
"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		(UnCLIPScheduler,)





     def 		UpperCAmelCase      (			self			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= {
          'num_train_timesteps': 1000,
          'variance_type': 'fixed_small_log',
          'clip_sample': True,
          'clip_sample_range': 1.0,
          'prediction_type': 'epsilon',
      }

      config.update(**_UpperCAmelCase   )
      return config





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for timesteps in [1, 5, 100, 1000]:
       self.check_over_configs(num_train_timesteps=_UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for variance in ["fixed_small_log", "learned_range"]:
       self.check_over_configs(variance_type=_UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for clip_sample in [True, False]:
       self.check_over_configs(clip_sample=_UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for clip_sample_range in [1, 5, 10, 20]:
       self.check_over_configs(clip_sample_range=_UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for prediction_type in ["epsilon", "sample"]:
       self.check_over_configs(prediction_type=_UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for time_step in [0, 500, 999]:
       for prev_timestep in [None, 5, 100, 250, 500, 750]:
        if prev_timestep is not None and prev_timestep >= time_step:
         continue

        self.check_over_forward(time_step=_UpperCAmelCase			,  prev_timestep=_UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.scheduler_classes[0]
      __UpperCamelCase       						= self.get_scheduler_config(variance_type='fixed_small_log'   )
      __UpperCamelCase       						= scheduler_class(**_UpperCAmelCase   )

      assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 1.0000E-10   )   ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_5_4_9_6_2_5   )   ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.9_9_9_4_9_8_7   )   ) < 1E-5





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.scheduler_classes[0]
      __UpperCamelCase       						= self.get_scheduler_config(variance_type='learned_range'   )
      __UpperCamelCase       						= scheduler_class(**_UpperCAmelCase   )

      __UpperCamelCase       						= 0.5

      assert scheduler._get_variance(1			,  predicted_variance=_UpperCAmelCase   ) - -1_0.1_7_1_2_7_9_0 < 1E-5
      assert scheduler._get_variance(487			,  predicted_variance=_UpperCAmelCase   ) - -5.7_9_9_8_0_5_2 < 1E-5
      assert scheduler._get_variance(999			,  predicted_variance=_UpperCAmelCase   ) - -0.0_0_1_0_0_1_1 < 1E-5





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      __UpperCamelCase       						= scheduler.timesteps

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

      for i, t in enumerate(_UpperCAmelCase   ):
       # 1. predict noise residual
       __UpperCamelCase       						= model(_UpperCAmelCase			,  _UpperCAmelCase   )

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

       __UpperCamelCase       						= pred_prev_sample

      __UpperCamelCase       						= torch.sum(torch.abs(_UpperCAmelCase   )   )
      __UpperCamelCase       						= torch.mean(torch.abs(_UpperCAmelCase   )   )

      assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5   ) < 1E-2
      assert abs(result_mean.item() - 0.3_2_8_4_7_4_3   ) < 1E-3





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      scheduler.set_timesteps(25   )

      __UpperCamelCase       						= scheduler.timesteps

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

      for i, t in enumerate(_UpperCAmelCase   ):
       # 1. predict noise residual
       __UpperCamelCase       						= model(_UpperCAmelCase			,  _UpperCAmelCase   )

       if i + 1 == timesteps.shape[0]:
        __UpperCamelCase       						= None
       else:
        __UpperCamelCase       						= timesteps[i + 1]

       # 2. predict previous mean of sample x_t-1
       __UpperCamelCase       						= scheduler.step(
           _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  prev_timestep=_UpperCAmelCase			,  generator=_UpperCAmelCase   ).prev_sample

       __UpperCamelCase       						= pred_prev_sample

      __UpperCamelCase       						= torch.sum(torch.abs(_UpperCAmelCase   )   )
      __UpperCamelCase       						= torch.mean(torch.abs(_UpperCAmelCase   )   )

      assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3   ) < 1E-2
      assert abs(result_mean.item() - 0.3_3_6_2_0_3_8   ) < 1E-3





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





"""simple docstring"""

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase			:       List[str]									=				{
    "configuration_autoformer": [
        "AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AutoformerConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Dict									=				[
          "AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "AutoformerForPrediction",
          "AutoformerModel",
          "AutoformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase			:       int									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



