'''simple docstring'''







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							str     		= (DPMSolverSinglestepScheduler,)
						A_			:							Optional[int]     		= (('num_inference_steps', 25),)




						def      __lowerCamelCase       (    self :       Any							,							**UpperCamelCase_ :       Tuple      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Tuple						    = {
													    'num_train_timesteps': 10_00,
													    'beta_start': 0.0001,
													    '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(**UpperCamelCase_      )
													return config




						def      __lowerCamelCase       (    self :       Tuple							,							UpperCamelCase_ :       Tuple=0							,							**UpperCamelCase_ :       List[Any]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :int						    = dict(self.forward_default_kwargs      )
													SCREAMING_SNAKE_CASE__     :str						    = kwargs.pop('num_inference_steps'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = self.dummy_sample
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 0.1 * sample
													SCREAMING_SNAKE_CASE__     :str						    = [residual + 0.2, residual + 0.15, residual + 0.10]

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = self.get_scheduler_config(**UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = scheduler_class(**UpperCamelCase_      )
																				scheduler.set_timesteps(UpperCamelCase_      )
																				# copy over dummy past residuals
																				SCREAMING_SNAKE_CASE__     :Dict						    = dummy_past_residuals[: scheduler.config.solver_order]

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :str						    = scheduler_class.from_pretrained(UpperCamelCase_      )
																											new_scheduler.set_timesteps(UpperCamelCase_      )
																											# copy over dummy past residuals
																											SCREAMING_SNAKE_CASE__     :Tuple						    = dummy_past_residuals[: new_scheduler.config.solver_order]

																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = sample, sample
																				for t in range(UpperCamelCase_							,							time_step + scheduler.config.solver_order + 1      ):
																											SCREAMING_SNAKE_CASE__     :str						    = scheduler.step(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							**UpperCamelCase_      ).prev_sample
																											SCREAMING_SNAKE_CASE__     :List[Any]						    = new_scheduler.step(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							**UpperCamelCase_      ).prev_sample

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




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													pass




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       Union[str, Any]=0							,							**UpperCamelCase_ :       Union[str, Any]      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = dict(self.forward_default_kwargs      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = kwargs.pop('num_inference_steps'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.dummy_sample
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 0.1 * sample
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = [residual + 0.2, residual + 0.15, residual + 0.10]

													for scheduler_class in self.scheduler_classes:
																				SCREAMING_SNAKE_CASE__     :Dict						    = self.get_scheduler_config()
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = scheduler_class(**UpperCamelCase_      )
																				scheduler.set_timesteps(UpperCamelCase_      )

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

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											scheduler.save_config(UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :Dict						    = scheduler_class.from_pretrained(UpperCamelCase_      )
																											# copy over dummy past residuals
																											new_scheduler.set_timesteps(UpperCamelCase_      )

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

																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = scheduler.step(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							**UpperCamelCase_      ).prev_sample
																				SCREAMING_SNAKE_CASE__     :Dict						    = new_scheduler.step(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							**UpperCamelCase_      ).prev_sample

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




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       str=None							,							**UpperCamelCase_ :       str      )     ->				Union[str, Any]:
													if scheduler is None:
																				SCREAMING_SNAKE_CASE__     :int						    = self.scheduler_classes[0]
																				SCREAMING_SNAKE_CASE__     :int						    = self.get_scheduler_config(**UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = scheduler_class(**UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :str						    = self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__     :Any						    = self.get_scheduler_config(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = scheduler_class(**UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Any						    = 10
													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.dummy_model()
													SCREAMING_SNAKE_CASE__     :int						    = self.dummy_sample_deter
													scheduler.set_timesteps(UpperCamelCase_      )

													for i, t in enumerate(scheduler.timesteps      ):
																				SCREAMING_SNAKE_CASE__     :Any						    = model(UpperCamelCase_							,							UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = scheduler.step(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      ).prev_sample

													return sample




						def      __lowerCamelCase       (    self :       int      )     ->				Optional[Any]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
													SCREAMING_SNAKE_CASE__     :int						    = 50
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.dummy_model()
													SCREAMING_SNAKE_CASE__     :str						    = self.dummy_sample_deter
													scheduler.set_timesteps(UpperCamelCase_      )

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

													SCREAMING_SNAKE_CASE__     :str						    = torch.mean(torch.abs(UpperCamelCase_      )      )

													assert abs(result_mean.item() - 0.2574      ) < 1e-3




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Optional[int]:
													for timesteps in [25, 50, 1_00, 9_99, 10_00]:
																				self.check_over_configs(num_train_timesteps=UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													# make sure that iterating over schedulers with same config names gives same results
													# for defaults
													SCREAMING_SNAKE_CASE__     :List[Any]						    = DPMSolverSinglestepScheduler(**self.get_scheduler_config()      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.full_loop(scheduler=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.mean(torch.abs(UpperCamelCase_      )      )

													assert abs(result_mean.item() - 0.2791      ) < 1e-3

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

													SCREAMING_SNAKE_CASE__     :int						    = self.full_loop(scheduler=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = torch.mean(torch.abs(UpperCamelCase_      )      )

													assert abs(result_mean.item() - 0.2791      ) < 1e-3




						def      __lowerCamelCase       (    self :       str      )     ->				Tuple:
													self.check_over_configs(thresholding=UpperCamelCase_      )
													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=UpperCamelCase_							,							prediction_type=UpperCamelCase_							,							sample_max_value=UpperCamelCase_							,							algorithm_type='dpmsolver++'							,							solver_order=UpperCamelCase_							,							solver_type=UpperCamelCase_							,							)




						def      __lowerCamelCase       (    self :       Tuple      )     ->				str:
													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				List[Any]:
													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=UpperCamelCase_							,							solver_type=UpperCamelCase_							,							prediction_type=UpperCamelCase_							,							algorithm_type=UpperCamelCase_							,							)
																																									SCREAMING_SNAKE_CASE__     :str						    = self.full_loop(
																																									    solver_order=UpperCamelCase_							,							solver_type=UpperCamelCase_							,							prediction_type=UpperCamelCase_							,							algorithm_type=UpperCamelCase_							,							)
																																									assert not torch.isnan(UpperCamelCase_      ).any(), "Samples have nan numbers"




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[Any]:
													self.check_over_configs(lower_order_final=UpperCamelCase_      )
													self.check_over_configs(lower_order_final=UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Any      )     ->				Any:
													self.check_over_configs(lambda_min_clipped=-float('inf'      )      )
													self.check_over_configs(lambda_min_clipped=-5.1      )




						def      __lowerCamelCase       (    self :       Dict      )     ->				Any:
													self.check_over_configs(variance_type=UpperCamelCase_      )
													self.check_over_configs(variance_type='learned_range'      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				str:
													for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
																				self.check_over_forward(num_inference_steps=UpperCamelCase_							,							time_step=0      )




						def      __lowerCamelCase       (    self :       Dict      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :str						    = self.full_loop()
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = torch.mean(torch.abs(UpperCamelCase_      )      )

													assert abs(result_mean.item() - 0.2791      ) < 1e-3




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Any						    = self.full_loop(use_karras_sigmas=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :str						    = torch.mean(torch.abs(UpperCamelCase_      )      )

													assert abs(result_mean.item() - 0.2248      ) < 1e-3




						def      __lowerCamelCase       (    self :       int      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Any						    = self.full_loop(prediction_type='v_prediction'      )
													SCREAMING_SNAKE_CASE__     :str						    = torch.mean(torch.abs(UpperCamelCase_      )      )

													assert abs(result_mean.item() - 0.1453      ) < 1e-3




						def      __lowerCamelCase       (    self :       Any      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :str						    = self.full_loop(prediction_type='v_prediction'							,							use_karras_sigmas=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = torch.mean(torch.abs(UpperCamelCase_      )      )

													assert abs(result_mean.item() - 0.0649      ) < 1e-3




						def      __lowerCamelCase       (    self :       Any      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.scheduler_classes[0]
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.get_scheduler_config(thresholding=UpperCamelCase_							,							dynamic_thresholding_ratio=0      )
													SCREAMING_SNAKE_CASE__     :Dict						    = scheduler_class(**UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 10
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.dummy_model()
													SCREAMING_SNAKE_CASE__     :Dict						    = self.dummy_sample_deter.half()
													scheduler.set_timesteps(UpperCamelCase_      )

													for i, t in enumerate(scheduler.timesteps      ):
																				SCREAMING_SNAKE_CASE__     :Tuple						    = model(UpperCamelCase_							,							UpperCamelCase_      )
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = scheduler.step(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_      ).prev_sample

													assert sample.dtype == torch.floataa







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_      	=	{
    '''configuration_x_clip''': [
        '''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XCLIPConfig''',
        '''XCLIPTextConfig''',
        '''XCLIPVisionConfig''',
    ],
    '''processing_x_clip''': ['''XCLIPProcessor'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XCLIPModel''',
		    '''XCLIPPreTrainedModel''',
		    '''XCLIPTextModel''',
		    '''XCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_x_clip import (
		    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XCLIPConfig,
		    XCLIPTextConfig,
		    XCLIPVisionConfig,
		)
		from .processing_x_clip import XCLIPProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_x_clip import (
				    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XCLIPModel,
				    XCLIPPreTrainedModel,
				    XCLIPTextModel,
				    XCLIPVisionModel,
				)

else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






