


'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     _lowerCamelCase       (     _UpperCamelCase					):




 '''simple docstring'''
 __lowercase					:		Optional[Any]     				=    (DDIMParallelScheduler,)
 __lowercase					:		List[Any]     				=    (('''eta''', 0.0), ('''num_inference_steps''', 5_0))


 def 			snake_case__ (   self   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      {
      'num_train_timesteps': 1_000,
      'beta_start': 0.0_0_0_1,
      'beta_end': 0.0_2,
      'beta_schedule': 'linear',
      'clip_sample': True,
  }

  config.update(**UpperCamelCase__    )
  return config


 def 			snake_case__ (   self   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		int	  =      self.scheduler_classes[0]
  __A	:		List[Any]	  =      self.get_scheduler_config(**UpperCamelCase__    )
  __A	:		Tuple	  =      scheduler_class(**UpperCamelCase__    )

  __A  ,__A	:		List[Any]	  =      10, 0.0

  __A	:		Tuple	  =      self.dummy_model()
  __A	:		List[Any]	  =      self.dummy_sample_deter

  scheduler.set_timesteps(UpperCamelCase__    )

  for t in scheduler.timesteps:
   __A	:		Optional[Any]	  =      model(UpperCamelCase__   ,   UpperCamelCase__    )
   __A	:		List[Any]	  =      scheduler.step(UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__   ,   UpperCamelCase__    ).prev_sample

  return sample


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for timesteps in [100, 500, 1_000]:
   self.check_over_configs(num_train_timesteps=UpperCamelCase__    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for steps_offset in [0, 1]:
   self.check_over_configs(steps_offset=UpperCamelCase__    )

  __A	:		Optional[Any]	  =      self.scheduler_classes[0]
  __A	:		Any	  =      self.get_scheduler_config(steps_offset=1    )
  __A	:		List[Any]	  =      scheduler_class(**UpperCamelCase__    )
  scheduler.set_timesteps(5    )
  assert torch.equal(scheduler.timesteps   ,   torch.LongTensor([801, 601, 401, 201, 1]    )    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,   [0.0_0_2, 0.0_2, 0.2, 2]    ):
   self.check_over_configs(beta_start=UpperCamelCase__   ,   beta_end=UpperCamelCase__    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for schedule in ["linear", "squaredcos_cap_v2"]:
   self.check_over_configs(beta_schedule=UpperCamelCase__    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







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


 def 			snake_case__ (   self    ):

  """simple docstring"""







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


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for timestep_spacing in ["trailing", "leading"]:
   self.check_over_configs(timestep_spacing=UpperCamelCase__    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for rescale_betas_zero_snr in [True, False]:
   self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase__    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self.check_over_configs(thresholding=UpperCamelCase__    )
  for threshold in [0.5, 1.0, 2.0]:
   for prediction_type in ["epsilon", "v_prediction"]:
    self.check_over_configs(
        thresholding=UpperCamelCase__   ,   prediction_type=UpperCamelCase__   ,   sample_max_value=UpperCamelCase__   ,   )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for t in [1, 10, 49]:
   self.check_over_forward(time_step=UpperCamelCase__    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for t, num_inference_steps in zip([1, 10, 50]   ,   [10, 50, 500]    ):
   self.check_over_forward(time_step=UpperCamelCase__   ,   num_inference_steps=UpperCamelCase__    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for t, eta in zip([1, 10, 49]   ,   [0.0, 0.5, 1.0]    ):
   self.check_over_forward(time_step=UpperCamelCase__   ,   eta=UpperCamelCase__    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      self.scheduler_classes[0]
  __A	:		Dict	  =      self.get_scheduler_config()
  __A	:		str	  =      scheduler_class(**UpperCamelCase__    )

  assert torch.sum(torch.abs(scheduler._get_variance(0   ,   0    ) - 0.0    )    ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(420   ,   400    ) - 0.1_4_7_7_1    )    ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(980   ,   960    ) - 0.3_2_4_6_0    )    ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(0   ,   0    ) - 0.0    )    ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(487   ,   486    ) - 0.0_0_9_7_9    )    ) < 1E-5
  assert torch.sum(torch.abs(scheduler._get_variance(999   ,   998    ) - 0.0_2    )    ) < 1E-5


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      self.scheduler_classes[0]
  __A	:		Any	  =      self.get_scheduler_config()
  __A	:		List[str]	  =      scheduler_class(**UpperCamelCase__    )

  __A  ,__A	:		Optional[Any]	  =      10, 0.0
  scheduler.set_timesteps(UpperCamelCase__    )

  __A	:		str	  =      self.dummy_model()
  __A	:		Any	  =      self.dummy_sample_deter
  __A	:		Optional[int]	  =      self.dummy_sample_deter + 0.1
  __A	:		Tuple	  =      self.dummy_sample_deter - 0.1

  __A	:		Tuple	  =      samplea.shape[0]
  __A	:		Optional[int]	  =      torch.stack([samplea, samplea, samplea]   ,   dim=0    )
  __A	:		int	  =      torch.arange(UpperCamelCase__    )[0:3, None].repeat(1   ,   UpperCamelCase__    )

  __A	:		Tuple	  =      model(samples.flatten(0   ,   1    )   ,   timesteps.flatten(0   ,   1    )    )
  __A	:		Dict	  =      scheduler.batch_step_no_noise(UpperCamelCase__   ,   timesteps.flatten(0   ,   1    )   ,   samples.flatten(0   ,   1    )   ,   UpperCamelCase__    )

  __A	:		str	  =      torch.sum(torch.abs(UpperCamelCase__    )    )
  __A	:		Union[str, Any]	  =      torch.mean(torch.abs(UpperCamelCase__    )    )

  assert abs(result_sum.item() - 1_147.7_904    ) < 1E-2
  assert abs(result_mean.item() - 0.4_9_8_2    ) < 1E-3


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		int	  =      self.full_loop()

  __A	:		Tuple	  =      torch.sum(torch.abs(UpperCamelCase__    )    )
  __A	:		Any	  =      torch.mean(torch.abs(UpperCamelCase__    )    )

  assert abs(result_sum.item() - 1_7_2.0_0_6_7    ) < 1E-2
  assert abs(result_mean.item() - 0.2_2_3_9_6_7    ) < 1E-3


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      self.full_loop(prediction_type='v_prediction'    )

  __A	:		Union[str, Any]	  =      torch.sum(torch.abs(UpperCamelCase__    )    )
  __A	:		Optional[int]	  =      torch.mean(torch.abs(UpperCamelCase__    )    )

  assert abs(result_sum.item() - 5_2.5_3_0_2    ) < 1E-2
  assert abs(result_mean.item() - 0.0_6_8_4    ) < 1E-3


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		str	  =      self.full_loop(set_alpha_to_one=UpperCamelCase__   ,   beta_start=0.0_1    )
  __A	:		int	  =      torch.sum(torch.abs(UpperCamelCase__    )    )
  __A	:		Union[str, Any]	  =      torch.mean(torch.abs(UpperCamelCase__    )    )

  assert abs(result_sum.item() - 1_4_9.8_2_9_5    ) < 1E-2
  assert abs(result_mean.item() - 0.1_9_5_1    ) < 1E-3







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      self.full_loop(set_alpha_to_one=UpperCamelCase__   ,   beta_start=0.0_1    )
  __A	:		Tuple	  =      torch.sum(torch.abs(UpperCamelCase__    )    )
  __A	:		Tuple	  =      torch.mean(torch.abs(UpperCamelCase__    )    )

  assert abs(result_sum.item() - 1_4_9.0_7_8_4    ) < 1E-2
  assert abs(result_mean.item() - 0.1_9_4_1    ) < 1E-3







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase    :     Any         =       {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''MaskFormerFeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''MaskFormerImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Tuple         =       [
       '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''MaskFormerForInstanceSegmentation''',
       '''MaskFormerModel''',
       '''MaskFormerPreTrainedModel''',
   ]
   _UpperCAmelCase    :     int         =       [
       '''MaskFormerSwinBackbone''',
       '''MaskFormerSwinModel''',
       '''MaskFormerSwinPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
   from .configuration_maskformer_swin import MaskFormerSwinConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_maskformer import MaskFormerFeatureExtractor
      from .image_processing_maskformer import MaskFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskformer import (
          MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskFormerForInstanceSegmentation,
          MaskFormerModel,
          MaskFormerPreTrainedModel,
      )
      from .modeling_maskformer_swin import (
          MaskFormerSwinBackbone,
          MaskFormerSwinModel,
          MaskFormerSwinPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase    :     str         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)



