



'''simple docstring'''





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




  a_							 =(DPMSolverMultistepScheduler,)
  a_							 =(("""num_inference_steps""", 25),)




  def 						_lowercase					(			self				:     int      ,     **_a				:     Dict       ) -> Optional[Any]:
    __lowerCamelCase	:	Union[str, Any]   	=			{
        'num_train_timesteps': 1000,
        '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',
        'lower_order_final': False,
        'lambda_min_clipped': -float('inf'       ),
        'variance_type': None,
    }

    config.update(**_a       )
    return config




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

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

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

      __lowerCamelCase	,__lowerCamelCase	:	Optional[Any]   	=			sample, sample
      for t in range(_a      ,     time_step + scheduler.config.solver_order + 1       ):
        __lowerCamelCase	:	List[Any]   	=			scheduler.step(_a      ,     _a      ,     _a      ,     **_a       ).prev_sample
        __lowerCamelCase	:	Dict   	=			new_scheduler.step(_a      ,     _a      ,     _a      ,     **_a       ).prev_sample

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




  def 						_lowercase					(			self				:     int       ) -> List[Any]:
    pass




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

    for scheduler_class in self.scheduler_classes:
      __lowerCamelCase	:	List[Any]   	=			self.get_scheduler_config()
      __lowerCamelCase	:	int   	=			scheduler_class(**_a       )
      scheduler.set_timesteps(_a       )

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

      with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(_a       )
        __lowerCamelCase	:	Any   	=			scheduler_class.from_pretrained(_a       )
        # copy over dummy past residuals
        new_scheduler.set_timesteps(_a       )

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

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

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




  def 						_lowercase					(			self				:     List[str]      ,     _a				:     int=None      ,     **_a				:     str       ) -> Dict:
    if scheduler is None:
      __lowerCamelCase	:	Any   	=			self.scheduler_classes[0]
      __lowerCamelCase	:	Any   	=			self.get_scheduler_config(**_a       )
      __lowerCamelCase	:	Dict   	=			scheduler_class(**_a       )

    __lowerCamelCase	:	str   	=			10
    __lowerCamelCase	:	Union[str, Any]   	=			self.dummy_model()
    __lowerCamelCase	:	Optional[Any]   	=			self.dummy_sample_deter
    scheduler.set_timesteps(_a       )

    for i, t in enumerate(scheduler.timesteps       ):
      __lowerCamelCase	:	List[str]   	=			model(_a      ,     _a       )
      __lowerCamelCase	:	Optional[Any]   	=			scheduler.step(_a      ,     _a      ,     _a       ).prev_sample

    return sample




  def 						_lowercase					(			self				:     int       ) -> Tuple:
    __lowerCamelCase	:	Any   	=			dict(self.forward_default_kwargs       )

    __lowerCamelCase	:	str   	=			kwargs.pop('num_inference_steps'      ,     _a       )

    for scheduler_class in self.scheduler_classes:
      __lowerCamelCase	:	List[Any]   	=			self.get_scheduler_config()
      __lowerCamelCase	:	Any   	=			scheduler_class(**_a       )

      __lowerCamelCase	:	Dict   	=			self.dummy_sample
      __lowerCamelCase	:	Optional[Any]   	=			0.1 * sample

      if num_inference_steps is not None and hasattr(_a      ,     'set_timesteps'       ):
        scheduler.set_timesteps(_a       )
      elif num_inference_steps is not None and not hasattr(_a      ,     'set_timesteps'       ):
        __lowerCamelCase	:	Any   	=			num_inference_steps

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

      __lowerCamelCase	:	List[Any]   	=			scheduler.timesteps[5]
      __lowerCamelCase	:	Tuple   	=			scheduler.timesteps[6]

      __lowerCamelCase	:	Any   	=			scheduler.step(_a      ,     _a      ,     _a      ,     **_a       ).prev_sample
      __lowerCamelCase	:	List[Any]   	=			scheduler.step(_a      ,     _a      ,     _a      ,     **_a       ).prev_sample

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




  def 						_lowercase					(			self				:     str       ) -> str:
    for timesteps in [25, 50, 100, 999, 1000]:
      self.check_over_configs(num_train_timesteps=_a       )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Dict:
    self.check_over_configs(thresholding=_a       )
    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=_a      ,     prediction_type=_a      ,     sample_max_value=_a      ,     algorithm_type='dpmsolver++'      ,     solver_order=_a      ,     solver_type=_a      ,     )




  def 						_lowercase					(			self				:     Tuple       ) -> int:
    for prediction_type in ["epsilon", "v_prediction"]:
      self.check_over_configs(prediction_type=_a       )




  def 						_lowercase					(			self				:     Any       ) -> int:
    for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
      for solver_type in ["midpoint", "heun"]:
        for order in [1, 2, 3]:
          for prediction_type in ["epsilon", "sample"]:
            if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
              if order == 3:
                continue
            else:
              self.check_over_configs(
                  solver_order=_a      ,     solver_type=_a      ,     prediction_type=_a      ,     algorithm_type=_a      ,     )
            __lowerCamelCase	:	Any   	=			self.full_loop(
                solver_order=_a      ,     solver_type=_a      ,     prediction_type=_a      ,     algorithm_type=_a      ,     )
            assert not torch.isnan(_a       ).any(), "Samples have nan numbers"




  def 						_lowercase					(			self				:     Dict       ) -> Dict:
    self.check_over_configs(lower_order_final=_a       )
    self.check_over_configs(lower_order_final=_a       )




  def 						_lowercase					(			self				:     List[str]       ) -> Union[str, Any]:
    self.check_over_configs(lambda_min_clipped=-float('inf'       )       )
    self.check_over_configs(lambda_min_clipped=-5.1       )




  def 						_lowercase					(			self				:     Tuple       ) -> List[Any]:
    self.check_over_configs(variance_type=_a       )
    self.check_over_configs(variance_type='learned_range'       )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> List[str]:
    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
      self.check_over_forward(num_inference_steps=_a      ,     time_step=0       )




  def 						_lowercase					(			self				:     str       ) -> Dict:
    __lowerCamelCase	:	List[Any]   	=			self.full_loop()
    __lowerCamelCase	:	List[Any]   	=			torch.mean(torch.abs(_a       )       )

    assert abs(result_mean.item() - 0.3301       ) < 1e-3




  def 						_lowercase					(			self				:     Any       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[int]   	=			self.full_loop(thresholding=_a      ,     dynamic_thresholding_ratio=0.87      ,     sample_max_value=0.5       )
    __lowerCamelCase	:	List[Any]   	=			torch.mean(torch.abs(_a       )       )

    assert abs(result_mean.item() - 1.1364       ) < 1e-3




  def 						_lowercase					(			self				:     Optional[Any]       ) -> Union[str, Any]:
    __lowerCamelCase	:	str   	=			self.full_loop(prediction_type='v_prediction'       )
    __lowerCamelCase	:	Tuple   	=			torch.mean(torch.abs(_a       )       )

    assert abs(result_mean.item() - 0.2251       ) < 1e-3




  def 						_lowercase					(			self				:     int       ) -> Dict:
    __lowerCamelCase	:	Optional[int]   	=			self.full_loop(prediction_type='v_prediction'      ,     use_karras_sigmas=_a       )
    __lowerCamelCase	:	str   	=			torch.mean(torch.abs(_a       )       )

    assert abs(result_mean.item() - 0.2096       ) < 1e-3




  def 						_lowercase					(			self				:     Tuple       ) -> Tuple:
    # make sure that iterating over schedulers with same config names gives same results
    # for defaults
    __lowerCamelCase	:	Tuple   	=			DPMSolverMultistepScheduler(**self.get_scheduler_config()       )
    __lowerCamelCase	:	Tuple   	=			self.full_loop(scheduler=_a       )
    __lowerCamelCase	:	int   	=			torch.mean(torch.abs(_a       )       )

    assert abs(result_mean.item() - 0.3301       ) < 1e-3

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

    __lowerCamelCase	:	str   	=			self.full_loop(scheduler=_a       )
    __lowerCamelCase	:	Any   	=			torch.mean(torch.abs(_a       )       )

    assert abs(result_mean.item() - 0.3301       ) < 1e-3




  def 						_lowercase					(			self				:     int       ) -> List[Any]:
    __lowerCamelCase	:	Union[str, Any]   	=			self.scheduler_classes[0]
    __lowerCamelCase	:	Any   	=			self.get_scheduler_config(thresholding=_a      ,     dynamic_thresholding_ratio=0       )
    __lowerCamelCase	:	List[Any]   	=			scheduler_class(**_a       )

    __lowerCamelCase	:	Dict   	=			10
    __lowerCamelCase	:	Tuple   	=			self.dummy_model()
    __lowerCamelCase	:	Dict   	=			self.dummy_sample_deter.half()
    scheduler.set_timesteps(_a       )

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

    assert sample.dtype == torch.floataa




  def 						_lowercase					(			self				:     Optional[Any]      ,     **_a				:     Optional[Any]       ) -> Union[str, Any]:
    for scheduler_class in self.scheduler_classes:
      __lowerCamelCase	:	Union[str, Any]   	=			self.get_scheduler_config(**_a       )
      __lowerCamelCase	:	Any   	=			scheduler_class(**_a       )

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











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase							    =						{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['ImageGPTFeatureExtractor']
   _UpperCamelCase							    =						['ImageGPTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ImageGPTForCausalImageModeling',
       'ImageGPTForImageClassification',
       'ImageGPTModel',
       'ImageGPTPreTrainedModel',
       'load_tf_weights_in_imagegpt',
   ]


if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )

else:
   import sys

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






