREAMING_SNAKE_CASE :				Optional[int]		=			new_scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample

											assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							pass


					def lowerCamelCase_			(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=0      ,						**lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			dict(self.forward_default_kwargs						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			kwargs.pop("""num_inference_steps"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			0.1 * sample
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE :				Any		=			scheduler_class(**lowerCamelCase_						)
									scheduler.set_timesteps(lowerCamelCase_						)

									# copy over dummy past residuals (must be after setting timesteps)
									SCREAMING_SNAKE_CASE :				int		=			dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class.from_pretrained(lowerCamelCase_						)
											# copy over dummy past residuals
											new_scheduler.set_timesteps(lowerCamelCase_						)

											# copy over dummy past residual (must be after setting timesteps)
											SCREAMING_SNAKE_CASE :				Dict		=			dummy_past_residuals[: new_scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample
									SCREAMING_SNAKE_CASE :				Dict		=			new_scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample

									assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"


					def lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			int=None      ,						**lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							if scheduler is None:
									SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config(**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.get_scheduler_config(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			10
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase_						)

							for i, t in enumerate(scheduler.timesteps						):
									SCREAMING_SNAKE_CASE :				Dict		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							return sample


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			50
							SCREAMING_SNAKE_CASE :				Dict		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase_						)

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]						):
									SCREAMING_SNAKE_CASE :				str		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							SCREAMING_SNAKE_CASE :				Any		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_574						) < 1e-3


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
							SCREAMING_SNAKE_CASE :				int		=			self.full_loop(scheduler=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3

							SCREAMING_SNAKE_CASE :				List[str]		=			DEISMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			DPMSolverMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Any		=			UniPCMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			DPMSolverSinglestepScheduler.from_config(scheduler.config						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.full_loop(scheduler=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[str]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							self.check_over_configs(thresholding=lowerCamelCase_						)
							for order in [1, 2, 3]:
									for solver_type in ["midpoint", "heun"]:
											for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(
															    thresholding=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						sample_max_value=lowerCamelCase_      ,						algorithm_type="""dpmsolver++"""      ,						solver_order=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						)


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							for algorithm_type in ["dpmsolver", "dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
											for order in [1, 2, 3]:
													for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(
															    solver_order=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						algorithm_type=lowerCamelCase_      ,						)
															SCREAMING_SNAKE_CASE :				int		=			self.full_loop(
															    solver_order=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						algorithm_type=lowerCamelCase_      ,						)
															assert not torch.isnan(lowerCamelCase_						).any(), "Samples have nan numbers"


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							self.check_over_configs(lower_order_final=lowerCamelCase_						)
							self.check_over_configs(lower_order_final=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							self.check_over_configs(lambda_min_clipped=-float("""inf"""						)						)
							self.check_over_configs(lambda_min_clipped=-5.1						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							self.check_over_configs(variance_type=lowerCamelCase_						)
							self.check_over_configs(variance_type="""learned_range"""						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
									self.check_over_forward(num_inference_steps=lowerCamelCase_      ,						time_step=0						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.full_loop()
							SCREAMING_SNAKE_CASE :				Any		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			self.full_loop(use_karras_sigmas=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_248						) < 1e-3


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.full_loop(prediction_type="""v_prediction"""						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.1_453						) < 1e-3


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.full_loop(prediction_type="""v_prediction"""      ,						use_karras_sigmas=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.0_649						) < 1e-3



					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_scheduler_config(thresholding=lowerCamelCase_      ,						dynamic_thresholding_ratio=0						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			10
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				int		=			self.dummy_sample_deter.half()
							scheduler.set_timesteps(lowerCamelCase_						)

							for i, t in enumerate(scheduler.timesteps						):
									SCREAMING_SNAKE_CASE :				List[Any]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Any		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							assert sample.dtype == torch.floataa



