


'''simple docstring'''







from typing import TYPE_CHECKING

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


a__  : int				=	{
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : int				=	['BartTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : List[str]				=	[
      'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BartForCausalLM',
      'BartForConditionalGeneration',
      'BartForQuestionAnswering',
      'BartForSequenceClassification',
      'BartModel',
      'BartPretrainedModel',
      'PretrainedBartModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Union[str, Any]				=	[
      'TFBartForConditionalGeneration',
      'TFBartForSequenceClassification',
      'TFBartModel',
      'TFBartPretrainedModel',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__  : Union[str, Any]				=	[
      'FlaxBartDecoderPreTrainedModel',
      'FlaxBartForCausalLM',
      'FlaxBartForConditionalGeneration',
      'FlaxBartForQuestionAnswering',
      'FlaxBartForSequenceClassification',
      'FlaxBartModel',
      'FlaxBartPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  from .tokenization_bart import BartTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bart_fast import BartTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bart import (
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
        BartForCausalLM,
        BartForConditionalGeneration,
        BartForQuestionAnswering,
        BartForSequenceClassification,
        BartModel,
        BartPretrainedModel,
        PretrainedBartModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bart import (
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        TFBartModel,
        TFBartPretrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bart import (
        FlaxBartDecoderPreTrainedModel,
        FlaxBartForCausalLM,
        FlaxBartForConditionalGeneration,
        FlaxBartForQuestionAnswering,
        FlaxBartForSequenceClassification,
        FlaxBartModel,
        FlaxBartPreTrainedModel,
    )

else:
  import sys

  a__  : int				=	_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  lowercase_					(   a__       ):
    __UpperCAmelCase             =     (DPMSolverMultistepScheduler,)
    __UpperCAmelCase             =     (('num_inference_steps', 25),)


    def        __a							(					self  , **a   ):
          UpperCamelCase__  =    {
              "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",
              "lower_order_final": False,
              "lambda_min_clipped": -float("inf"   ),
              "variance_type": None,
          }

          config.update(**a   )
          return config


    def        __a							(					self  , a=0  , **a   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )
          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )
          UpperCamelCase__  =    self.dummy_sample
          UpperCamelCase__  =    0.1 * sample
          UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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


    def        __a							(					self  , a=0  , **a   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )
          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )
          UpperCamelCase__  =    self.dummy_sample
          UpperCamelCase__  =    0.1 * sample
          UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                UpperCamelCase__  =    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        __a							(					self  , a=None  , **a   ):
          if scheduler is None:
                UpperCamelCase__  =    self.scheduler_classes[0]
                UpperCamelCase__  =    self.get_scheduler_config(**a   )
                UpperCamelCase__  =    scheduler_class(**a   )

          UpperCamelCase__  =    10
          UpperCamelCase__  =    self.dummy_model()
          UpperCamelCase__  =    self.dummy_sample_deter
          scheduler.set_timesteps(a   )

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

          return sample


    def        __a							(					self   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )

          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )

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

                UpperCamelCase__  =    self.dummy_sample
                UpperCamelCase__  =    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"   ):
                      UpperCamelCase__  =    num_inference_steps

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

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

                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample

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


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


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


    def        __a							(					self   ):
          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  , )
                                  UpperCamelCase__  =    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        __a							(					self   ):
          self.check_over_configs(lower_order_final=a   )
          self.check_over_configs(lower_order_final=a   )


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


    def        __a							(					self   ):
          self.check_over_configs(variance_type=a   )
          self.check_over_configs(variance_type="learned_range"   )


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


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop()
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

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


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(thresholding=a  , dynamic_thresholding_ratio=0.87  , sample_max_value=0.5   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

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


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(prediction_type="v_prediction"   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

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


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(prediction_type="v_prediction"  , use_karras_sigmas=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

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


    def        __a							(					self   ):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          UpperCamelCase__  =    DPMSolverMultistepScheduler(**self.get_scheduler_config()   )
          UpperCamelCase__  =    self.full_loop(scheduler=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

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

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

          UpperCamelCase__  =    self.full_loop(scheduler=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

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


    def        __a							(					self   ):
          UpperCamelCase__  =    self.scheduler_classes[0]
          UpperCamelCase__  =    self.get_scheduler_config(thresholding=a  , dynamic_thresholding_ratio=0   )
          UpperCamelCase__  =    scheduler_class(**a   )

          UpperCamelCase__  =    10
          UpperCamelCase__  =    self.dummy_model()
          UpperCamelCase__  =    self.dummy_sample_deter.half()
          scheduler.set_timesteps(a   )

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

          assert sample.dtype == torch.floataa


    def        __a							(					self  , **a   ):
          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config(**a   )
                UpperCamelCase__  =    scheduler_class(**a   )

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





