




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class    __lowercase						(  _A ):
   _a							=      (DDPMParallelScheduler,)







   def 		UpperCamelCase__			(	self      ,							**UpperCamelCase      )				->    Optional[Any]:
     __a			=					{
         "num_train_timesteps": 1000,
         "beta_start": 0.0_001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "variance_type": "fixed_small",
         "clip_sample": True,
     }

     config.update(**__lowerCamelCase      )
     return config







   def 		UpperCamelCase__			(	self      )				->    str:
     for timesteps in [1, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=__lowerCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]      ,							[0.002, 0.02, 0.2, 2]      ):
       self.check_over_configs(beta_start=__lowerCamelCase      ,							beta_end=__lowerCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     for schedule in ["linear", "squaredcos_cap_v2"]:
       self.check_over_configs(beta_schedule=__lowerCamelCase      )







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







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=__lowerCamelCase      )







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







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







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     for t in [0, 500, 999]:
       self.check_over_forward(time_step=__lowerCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config()
     __a			=					scheduler_class(**__lowerCamelCase      )

     assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.00_979      )      ) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1e-5







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config()
     __a			=					scheduler_class(**__lowerCamelCase      )

     __a			=					len(__lowerCamelCase      )

     __a			=					self.dummy_model()
     __a			=					self.dummy_sample_deter
     __a			=					self.dummy_sample_deter + 0.1
     __a			=					self.dummy_sample_deter - 0.1

     __a			=					samplea.shape[0]
     __a			=					torch.stack([samplea, samplea, samplea]      ,							dim=0      )
     __a			=					torch.arange(__lowerCamelCase      )[0:3, None].repeat(1      ,							__lowerCamelCase      )

     __a			=					model(samples.flatten(0      ,							1      )      ,							timesteps.flatten(0      ,							1      )      )
     __a			=					scheduler.batch_step_no_noise(__lowerCamelCase      ,							timesteps.flatten(0      ,							1      )      ,							samples.flatten(0      ,							1      )      )

     __a			=					torch.sum(torch.abs(__lowerCamelCase      )      )
     __a			=					torch.mean(torch.abs(__lowerCamelCase      )      )

     assert abs(result_sum.item() - 1_153.1_833      ) < 1e-2
     assert abs(result_mean.item() - 0.5_005      ) < 1e-3







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config()
     __a			=					scheduler_class(**__lowerCamelCase      )

     __a			=					len(__lowerCamelCase      )

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

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

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

       __a			=					pred_prev_sample

     __a			=					torch.sum(torch.abs(__lowerCamelCase      )      )
     __a			=					torch.mean(torch.abs(__lowerCamelCase      )      )

     assert abs(result_sum.item() - 258.9_606      ) < 1e-2
     assert abs(result_mean.item() - 0.3_372      ) < 1e-3







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config(prediction_type='v_prediction'      )
     __a			=					scheduler_class(**__lowerCamelCase      )

     __a			=					len(__lowerCamelCase      )

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

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

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

       __a			=					pred_prev_sample

     __a			=					torch.sum(torch.abs(__lowerCamelCase      )      )
     __a			=					torch.mean(torch.abs(__lowerCamelCase      )      )

     assert abs(result_sum.item() - 202.0_296      ) < 1e-2
     assert abs(result_mean.item() - 0.2_631      ) < 1e-3







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config()
     __a			=					scheduler_class(**__lowerCamelCase      )

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

     scheduler.set_timesteps(timesteps=__lowerCamelCase      )

     __a			=					scheduler.timesteps

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

       __a			=					scheduler.previous_timestep(__lowerCamelCase      )
       __a			=					prev_t.item()

       self.assertEqual(__lowerCamelCase      ,							__lowerCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config()
     __a			=					scheduler_class(**__lowerCamelCase      )

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

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







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					self.scheduler_classes[0]
     __a			=					self.get_scheduler_config()
     __a			=					scheduler_class(**__lowerCamelCase      )

     __a			=					[100, 87, 50, 1, 0]
     __a			=					len(__lowerCamelCase      )

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







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

     __a			=					[scheduler.config.num_train_timesteps]

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









'''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__)



