

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Tuple      		=       (IPNDMScheduler,)
      _lowerCamelCase       :			List[str]      		=       (('num_inference_steps', 5_0),)





      def 		__A	(   self							:				Optional[Any]						,			**UpperCAmelCase							:				str	):
            A_						  =				{"num_train_timesteps": 1000}
            config.update(**UpperCAmelCase	)
            return config





      def 		__A	(   self							:				List[str]						,			UpperCAmelCase							:				Dict=0						,			**UpperCAmelCase							:				List[Any]	):
            A_						  =				dict(self.forward_default_kwargs	)
            A_						  =				kwargs.pop("num_inference_steps"						,			UpperCAmelCase	)
            A_						  =				self.dummy_sample
            A_						  =				0.1 * sample
            A_						  =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

                  A_						  =				scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).prev_sample
                  A_						  =				new_scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).prev_sample

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

                  A_						  =				scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).prev_sample
                  A_						  =				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 		__A	(   self							:				Tuple	):
            pass





      def 		__A	(   self							:				Optional[int]						,			UpperCAmelCase							:				List[Any]=0						,			**UpperCAmelCase							:				str	):
            A_						  =				dict(self.forward_default_kwargs	)
            A_						  =				kwargs.pop("num_inference_steps"						,			UpperCAmelCase	)
            A_						  =				self.dummy_sample
            A_						  =				0.1 * sample
            A_						  =				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

            for scheduler_class in self.scheduler_classes:
                  A_						  =				self.get_scheduler_config()
                  A_						  =				scheduler_class(**UpperCAmelCase	)
                  scheduler.set_timesteps(UpperCAmelCase	)

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

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(UpperCAmelCase	)
                        A_						  =				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)
                        A_						  =				dummy_past_residuals[:]

                  A_						  =				scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).prev_sample
                  A_						  =				new_scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).prev_sample

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

                  A_						  =				scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).prev_sample
                  A_						  =				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 		__A	(   self							:				Optional[int]						,			**UpperCAmelCase							:				List[str]	):
            A_						  =				self.scheduler_classes[0]
            A_						  =				self.get_scheduler_config(**UpperCAmelCase	)
            A_						  =				scheduler_class(**UpperCAmelCase	)

            A_						  =				10
            A_						  =				self.dummy_model()
            A_						  =				self.dummy_sample_deter
            scheduler.set_timesteps(UpperCAmelCase	)

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

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

            return sample





      def 		__A	(   self							:				int	):
            A_						  =				dict(self.forward_default_kwargs	)

            A_						  =				kwargs.pop("num_inference_steps"						,			UpperCAmelCase	)

            for scheduler_class in self.scheduler_classes:
                  A_						  =				self.get_scheduler_config()
                  A_						  =				scheduler_class(**UpperCAmelCase	)

                  A_						  =				self.dummy_sample
                  A_						  =				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"	):
                        A_						  =				num_inference_steps

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

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

                  A_						  =				scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).prev_sample
                  A_						  =				scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).prev_sample

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

                  A_						  =				scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).prev_sample
                  A_						  =				scheduler.step(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase						,			**UpperCAmelCase	).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, 1000]:
                  self.check_over_configs(num_train_timesteps=UpperCAmelCase						,			time_step=UpperCAmelCase	)





      def 		__A	(   self							:				Tuple	):
            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 		__A	(   self							:				Optional[Any]	):
            A_						  =				self.full_loop()
            A_						  =				torch.mean(torch.abs(UpperCAmelCase	)	)

            assert abs(result_mean.item() - 2540529	) < 10



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case       =       {
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case       =       [
      """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TimeSeriesTransformerForPrediction""",
      """TimeSeriesTransformerModel""",
      """TimeSeriesTransformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

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





