
import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class   __lowerCAmelCase		(		unittest.TestCase ):



      """simple docstring"""







      A__		:     Tuple       				=      (ScoreSdeVeScheduler,)
      A__		:     Optional[int]       				=      ()




      @property
      def 						snake_case_     ( self						:    Optional[int]			):
          __lowercase						:     str		       =    4
          __lowercase						:     List[str]		       =    3
          __lowercase						:     Union[str, Any]		       =    8
          __lowercase						:     Any		       =    8

          __lowercase						:     str		       =    torch.rand((batch_size, num_channels, height, width)			)

          return sample




      @property
      def 						snake_case_     ( self						:    int			):
          __lowercase						:     Optional[Any]		       =    4
          __lowercase						:     Dict		       =    3
          __lowercase						:     Any		       =    8
          __lowercase						:     Any		       =    8

          __lowercase						:     Optional[int]		       =    batch_size * num_channels * height * width
          __lowercase						:     Union[str, Any]		       =    torch.arange(_snake_case			)
          __lowercase						:     str		       =    sample.reshape(_snake_case			,    _snake_case			,    _snake_case			,    _snake_case			)
          __lowercase						:     Any		       =    sample / num_elems
          __lowercase						:     List[str]		       =    sample.permute(3			,    0			,    1			,    2			)

          return sample




      def 						snake_case_     ( self						:    str			):







          def model(_snake_case						:    Any			,    _snake_case						:    Optional[int]			,    *_snake_case						:    Optional[Any]			):
              return sample * t / (t + 1)

          return model




      def 						snake_case_     ( self						:    List[Any]			,    **_snake_case						:    Tuple			):
          __lowercase						:     List[str]		       =    {
              "num_train_timesteps": 2000,
              "snr": 0.15,
              "sigma_min": 0.01,
              "sigma_max": 1348,
              "sampling_eps": 1E-5,
          }

          config.update(**_snake_case			)
          return config




      def 						snake_case_     ( self						:    List[str]			,    _snake_case						:    Any=0			,    **_snake_case						:    Optional[int]			):
          __lowercase						:     Any		       =    dict(self.forward_default_kwargs			)

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

              __lowercase						:     Optional[Any]		       =    self.get_scheduler_config(**_snake_case			)
              __lowercase						:     Union[str, Any]		       =    scheduler_class(**_snake_case			)

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_snake_case			)
                  __lowercase						:     int		       =    scheduler_class.from_pretrained(_snake_case			)

              __lowercase						:     Union[str, Any]		       =    scheduler.step_pred(
                  _snake_case			,    _snake_case			,    _snake_case			,    generator=torch.manual_seed(0			)			,    **_snake_case			).prev_sample
              __lowercase						:     Any		       =    new_scheduler.step_pred(
                  _snake_case			,    _snake_case			,    _snake_case			,    generator=torch.manual_seed(0			)			,    **_snake_case			).prev_sample

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

              __lowercase						:     Dict		       =    scheduler.step_correct(_snake_case			,    _snake_case			,    generator=torch.manual_seed(0			)			,    **_snake_case			).prev_sample
              __lowercase						:     List[Any]		       =    new_scheduler.step_correct(
                  _snake_case			,    _snake_case			,    generator=torch.manual_seed(0			)			,    **_snake_case			).prev_sample

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




      def 						snake_case_     ( self						:    Optional[int]			,    _snake_case						:    int=0			,    **_snake_case						:    Optional[Any]			):
          __lowercase						:     Any		       =    dict(self.forward_default_kwargs			)
          kwargs.update(_snake_case			)

          for scheduler_class in self.scheduler_classes:
              __lowercase						:     Tuple		       =    self.dummy_sample
              __lowercase						:     Dict		       =    0.1 * sample

              __lowercase						:     Union[str, Any]		       =    self.get_scheduler_config()
              __lowercase						:     Optional[int]		       =    scheduler_class(**_snake_case			)

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_snake_case			)
                  __lowercase						:     Any		       =    scheduler_class.from_pretrained(_snake_case			)

              __lowercase						:     Tuple		       =    scheduler.step_pred(
                  _snake_case			,    _snake_case			,    _snake_case			,    generator=torch.manual_seed(0			)			,    **_snake_case			).prev_sample
              __lowercase						:     Optional[Any]		       =    new_scheduler.step_pred(
                  _snake_case			,    _snake_case			,    _snake_case			,    generator=torch.manual_seed(0			)			,    **_snake_case			).prev_sample

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

              __lowercase						:     Any		       =    scheduler.step_correct(_snake_case			,    _snake_case			,    generator=torch.manual_seed(0			)			,    **_snake_case			).prev_sample
              __lowercase						:     int		       =    new_scheduler.step_correct(
                  _snake_case			,    _snake_case			,    generator=torch.manual_seed(0			)			,    **_snake_case			).prev_sample

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




      def 						snake_case_     ( self						:    Dict			):







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




      def 						snake_case_     ( self						:    Optional[int]			):







          for sigma_min, sigma_max in zip([0.00_01, 0.0_01, 0.01]			,    [1, 100, 1000]			):
              self.check_over_configs(sigma_min=_snake_case			,    sigma_max=_snake_case			)




      def 						snake_case_     ( self						:    str			):







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




      def 						snake_case_     ( self						:    str			):
          __lowercase						:     Tuple		       =    dict(self.forward_default_kwargs			)

          __lowercase						:     str		       =    self.scheduler_classes[0]
          __lowercase						:     Any		       =    self.get_scheduler_config()
          __lowercase						:     Optional[int]		       =    scheduler_class(**_snake_case			)

          __lowercase						:     Optional[Any]		       =    3

          __lowercase						:     Optional[int]		       =    self.dummy_model()
          __lowercase						:     Tuple		       =    self.dummy_sample_deter

          scheduler.set_sigmas(_snake_case			)
          scheduler.set_timesteps(_snake_case			)
          __lowercase						:     List[str]		       =    torch.manual_seed(0			)

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

              for _ in range(scheduler.config.correct_steps			):
                  with torch.no_grad():
                      __lowercase						:     Optional[int]		       =    model(_snake_case			,    _snake_case			)
                  __lowercase						:     List[Any]		       =    scheduler.step_correct(_snake_case			,    _snake_case			,    generator=_snake_case			,    **_snake_case			).prev_sample

              with torch.no_grad():
                  __lowercase						:     Any		       =    model(_snake_case			,    _snake_case			)

              __lowercase						:     List[Any]		       =    scheduler.step_pred(_snake_case			,    _snake_case			,    _snake_case			,    generator=_snake_case			,    **_snake_case			)
              __lowercase						:     Tuple		       =    output.prev_sample, output.prev_sample_mean

          __lowercase						:     Optional[int]		       =    torch.sum(torch.abs(_snake_case			)			)
          __lowercase						:     List[str]		       =    torch.mean(torch.abs(_snake_case			)			)

          assert np.isclose(result_sum.item()			,    143_7275_8528.0			)
          assert np.isclose(result_mean.item()			,    1871_4530.0			)




      def 						snake_case_     ( self						:    Any			):
          __lowercase						:     List[str]		       =    dict(self.forward_default_kwargs			)

          __lowercase						:     str		       =    kwargs.pop('''num_inference_steps'''			,    _snake_case			)

          for scheduler_class in self.scheduler_classes:
              __lowercase						:     Dict		       =    self.get_scheduler_config()
              __lowercase						:     List[Any]		       =    scheduler_class(**_snake_case			)

              __lowercase						:     Optional[Any]		       =    self.dummy_sample
              __lowercase						:     str		       =    0.1 * sample

              if num_inference_steps is not None and hasattr(_snake_case			,    '''set_timesteps'''			):
                  scheduler.set_timesteps(_snake_case			)
              elif num_inference_steps is not None and not hasattr(_snake_case			,    '''set_timesteps'''			):
                  __lowercase						:     List[str]		       =    num_inference_steps

              __lowercase						:     str		       =    scheduler.step_pred(_snake_case			,    0			,    _snake_case			,    generator=torch.manual_seed(0			)			,    **_snake_case			).prev_sample
              __lowercase						:     str		       =    scheduler.step_pred(_snake_case			,    1			,    _snake_case			,    generator=torch.manual_seed(0			)			,    **_snake_case			).prev_sample

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




from typing import TYPE_CHECKING

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


__lowerCAmelCase   :     Optional[int]      =						{"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase   :     List[Any]      =						[
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase   :     Tuple      =						["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

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


