





import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class    _a  (  unittest.TestCase):

      """simple docstring"""


      UpperCamelCase__       						=   (ScoreSdeVeScheduler,)
      UpperCamelCase__       						=   ()






      @property
      def 	UpperCAmelCase_   (						self:	Dict			):
          '''simple docstring'''


          UpperCamelCase__:					Any												=      4
          UpperCamelCase__:					Optional[int]												=      3
          UpperCamelCase__:					Optional[Any]												=      8
          UpperCamelCase__:					Any												=      8

          UpperCamelCase__:					List[Any]												=      torch.rand((batch_size, num_channels, height, width)			)

          return sample






      @property
      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      4
          UpperCamelCase__:					Dict												=      3
          UpperCamelCase__:					str												=      8
          UpperCamelCase__:					Tuple												=      8

          UpperCamelCase__:					Dict												=      batch_size * num_channels * height * width
          UpperCamelCase__:					Tuple												=      torch.arange(__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      sample.reshape(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)
          UpperCamelCase__:					Tuple												=      sample / num_elems
          UpperCamelCase__:					int												=      sample.permute(3					,  0					,  1					,  2			)

          return sample






      def 	UpperCAmelCase_   (						self:	Dict			):
          '''simple docstring'''


          def model(__lowerCamelCase:	str					,  __lowerCamelCase:	int					,  *__lowerCamelCase:	Dict			):
              return sample * t / (t + 1)

          return model






      def 	UpperCAmelCase_   (						self:	Union[str, Any]					,  **__lowerCamelCase:	str			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      {
              "num_train_timesteps": 2000,
              "snr": 0.15,
              "sigma_min": 0.01,
              "sigma_max": 1348,
              "sampling_eps": 1e-5,
          }

          config.update(**__lowerCamelCase			)
          return config






      def 	UpperCAmelCase_   (						self:	str					,  __lowerCamelCase:	Optional[int]=0					,  **__lowerCamelCase:	str			):
          '''simple docstring'''


          UpperCamelCase__:					Dict												=      dict(self.forward_default_kwargs			)

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase__:					List[Any]												=      self.dummy_sample
              UpperCamelCase__:					Tuple												=      0.1 * sample

              UpperCamelCase__:					List[Any]												=      self.get_scheduler_config(**__lowerCamelCase			)
              UpperCamelCase__:					Any												=      scheduler_class(**__lowerCamelCase			)

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(__lowerCamelCase			)
                  UpperCamelCase__:					int												=      scheduler_class.from_pretrained(__lowerCamelCase			)

              UpperCamelCase__:					List[str]												=      scheduler.step_pred(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample
              UpperCamelCase__:					int												=      new_scheduler.step_pred(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample

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

              UpperCamelCase__:					List[str]												=      scheduler.step_correct(__lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample
              UpperCamelCase__:					List[str]												=      new_scheduler.step_correct(
                  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample

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






      def 	UpperCAmelCase_   (						self:	Any					,  __lowerCamelCase:	Optional[int]=0					,  **__lowerCamelCase:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					List[Any]												=      dict(self.forward_default_kwargs			)
          kwargs.update(__lowerCamelCase			)

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase__:					Any												=      self.dummy_sample
              UpperCamelCase__:					Tuple												=      0.1 * sample

              UpperCamelCase__:					Dict												=      self.get_scheduler_config()
              UpperCamelCase__:					Any												=      scheduler_class(**__lowerCamelCase			)

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(__lowerCamelCase			)
                  UpperCamelCase__:					Optional[Any]												=      scheduler_class.from_pretrained(__lowerCamelCase			)

              UpperCamelCase__:					List[str]												=      scheduler.step_pred(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample
              UpperCamelCase__:					int												=      new_scheduler.step_pred(
                  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample

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

              UpperCamelCase__:					Dict												=      scheduler.step_correct(__lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample
              UpperCamelCase__:					Any												=      new_scheduler.step_correct(
                  __lowerCamelCase					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample

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






      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


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






      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]					,  [1, 100, 1000]			):
              self.check_over_configs(sigma_min=__lowerCamelCase					,  sigma_max=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          for t in [0.1, 0.5, 0.75]:
              self.check_over_forward(time_step=__lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      dict(self.forward_default_kwargs			)

          UpperCamelCase__:					List[str]												=      self.scheduler_classes[0]
          UpperCamelCase__:					List[Any]												=      self.get_scheduler_config()
          UpperCamelCase__:					Union[str, Any]												=      scheduler_class(**__lowerCamelCase			)

          UpperCamelCase__:					int												=      3

          UpperCamelCase__:					Tuple												=      self.dummy_model()
          UpperCamelCase__:					List[Any]												=      self.dummy_sample_deter

          scheduler.set_sigmas(__lowerCamelCase			)
          scheduler.set_timesteps(__lowerCamelCase			)
          UpperCamelCase__:					Optional[Any]												=      torch.manual_seed(0			)

          for i, t in enumerate(scheduler.timesteps			):
              UpperCamelCase__:					Dict												=      scheduler.sigmas[i]

              for _ in range(scheduler.config.correct_steps			):
                  with torch.no_grad():
                      UpperCamelCase__:					int												=      model(__lowerCamelCase					,  __lowerCamelCase			)
                  UpperCamelCase__:					Union[str, Any]												=      scheduler.step_correct(__lowerCamelCase					,  __lowerCamelCase					,  generator=__lowerCamelCase					,  **__lowerCamelCase			).prev_sample

              with torch.no_grad():
                  UpperCamelCase__:					str												=      model(__lowerCamelCase					,  __lowerCamelCase			)

              UpperCamelCase__:					Tuple												=      scheduler.step_pred(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  generator=__lowerCamelCase					,  **__lowerCamelCase			)
              UpperCamelCase__							,				UpperCamelCase__:					Union[str, Any]												=      output.prev_sample, output.prev_sample_mean

          UpperCamelCase__:					List[str]												=      torch.sum(torch.abs(__lowerCamelCase			)			)
          UpperCamelCase__:					Any												=      torch.mean(torch.abs(__lowerCamelCase			)			)

          assert np.isclose(result_sum.item()					,  14_372_758_528.0			)
          assert np.isclose(result_mean.item()					,  18_714_530.0			)






      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					Dict												=      dict(self.forward_default_kwargs			)

          UpperCamelCase__:					List[Any]												=      kwargs.pop("num_inference_steps"					,  __lowerCamelCase			)

          for scheduler_class in self.scheduler_classes:
              UpperCamelCase__:					Any												=      self.get_scheduler_config()
              UpperCamelCase__:					List[Any]												=      scheduler_class(**__lowerCamelCase			)

              UpperCamelCase__:					str												=      self.dummy_sample
              UpperCamelCase__:					Optional[int]												=      0.1 * sample

              if num_inference_steps is not None and hasattr(__lowerCamelCase					,  "set_timesteps"			):
                  scheduler.set_timesteps(__lowerCamelCase			)
              elif num_inference_steps is not None and not hasattr(__lowerCamelCase					,  "set_timesteps"			):
                  UpperCamelCase__:					Union[str, Any]												=      num_inference_steps

              UpperCamelCase__:					Optional[Any]												=      scheduler.step_pred(__lowerCamelCase					,  0					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample
              UpperCamelCase__:					Union[str, Any]												=      scheduler.step_pred(__lowerCamelCase					,  1					,  __lowerCamelCase					,  generator=torch.manual_seed(0			)					,  **__lowerCamelCase			).prev_sample

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








from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims

