





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    (UniPCMultistepScheduler,)
       __snake_case 			=    (('num_inference_steps', 2_5),)




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							{
                "num_train_timesteps": 1_0_0_0,
                "beta_start": 0.0001,
                "beta_end": 0.02,
                "beta_schedule": "linear",
                "solver_order": 2,
                "solver_type": "bh2",
            }

            config.update(**__lowerCamelCase							)
            return config




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

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

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

                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							sample, sample
                 for t in range(__lowerCamelCase    ,						time_step + scheduler.config.solver_order + 1							):
                      _SCREAMING_SNAKE_CASE       :	List[str]						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase							).prev_sample
                      _SCREAMING_SNAKE_CASE       :	str						    =							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     UpperCamelCase_			(      self    ,						__lowerCamelCase=0    ,						**__lowerCamelCase							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							dict(self.forward_default_kwargs							)
            _SCREAMING_SNAKE_CASE       :	int						    =							kwargs.pop("num_inference_steps"    ,						__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.dummy_sample
            _SCREAMING_SNAKE_CASE       :	int						    =							0.1 * sample
            _SCREAMING_SNAKE_CASE       :	List[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       :	Tuple						    =							scheduler_class(**__lowerCamelCase							)
                 scheduler.set_timesteps(__lowerCamelCase							)

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowerCamelCase							)
                      _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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       :	Union[str, Any]						    =							dummy_past_residuals[: new_scheduler.config.solver_order]

                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							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     UpperCamelCase_			(      self    ,						__lowerCamelCase=None    ,						**__lowerCamelCase							)			->     Dict:
            if scheduler is None:
                 _SCREAMING_SNAKE_CASE       :	Any						    =							self.scheduler_classes[0]
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.get_scheduler_config(**__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	str						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	int						    =							self.get_scheduler_config(**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	int						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							1_0
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.dummy_model()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.dummy_sample_deter
            scheduler.set_timesteps(__lowerCamelCase							)

            for i, t in enumerate(scheduler.timesteps							):
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							model(__lowerCamelCase    ,						__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	int						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							).prev_sample

            return sample




       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							dict(self.forward_default_kwargs							)

            _SCREAMING_SNAKE_CASE       :	str						    =							kwargs.pop("num_inference_steps"    ,						__lowerCamelCase							)

            for scheduler_class in self.scheduler_classes:
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_scheduler_config()
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							scheduler_class(**__lowerCamelCase							)

                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.dummy_sample
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							0.1 * sample

                 if num_inference_steps is not None and hasattr(__lowerCamelCase    ,						"set_timesteps"							):
                      scheduler.set_timesteps(__lowerCamelCase							)
                 elif num_inference_steps is not None and not hasattr(__lowerCamelCase    ,						"set_timesteps"							):
                      _SCREAMING_SNAKE_CASE       :	Dict						    =							num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							[residual + 0.2, residual + 0.15, residual + 0.10]
                 _SCREAMING_SNAKE_CASE       :	str						    =							dummy_past_residuals[: scheduler.config.solver_order]

                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							scheduler.timesteps[5]
                 _SCREAMING_SNAKE_CASE       :	int						    =							scheduler.timesteps[6]

                 _SCREAMING_SNAKE_CASE       :	str						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase							).prev_sample
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						**__lowerCamelCase							).prev_sample

                 self.assertEqual(output_a.shape    ,						sample.shape							)
                 self.assertEqual(output_a.shape    ,						output_a.shape							)




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

            assert abs(result_mean.item() - 0.2464							) < 1E-3

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							DPMSolverSinglestepScheduler.from_config(scheduler.config							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							DEISMultistepScheduler.from_config(scheduler.config							)
            _SCREAMING_SNAKE_CASE       :	int						    =							DPMSolverMultistepScheduler.from_config(scheduler.config							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							UniPCMultistepScheduler.from_config(scheduler.config							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.full_loop(scheduler=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							torch.mean(torch.abs(__lowerCamelCase							)							)

            assert abs(result_mean.item() - 0.2464							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Tuple:
            self.check_over_configs(thresholding=__lowerCamelCase							)
            for order in [1, 2, 3]:
                 for solver_type in ["bh1", "bh2"]:
                      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    ,						solver_order=__lowerCamelCase    ,						solver_type=__lowerCamelCase    ,						)




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




       def     UpperCamelCase_			(      self							)			->     Tuple:
            for solver_type in ["bh1", "bh2"]:
                 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    ,						)
                           _SCREAMING_SNAKE_CASE       :	Any						    =							self.full_loop(
                               solver_order=__lowerCamelCase    ,						solver_type=__lowerCamelCase    ,						prediction_type=__lowerCamelCase    ,						)
                           assert not torch.isnan(__lowerCamelCase							).any(), "Samples have nan numbers"




       def     UpperCamelCase_			(      self							)			->     Tuple:
            self.check_over_configs(lower_order_final=__lowerCamelCase							)
            self.check_over_configs(lower_order_final=__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     List[Any]:
            for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=__lowerCamelCase    ,						time_step=0							)




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

            assert abs(result_mean.item() - 0.2464							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.full_loop(prediction_type="v_prediction"							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							torch.mean(torch.abs(__lowerCamelCase							)							)

            assert abs(result_mean.item() - 0.1014							) < 1E-3




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							self.scheduler_classes[0]
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_scheduler_config(thresholding=__lowerCamelCase    ,						dynamic_thresholding_ratio=0							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							scheduler_class(**__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							1_0
            _SCREAMING_SNAKE_CASE       :	int						    =							self.dummy_model()
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							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       :	Optional[int]						    =							scheduler.step(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							).prev_sample

            assert sample.dtype == torch.floataa







       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     Dict:
            for scheduler_class in self.scheduler_classes:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.get_scheduler_config(**__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							scheduler_class(**__lowerCamelCase							)

                 scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
                 assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LiltForQuestionAnswering',
           'LiltForSequenceClassification',
           'LiltForTokenClassification',
           'LiltModel',
           'LiltPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

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