






'''simple docstring'''






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class a_    (    UpperCamelCase_							):
   _snake_case        =							(DEISMultistepScheduler,)
   _snake_case        =							(("""num_inference_steps""", 25),)







   def     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= {
          'num_train_timesteps': 1_0_0_0,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'solver_order': 2,
      }

      config.update(**__a)
      return config







   def     SCREAMING_SNAKE_CASE__					(self					,   __a=0					,   **__a)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= dict(self.forward_default_kwargs)
      __snake_case				:      Optional[int]     							= kwargs.pop('num_inference_steps'					,   __a)
      __snake_case				:      List[Any]     							= self.dummy_sample
      __snake_case				:      Optional[int]     							= 0.1 * sample
      __snake_case				:      Tuple     							= [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
         __snake_case				:      Optional[int]     							= self.get_scheduler_config(**__a)
         __snake_case				:      int     							= scheduler_class(**__a)
         scheduler.set_timesteps(__a)
         # copy over dummy past residuals
         __snake_case				:      Optional[Any]     							= dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(__a)
            __snake_case				:      Optional[int]     							= scheduler_class.from_pretrained(__a)
            new_scheduler.set_timesteps(__a)
            # copy over dummy past residuals
            __snake_case				:      Optional[Any]     							= dummy_past_residuals[: new_scheduler.config.solver_order]

         __snake_case				:      int     							= sample, sample
         for t in range(__a					,   time_step + scheduler.config.solver_order + 1):
            __snake_case				:      Tuple     							= scheduler.step(__a					,   __a					,   __a					,   **__a).prev_sample
            __snake_case				:      List[str]     							= new_scheduler.step(__a					,   __a					,   __a					,   **__a).prev_sample

            assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self					,   __a=0					,   **__a)			->      Any:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= dict(self.forward_default_kwargs)
      __snake_case				:      str     							= kwargs.pop('num_inference_steps'					,   __a)
      __snake_case				:      Any     							= self.dummy_sample
      __snake_case				:      List[Any]     							= 0.1 * sample
      __snake_case				:      Optional[Any]     							= [residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
         __snake_case				:      Tuple     							= self.get_scheduler_config()
         __snake_case				:      Any     							= scheduler_class(**__a)
         scheduler.set_timesteps(__a)

         # copy over dummy past residuals (must be after setting timesteps)
         __snake_case				:      int     							= dummy_past_residuals[: scheduler.config.solver_order]

         with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(__a)
            __snake_case				:      Optional[int]     							= scheduler_class.from_pretrained(__a)
            # copy over dummy past residuals
            new_scheduler.set_timesteps(__a)

            # copy over dummy past residual (must be after setting timesteps)
            __snake_case				:      Tuple     							= dummy_past_residuals[: new_scheduler.config.solver_order]

         __snake_case				:      Dict     							= scheduler.step(__a					,   __a					,   __a					,   **__a).prev_sample
         __snake_case				:      Tuple     							= new_scheduler.step(__a					,   __a					,   __a					,   **__a).prev_sample

         assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"







   def     SCREAMING_SNAKE_CASE__					(self					,   __a=None					,   **__a)			->      Optional[Any]:
      """simple docstring"""






      if scheduler is None:
         __snake_case				:      Dict     							= self.scheduler_classes[0]
         __snake_case				:      Tuple     							= self.get_scheduler_config(**__a)
         __snake_case				:      Optional[int]     							= scheduler_class(**__a)

      __snake_case				:      Optional[Any]     							= self.scheduler_classes[0]
      __snake_case				:      Optional[Any]     							= self.get_scheduler_config(**__a)
      __snake_case				:      int     							= scheduler_class(**__a)

      __snake_case				:      Any     							= 1_0
      __snake_case				:      Optional[Any]     							= self.dummy_model()
      __snake_case				:      str     							= self.dummy_sample_deter
      scheduler.set_timesteps(__a)

      for i, t in enumerate(scheduler.timesteps):
         __snake_case				:      Tuple     							= model(__a					,   __a)
         __snake_case				:      Optional[Any]     							= scheduler.step(__a					,   __a					,   __a).prev_sample

      return sample







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= dict(self.forward_default_kwargs)

      __snake_case				:      str     							= kwargs.pop('num_inference_steps'					,   __a)

      for scheduler_class in self.scheduler_classes:
         __snake_case				:      Any     							= self.get_scheduler_config()
         __snake_case				:      Optional[int]     							= scheduler_class(**__a)

         __snake_case				:      Dict     							= self.dummy_sample
         __snake_case				:      Dict     							= 0.1 * sample

         if num_inference_steps is not None and hasattr(__a					,   'set_timesteps'):
            scheduler.set_timesteps(__a)
         elif num_inference_steps is not None and not hasattr(__a					,   'set_timesteps'):
            __snake_case				:      str     							= num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         __snake_case				:      Any     							= [residual + 0.2, residual + 0.15, residual + 0.10]
         __snake_case				:      List[Any]     							= dummy_past_residuals[: scheduler.config.solver_order]

         __snake_case				:      Union[str, Any]     							= scheduler.timesteps[5]
         __snake_case				:      List[str]     							= scheduler.timesteps[6]

         __snake_case				:      Optional[Any]     							= scheduler.step(__a					,   __a					,   __a					,   **__a).prev_sample
         __snake_case				:      List[Any]     							= scheduler.step(__a					,   __a					,   __a					,   **__a).prev_sample

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







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= DEISMultistepScheduler(**self.get_scheduler_config())
      __snake_case				:      Dict     							= self.full_loop(scheduler=__a)
      __snake_case				:      Any     							= torch.mean(torch.abs(__a))

      assert abs(result_mean.item() - 0.23_916) < 1E-3

      __snake_case				:      Dict     							= DPMSolverSinglestepScheduler.from_config(scheduler.config)
      __snake_case				:      Union[str, Any]     							= DPMSolverMultistepScheduler.from_config(scheduler.config)
      __snake_case				:      Tuple     							= UniPCMultistepScheduler.from_config(scheduler.config)
      __snake_case				:      Dict     							= DEISMultistepScheduler.from_config(scheduler.config)

      __snake_case				:      Dict     							= self.full_loop(scheduler=__a)
      __snake_case				:      List[str]     							= torch.mean(torch.abs(__a))

      assert abs(result_mean.item() - 0.23_916) < 1E-3







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
         self.check_over_configs(num_train_timesteps=__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      self.check_over_configs(thresholding=__a)
      for order in [1, 2, 3]:
         for solver_type in ["logrho"]:
            for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                  self.check_over_configs(
                      thresholding=__a					,   prediction_type=__a					,   sample_max_value=__a					,   algorithm_type='deis'					,   solver_order=__a					,   solver_type=__a					,   )







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      for algorithm_type in ["deis"]:
         for solver_type in ["logrho"]:
            for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                  self.check_over_configs(
                      solver_order=__a					,   solver_type=__a					,   prediction_type=__a					,   algorithm_type=__a					,   )
                  __snake_case				:      Dict     							= self.full_loop(
                      solver_order=__a					,   solver_type=__a					,   prediction_type=__a					,   algorithm_type=__a					,   )
                  assert not torch.isnan(__a).any(), "Samples have nan numbers"







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      self.check_over_configs(lower_order_final=__a)
      self.check_over_configs(lower_order_final=__a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
         self.check_over_forward(num_inference_steps=__a					,   time_step=0)







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      __snake_case				:      List[Any]     							= self.full_loop()
      __snake_case				:      Optional[Any]     							= torch.mean(torch.abs(__a))

      assert abs(result_mean.item() - 0.23_916) < 1E-3







   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      List[str]     							= self.full_loop(prediction_type='v_prediction')
      __snake_case				:      Tuple     							= torch.mean(torch.abs(__a))

      assert abs(result_mean.item() - 0.091) < 1E-3







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[str]     							= self.scheduler_classes[0]
      __snake_case				:      int     							= self.get_scheduler_config(thresholding=__a					,   dynamic_thresholding_ratio=0)
      __snake_case				:      Union[str, Any]     							= scheduler_class(**__a)

      __snake_case				:      List[str]     							= 1_0
      __snake_case				:      Dict     							= self.dummy_model()
      __snake_case				:      str     							= self.dummy_sample_deter.half()
      scheduler.set_timesteps(__a)

      for i, t in enumerate(scheduler.timesteps):
         __snake_case				:      List[Any]     							= model(__a					,   __a)
         __snake_case				:      Dict     							= scheduler.step(__a					,   __a					,   __a).prev_sample

      assert sample.dtype == torch.floataa







'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class a_    (    UpperCamelCase_							):
   _snake_case        =							"""vit_msn"""







   def __init__(self					,   __a=7_6_8					,   __a=1_2					,   __a=1_2					,   __a=3_0_7_2					,   __a="gelu"					,   __a=0.0					,   __a=0.0					,   __a=0.02					,   __a=1E-06					,   __a=2_2_4					,   __a=1_6					,   __a=3					,   __a=True					,   **__a					,   )			->      Any:
      """simple docstring"""






      super().__init__(**__a)

      __snake_case				:      List[str]     							= hidden_size
      __snake_case				:      Optional[int]     							= num_hidden_layers
      __snake_case				:      Optional[Any]     							= num_attention_heads
      __snake_case				:      str     							= intermediate_size
      __snake_case				:      List[str]     							= hidden_act
      __snake_case				:      List[Any]     							= hidden_dropout_prob
      __snake_case				:      Tuple     							= attention_probs_dropout_prob
      __snake_case				:      List[str]     							= initializer_range
      __snake_case				:      Optional[int]     							= layer_norm_eps
      __snake_case				:      Dict     							= image_size
      __snake_case				:      int     							= patch_size
      __snake_case				:      Dict     							= num_channels
      __snake_case				:      Tuple     							= qkv_bias