"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase_:					List[Any]     =					{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:					str     =					[
     "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MCTCTForCTC",
     "MCTCTModel",
     "MCTCTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
 from .feature_extraction_mctct import MCTCTFeatureExtractor
 from .processing_mctct import MCTCTProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
 import sys

 lowerCAmelCase_:					Tuple     =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    a__    (   _a	):
   snake_case_							=      (IPNDMScheduler,)
   snake_case_							=      (("num_inference_steps", 50),)


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							{"num_train_timesteps": 1000}
        config.update(**_UpperCAmelCase  )
        return config


   def 	snake_case__	(     self,				_UpperCAmelCase=0,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )
        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )
        lowercase__				 =							self.dummy_sample
        lowercase__				 =							0.1 * sample
        lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
             lowercase__				 =							self.get_scheduler_config(**_UpperCAmelCase  )
             lowercase__				 =							scheduler_class(**_UpperCAmelCase  )
             scheduler.set_timesteps(_UpperCAmelCase  )
             # copy over dummy past residuals
             lowercase__				 =							dummy_past_residuals[:]

             if time_step is None:
                  lowercase__				 =							scheduler.timesteps[len(scheduler.timesteps  ) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_UpperCAmelCase  )
                  lowercase__				 =							scheduler_class.from_pretrained(_UpperCAmelCase  )
                  new_scheduler.set_timesteps(_UpperCAmelCase  )
                  # copy over dummy past residuals
                  lowercase__				 =							dummy_past_residuals[:]

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self,				_UpperCAmelCase=0,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )
        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )
        lowercase__				 =							self.dummy_sample
        lowercase__				 =							0.1 * sample
        lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
             lowercase__				 =							self.get_scheduler_config()
             lowercase__				 =							scheduler_class(**_UpperCAmelCase  )
             scheduler.set_timesteps(_UpperCAmelCase  )

             # copy over dummy past residuals (must be after setting timesteps)
             lowercase__				 =							dummy_past_residuals[:]

             if time_step is None:
                  lowercase__				 =							scheduler.timesteps[len(scheduler.timesteps  ) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_UpperCAmelCase  )
                  lowercase__				 =							scheduler_class.from_pretrained(_UpperCAmelCase  )
                  # copy over dummy past residuals
                  new_scheduler.set_timesteps(_UpperCAmelCase  )

                  # copy over dummy past residual (must be after setting timesteps)
                  lowercase__				 =							dummy_past_residuals[:]

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							new_scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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


   def 	snake_case__	(     self,				**_UpperCAmelCase  ):
        '''simple docstring'''



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

        lowercase__				 =							10
        lowercase__				 =							self.dummy_model()
        lowercase__				 =							self.dummy_sample_deter
        scheduler.set_timesteps(_UpperCAmelCase  )

        for i, t in enumerate(scheduler.timesteps  ):
             lowercase__				 =							model(_UpperCAmelCase,				_UpperCAmelCase  )
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ).prev_sample

        for i, t in enumerate(scheduler.timesteps  ):
             lowercase__				 =							model(_UpperCAmelCase,				_UpperCAmelCase  )
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ).prev_sample

        return sample


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							dict(self.forward_default_kwargs  )

        lowercase__				 =							kwargs.pop("num_inference_steps",				_UpperCAmelCase  )

        for scheduler_class in self.scheduler_classes:
             lowercase__				 =							self.get_scheduler_config()
             lowercase__				 =							scheduler_class(**_UpperCAmelCase  )

             lowercase__				 =							self.dummy_sample
             lowercase__				 =							0.1 * sample

             if num_inference_steps is not None and hasattr(_UpperCAmelCase,				"set_timesteps"  ):
                  scheduler.set_timesteps(_UpperCAmelCase  )
             elif num_inference_steps is not None and not hasattr(_UpperCAmelCase,				"set_timesteps"  ):
                  lowercase__				 =							num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             lowercase__				 =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             lowercase__				 =							dummy_past_residuals[:]

             lowercase__				 =							scheduler.timesteps[5]
             lowercase__				 =							scheduler.timesteps[6]

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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

             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample
             lowercase__				 =							scheduler.step(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ).prev_sample

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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for t, num_inference_steps in zip([1, 5, 10],				[10, 50, 100]  ):
             self.check_over_forward(num_inference_steps=_UpperCAmelCase,				time_step=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.full_loop()
        lowercase__				 =							torch.mean(torch.abs(_UpperCAmelCase  )  )

        assert abs(result_mean.item() - 254_0529  ) < 10




