

'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class 	lowerCAmelCase  (   snake_case_ ):
       lowerCAmelCase_												=			(CMStochasticIterativeScheduler,)
       lowerCAmelCase_												=			1_0







       def       snake_case (				self      :			Union[str, Any]    ,   **__lowercase      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		={
                "num_train_timesteps": 201,
                "sigma_min": 0.0_0_2,
                "sigma_max": 8_0.0,
            }

            config.update(**UpperCamelCase__   )
            return config







       def       snake_case (				self      :			List[str]   ):







            """simple docstring"""







            __lowercase      		=10

            __lowercase      		=self.get_scheduler_config()
            __lowercase      		=self.scheduler_classes[0](**UpperCamelCase__   )

            scheduler.set_timesteps(UpperCamelCase__   )

            __lowercase      		=scheduler.timesteps[0]
            __lowercase      		=scheduler.timesteps[1]

            __lowercase      		=self.dummy_sample
            __lowercase      		=0.1 * sample

            __lowercase      		=scheduler.step(UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__   ).prev_sample
            __lowercase      		=scheduler.step(UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__   ).prev_sample

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







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=UpperCamelCase__   )







       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=self.scheduler_classes[0]
            __lowercase      		=self.get_scheduler_config()
            __lowercase      		=scheduler_class(**UpperCamelCase__   )

            __lowercase      		=1
            scheduler.set_timesteps(UpperCamelCase__   )
            __lowercase      		=scheduler.timesteps

            __lowercase      		=torch.manual_seed(0   )

            __lowercase      		=self.dummy_model()
            __lowercase      		=self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(UpperCamelCase__   ):
                 # 1. scale model input
                 __lowercase      		=scheduler.scale_model_input(UpperCamelCase__    ,   UpperCamelCase__   )

                 # 2. predict noise residual
                 __lowercase      		=model(UpperCamelCase__    ,   UpperCamelCase__   )

                 # 3. predict previous sample x_t-1
                 __lowercase      		=scheduler.step(UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   generator=UpperCamelCase__   ).prev_sample

                 __lowercase      		=pred_prev_sample

            __lowercase      		=torch.sum(torch.abs(UpperCamelCase__   )   )
            __lowercase      		=torch.mean(torch.abs(UpperCamelCase__   )   )

            assert abs(result_sum.item() - 1_9_2.7_6_1_4   ) < 1E-2
            assert abs(result_mean.item() - 0.2_5_1_0   ) < 1E-3







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=self.scheduler_classes[0]
            __lowercase      		=self.get_scheduler_config()
            __lowercase      		=scheduler_class(**UpperCamelCase__   )

            __lowercase      		=[106, 0]
            scheduler.set_timesteps(timesteps=UpperCamelCase__   )
            __lowercase      		=scheduler.timesteps

            __lowercase      		=torch.manual_seed(0   )

            __lowercase      		=self.dummy_model()
            __lowercase      		=self.dummy_sample_deter * scheduler.init_noise_sigma

            for t in timesteps:
                 # 1. scale model input
                 __lowercase      		=scheduler.scale_model_input(UpperCamelCase__    ,   UpperCamelCase__   )

                 # 2. predict noise residual
                 __lowercase      		=model(UpperCamelCase__    ,   UpperCamelCase__   )

                 # 3. predict previous sample x_t-1
                 __lowercase      		=scheduler.step(UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__    ,   generator=UpperCamelCase__   ).prev_sample

                 __lowercase      		=pred_prev_sample

            __lowercase      		=torch.sum(torch.abs(UpperCamelCase__   )   )
            __lowercase      		=torch.mean(torch.abs(UpperCamelCase__   )   )

            assert abs(result_sum.item() - 3_4_7.6_3_5_7   ) < 1E-2
            assert abs(result_mean.item() - 0.4_5_2_7   ) < 1E-3







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=self.scheduler_classes[0]
            __lowercase      		=self.get_scheduler_config()
            __lowercase      		=scheduler_class(**UpperCamelCase__   )

            __lowercase      		=[39, 30, 12, 15, 0]

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







       def       snake_case (				self      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=self.scheduler_classes[0]
            __lowercase      		=self.get_scheduler_config()
            __lowercase      		=scheduler_class(**UpperCamelCase__   )

            __lowercase      		=[39, 30, 12, 1, 0]
            __lowercase      		=len(UpperCamelCase__   )

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







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=self.scheduler_classes[0]
            __lowercase      		=self.get_scheduler_config()
            __lowercase      		=scheduler_class(**UpperCamelCase__   )

            __lowercase      		=[scheduler.config.num_train_timesteps]

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






"""simple docstring"""



from typing import TYPE_CHECKING

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


__snake_case       =       {
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       ["""PoolFormerFeatureExtractor"""]
  __snake_case       =       ["""PoolFormerImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """PoolFormerForImageClassification""",
      """PoolFormerModel""",
      """PoolFormerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_poolformer import (
      POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      PoolFormerConfig,
      PoolFormerOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_poolformer import PoolFormerFeatureExtractor
    from .image_processing_poolformer import PoolFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_poolformer import (
        POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        PoolFormerForImageClassification,
        PoolFormerModel,
        PoolFormerPreTrainedModel,
    )


else:
  import sys

  __snake_case       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure)





