




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class    __lowercase						(  UpperCamelCase_ ):
   _a							=      (DEISMultistepScheduler,)
   _a							=      (("""num_inference_steps""", 25),)







   def 		UpperCamelCase__			(	self      ,							**UpperCamelCase      )				->    List[Any]:
     __a			=					{
         'num_train_timesteps': 1000,
         'beta_start': 0.0_001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
         'solver_order': 2,
     }

     config.update(**__a      )
     return config







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase=0      ,							**UpperCamelCase      )				->    Optional[int]:
     __a			=					dict(self.forward_default_kwargs      )
     __a			=					kwargs.pop('num_inference_steps'      ,							__a      )
     __a			=					self.dummy_sample
     __a			=					0.1 * sample
     __a			=					[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

       __a			=					sample, sample
       for t in range(__a      ,							time_step + scheduler.config.solver_order + 1      ):
         __a			=					scheduler.step(__a      ,							__a      ,							__a      ,							**__a      ).prev_sample
         __a			=					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 		UpperCamelCase__			(	self      )				->    Tuple:
     pass







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase=0      ,							**UpperCamelCase      )				->    Any:
     __a			=					dict(self.forward_default_kwargs      )
     __a			=					kwargs.pop('num_inference_steps'      ,							__a      )
     __a			=					self.dummy_sample
     __a			=					0.1 * sample
     __a			=					[residual + 0.2, residual + 0.15, residual + 0.10]

     for scheduler_class in self.scheduler_classes:
       __a			=					self.get_scheduler_config()
       __a			=					scheduler_class(**__a      )
       scheduler.set_timesteps(__a      )

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

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(__a      )
         __a			=					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)
         __a			=					dummy_past_residuals[: new_scheduler.config.solver_order]

       __a			=					scheduler.step(__a      ,							__a      ,							__a      ,							**__a      ).prev_sample
       __a			=					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 		UpperCamelCase__			(	self      ,							UpperCamelCase=None      ,							**UpperCamelCase      )				->    Optional[Any]:






     if scheduler is None:
       __a			=					self.scheduler_classes[0]
       __a			=					self.get_scheduler_config(**__a      )
       __a			=					scheduler_class(**__a      )

     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config(**__a      )
     __a			=					scheduler_class(**__a      )

     __a			=					10
     __a			=					self.dummy_model()
     __a			=					self.dummy_sample_deter
     scheduler.set_timesteps(__a      )

     for i, t in enumerate(scheduler.timesteps      ):
       __a			=					model(__a      ,							__a      )
       __a			=					scheduler.step(__a      ,							__a      ,							__a      ).prev_sample

     return sample







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					dict(self.forward_default_kwargs      )

     __a			=					kwargs.pop('num_inference_steps'      ,							__a      )

     for scheduler_class in self.scheduler_classes:
       __a			=					self.get_scheduler_config()
       __a			=					scheduler_class(**__a      )

       __a			=					self.dummy_sample
       __a			=					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'      ):
         __a			=					num_inference_steps

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

       __a			=					scheduler.timesteps[5]
       __a			=					scheduler.timesteps[6]

       __a			=					scheduler.step(__a      ,							__a      ,							__a      ,							**__a      ).prev_sample
       __a			=					scheduler.step(__a      ,							__a      ,							__a      ,							**__a      ).prev_sample

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







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					DEISMultistepScheduler(**self.get_scheduler_config()      )
     __a			=					self.full_loop(scheduler=__a      )
     __a			=					torch.mean(torch.abs(__a      )      )

     assert abs(result_mean.item() - 0.23_916      ) < 1e-3

     __a			=					DPMSolverSinglestepScheduler.from_config(scheduler.config      )
     __a			=					DPMSolverMultistepScheduler.from_config(scheduler.config      )
     __a			=					UniPCMultistepScheduler.from_config(scheduler.config      )
     __a			=					DEISMultistepScheduler.from_config(scheduler.config      )

     __a			=					self.full_loop(scheduler=__a      )
     __a			=					torch.mean(torch.abs(__a      )      )

     assert abs(result_mean.item() - 0.23_916      ) < 1e-3







   def 		UpperCamelCase__			(	self      )				->    int:






     for timesteps in [25, 50, 100, 999, 1000]:
       self.check_over_configs(num_train_timesteps=__a      )







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     self.check_over_configs(thresholding=__a      )
     for order in [1, 2, 3]:
       for solver_type in ["logrho"]:
         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='deis'      ,							solver_order=__a      ,							solver_type=__a      ,							)







   def 		UpperCamelCase__			(	self      )				->    int:






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







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:






     for algorithm_type in ["deis"]:
       for solver_type in ["logrho"]:
         for order in [1, 2, 3]:
           for prediction_type in ["epsilon", "sample"]:
             self.check_over_configs(
                 solver_order=__a      ,							solver_type=__a      ,							prediction_type=__a      ,							algorithm_type=__a      ,							)
             __a			=					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 		UpperCamelCase__			(	self      )				->    Any:
     self.check_over_configs(lower_order_final=__a      )
     self.check_over_configs(lower_order_final=__a      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:






     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 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					self.full_loop()
     __a			=					torch.mean(torch.abs(__a      )      )

     assert abs(result_mean.item() - 0.23_916      ) < 1e-3







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					self.full_loop(prediction_type='v_prediction'      )
     __a			=					torch.mean(torch.abs(__a      )      )

     assert abs(result_mean.item() - 0.091      ) < 1e-3







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config(thresholding=__a      ,							dynamic_thresholding_ratio=0      )
     __a			=					scheduler_class(**__a      )

     __a			=					10
     __a			=					self.dummy_model()
     __a			=					self.dummy_sample_deter.half()
     scheduler.set_timesteps(__a      )

     for i, t in enumerate(scheduler.timesteps      ):
       __a			=					model(__a      ,							__a      )
       __a			=					scheduler.step(__a      ,							__a      ,							__a      ).prev_sample

     assert sample.dtype == torch.floataa









'''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__)



