import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						(DDPMScheduler,)







  def    __A     (							self :       Any  ,     **__magic_name__ :       int   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  {
            "num_train_timesteps": 1_000,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "variance_type": "fixed_small",
            "clip_sample": True,
        }

        config.update(**__magic_name__   )
        return config







  def    __A     (							self :       Optional[Any]   )  ->	Optional[Any]:
        for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=__magic_name__   )







  def    __A     (							self :       List[str]   )  ->	Tuple:
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]  ,     [0.002, 0.02, 0.2, 2]   ):
              self.check_over_configs(beta_start=__magic_name__  ,     beta_end=__magic_name__   )







  def    __A     (							self :       int   )  ->	List[str]:
        for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	List[Any]:
        for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=__magic_name__   )







  def    __A     (							self :       Tuple   )  ->	Optional[int]:
        for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=__magic_name__   )







  def    __A     (							self :       List[Any]   )  ->	List[str]:
        self.check_over_configs(thresholding=__magic_name__   )
        for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__magic_name__  ,     prediction_type=__magic_name__  ,     sample_max_value=__magic_name__  ,     )







  def    __A     (							self :       Optional[Any]   )  ->	Union[str, Any]:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=__magic_name__   )







  def    __A     (							self :       List[Any]   )  ->	Tuple:
        for t in [0, 500, 999]:
              self.check_over_forward(time_step=__magic_name__   )







  def    __A     (							self :       int   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

        assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0979   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.02   )   ) < 1e-5







  def    __A     (							self :       Tuple   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  len(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.dummy_model()
        SCREAMING_SNAKE_CASE_          =  self.dummy_sample_deter
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )

        for t in reversed(range(__magic_name__   )   ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     __magic_name__   )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE_          =  scheduler.step(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     generator=__magic_name__   ).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              SCREAMING_SNAKE_CASE_          =  pred_prev_sample

        SCREAMING_SNAKE_CASE_          =  torch.sum(torch.abs(__magic_name__   )   )
        SCREAMING_SNAKE_CASE_          =  torch.mean(torch.abs(__magic_name__   )   )

        assert abs(result_sum.item() - 258.9606   ) < 1e-2
        assert abs(result_mean.item() - 0.3372   ) < 1e-3







  def    __A     (							self :       Any   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config(prediction_type="v_prediction"   )
        SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  len(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.dummy_model()
        SCREAMING_SNAKE_CASE_          =  self.dummy_sample_deter
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )

        for t in reversed(range(__magic_name__   )   ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     __magic_name__   )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE_          =  scheduler.step(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     generator=__magic_name__   ).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              SCREAMING_SNAKE_CASE_          =  pred_prev_sample

        SCREAMING_SNAKE_CASE_          =  torch.sum(torch.abs(__magic_name__   )   )
        SCREAMING_SNAKE_CASE_          =  torch.mean(torch.abs(__magic_name__   )   )

        assert abs(result_sum.item() - 202.0296   ) < 1e-2
        assert abs(result_mean.item() - 0.2631   ) < 1e-3







  def    __A     (							self :       Union[str, Any]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  [100, 87, 50, 1, 0]

        scheduler.set_timesteps(timesteps=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  scheduler.timesteps

        for i, timestep in enumerate(__magic_name__   ):
              if i == len(__magic_name__   ) - 1:
                    SCREAMING_SNAKE_CASE_          =  -1
              else:
                    SCREAMING_SNAKE_CASE_          =  timesteps[i + 1]

              SCREAMING_SNAKE_CASE_          =  scheduler.previous_timestep(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  prev_t.item()

              self.assertEqual(__magic_name__  ,     __magic_name__   )







  def    __A     (							self :       List[str]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  [100, 87, 50, 51, 0]

        with self.assertRaises(__magic_name__  ,     msg="`custom_timesteps` must be in descending order."   ):
              scheduler.set_timesteps(timesteps=__magic_name__   )







  def    __A     (							self :       Dict   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  [100, 87, 50, 1, 0]
        SCREAMING_SNAKE_CASE_          =  len(__magic_name__   )

        with self.assertRaises(__magic_name__  ,     msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."   ):
              scheduler.set_timesteps(num_inference_steps=__magic_name__  ,     timesteps=__magic_name__   )







  def    __A     (							self :       Any   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_          =  self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_          =  scheduler_class(**__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  [scheduler.config.num_train_timesteps]

        with self.assertRaises(
            __magic_name__  ,     msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"  ,     ):
              scheduler.set_timesteps(timesteps=__magic_name__   )



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_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


