




from typing import TYPE_CHECKING

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


__a          =			{
    'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'],
    'tokenization_ctrl': ['CTRLTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			[
         'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
         'CTRLForSequenceClassification',
         'CTRLLMHeadModel',
         'CTRLModel',
         'CTRLPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			[
         'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFCTRLForSequenceClassification',
         'TFCTRLLMHeadModel',
         'TFCTRLModel',
         'TFCTRLPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
     from .tokenization_ctrl import CTRLTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ctrl import (
              CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
              CTRLForSequenceClassification,
              CTRLLMHeadModel,
              CTRLModel,
              CTRLPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_ctrl import (
              TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCTRLForSequenceClassification,
              TFCTRLLMHeadModel,
              TFCTRLModel,
              TFCTRLPreTrainedModel,
          )

else:
     import sys

     __a          =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				(IPNDMScheduler,)
    lowerCAmelCase        =				(('''num_inference_steps''', 50),)





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							Tuple       =      {'''num_train_timesteps''': 1_000}
         config.update(**_SCREAMING_SNAKE_CASE  )
         return config





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=0       ,**_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      dict(self.forward_default_kwargs  )
         UpperCAmelCase_      :							int       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      self.dummy_sample
         UpperCAmelCase_      :							Optional[Any]       =      0.1 * sample
         UpperCAmelCase_      :							Optional[int]       =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							Tuple       =      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[int]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
              # copy over dummy past residuals
              UpperCAmelCase_      :							Optional[Any]       =      dummy_past_residuals[:]

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

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

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							str       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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

              UpperCAmelCase_      :							Union[str, Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Optional[int]       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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





    def 							a__				(				self  )			->		List[Any]:
         pass





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE=0       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      dict(self.forward_default_kwargs  )
         UpperCAmelCase_      :							Tuple       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      self.dummy_sample
         UpperCAmelCase_      :							Tuple       =      0.1 * sample
         UpperCAmelCase_      :							int       =      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							str       =      self.get_scheduler_config()
              UpperCAmelCase_      :							str       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

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

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

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

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

              UpperCAmelCase_      :							Tuple       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Optional[int]       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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

              UpperCAmelCase_      :							str       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							List[str]       =      new_scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         UpperCAmelCase_      :							Tuple       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							List[Any]       =      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      10
         UpperCAmelCase_      :							Tuple       =      self.dummy_model()
         UpperCAmelCase_      :							List[str]       =      self.dummy_sample_deter
         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

         for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_      :							int       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Union[str, Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ).prev_sample

         for i, t in enumerate(scheduler.timesteps  ):
              UpperCAmelCase_      :							Optional[int]       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ).prev_sample

         return sample





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							List[Any]       =      dict(self.forward_default_kwargs  )

         UpperCAmelCase_      :							Any       =      kwargs.pop('''num_inference_steps'''       ,_SCREAMING_SNAKE_CASE  )

         for scheduler_class in self.scheduler_classes:
              UpperCAmelCase_      :							int       =      self.get_scheduler_config()
              UpperCAmelCase_      :							List[Any]       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

              UpperCAmelCase_      :							Optional[int]       =      self.dummy_sample
              UpperCAmelCase_      :							List[Any]       =      0.1 * sample

              if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE       ,'''set_timesteps'''  ):
                   scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )
              elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE       ,'''set_timesteps'''  ):
                   UpperCAmelCase_      :							Union[str, Any]       =      num_inference_steps

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

              UpperCAmelCase_      :							List[Any]       =      scheduler.timesteps[5]
              UpperCAmelCase_      :							Dict       =      scheduler.timesteps[6]

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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

              UpperCAmelCase_      :							List[Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample
              UpperCAmelCase_      :							Dict       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  ).prev_sample

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





    def 							a__				(				self  )			->		Any:
         for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE       ,time_step=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         for t, num_inference_steps in zip([1, 5, 10]       ,[10, 50, 100]  ):
              self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE       ,time_step=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      self.full_loop()
         UpperCAmelCase_      :							str       =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

         assert abs(result_mean.item() - 2_540_529  ) < 10