


"""simple docstring"""






import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union


class      __lowerCamelCase	(       enum.Enum					):

     '''simple docstring'''
     A_							:						List[Any]     =		'timm'
     A_							:						str     =		'transformers'







def    A_ ( _lowerCAmelCase  :       Optional[Iterable[str]],  _lowerCAmelCase  :       Optional[Iterable[int]],  _lowerCAmelCase  :       Optional[Iterable[str]]							):






     """simple docstring"""

     if stage_names is None:
          raise ValueError('''Stage_names must be set for transformers backbones'''							)

     if out_features is not None:
          if not isinstance(_lowerCAmelCase,  (list,)							):
               raise ValueError(f'out_features must be a list {type(_lowerCAmelCase							)}'							)
          if any(feat not in stage_names for feat in out_features							):
               raise ValueError(f'out_features must be a subset of stage_names: {stage_names} got {out_features}'							)

     if out_indices is not None:
          if not isinstance(_lowerCAmelCase,  (list, tuple)							):
               raise ValueError(f'out_indices must be a list or tuple, got {type(_lowerCAmelCase							)}'							)
          if any(idx >= len(_lowerCAmelCase							) for idx in out_indices							):
               raise ValueError('''out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'''							)

     if out_features is not None and out_indices is not None:
          if len(_lowerCAmelCase							) != len(_lowerCAmelCase							):
               raise ValueError('''out_features and out_indices should have the same length if both are set'''							)
          if out_features != [stage_names[idx] for idx in out_indices]:
               raise ValueError('''out_features and out_indices should correspond to the same stages if both are set'''							)
def    A_ ( _lowerCAmelCase  :       Optional[List[str]],  _lowerCAmelCase  :       Optional[Union[List[int], Tuple[int]]],  _lowerCAmelCase  :       List[str],  ):






     """simple docstring"""

     if out_indices is None and out_features is None:
          _a     						=					[len(_lowerCAmelCase							) - 1]
          _a     						=					[stage_names[-1]]
     elif out_indices is None and out_features is not None:
          _a     						=					[stage_names.index(_lowerCAmelCase							) for layer in out_features]
     elif out_features is None and out_indices is not None:
          _a     						=					[stage_names[idx] for idx in out_indices]
     return out_features, out_indices
def    A_ ( _lowerCAmelCase  :       Optional[List[str]],  _lowerCAmelCase  :       Optional[Union[List[int], Tuple[int]]],  _lowerCAmelCase  :       List[str],  ):






     """simple docstring"""

     verify_out_features_out_indices(out_features=_lowerCAmelCase,  out_indices=_lowerCAmelCase,  stage_names=_lowerCAmelCase							)
     _a						,   _a     						=					_align_output_features_output_indices(
         out_features=_lowerCAmelCase,  out_indices=_lowerCAmelCase,  stage_names=_lowerCAmelCase							)
     # Verify that the aligned out_features and out_indices are valid
     verify_out_features_out_indices(out_features=_lowerCAmelCase,  out_indices=_lowerCAmelCase,  stage_names=_lowerCAmelCase							)
     return output_features, output_indices


class      __lowerCamelCase	:

     '''simple docstring'''
     A_							:						Optional[BackboneType]     =		None


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				None:
          if getattr(self     ,					'''_backbone'''     ,					__UpperCAmelCase				) is None:
               raise ValueError('''self._backbone must be set before calling _init_timm_backbone'''				)

          # These will diagree with the defaults for the transformers models e.g. for resnet50
          # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
          # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
          _a     						=					[stage['''module'''] for stage in self._backbone.feature_info.info]
          _a     						=					[stage['''num_chs'''] for stage in self._backbone.feature_info.info]
          _a     						=					self._backbone.feature_info.out_indices
          _a     						=					self._backbone.feature_info.module_name()

          # We verify the out indices and out features are valid
          verify_out_features_out_indices(
              out_features=__UpperCAmelCase     ,					out_indices=__UpperCAmelCase     ,					stage_names=self.stage_names				)
          _a						,   _a     						=					out_features, out_indices


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				None:
          _a     						=					getattr(__UpperCAmelCase     ,					'''stage_names'''				)
          _a     						=					getattr(__UpperCAmelCase     ,					'''out_features'''     ,					__UpperCAmelCase				)
          _a     						=					getattr(__UpperCAmelCase     ,					'''out_indices'''     ,					__UpperCAmelCase				)

          _a     						=					stage_names
          _a						,   _a     						=					get_aligned_output_features_output_indices(
              out_features=__UpperCAmelCase     ,					out_indices=__UpperCAmelCase     ,					stage_names=__UpperCAmelCase				)
          # Number of channels for each stage. This is set in the transformer backbone model init
          _a     						=					None


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				None:
          _a     						=					config

          _a     						=					getattr(__UpperCAmelCase     ,					'''use_timm_backbone'''     ,					__UpperCAmelCase				)
          _a     						=					BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

          if self.backbone_type == BackboneType.TIMM:
               self._init_timm_backbone(__UpperCAmelCase				)
          elif self.backbone_type == BackboneType.TRANSFORMERS:
               self._init_transformers_backbone(__UpperCAmelCase				)
          else:
               raise ValueError(F'backbone_type {self.backbone_type} not supported.'				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				str:
          return self._out_features


     @out_features.setter
     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				List[Any]:
          _a						,   _a     						=					get_aligned_output_features_output_indices(
              out_features=__UpperCAmelCase     ,					out_indices=__UpperCAmelCase     ,					stage_names=self.stage_names				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          return self._out_indices


     @out_indices.setter
     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				int:
          _a						,   _a     						=					get_aligned_output_features_output_indices(
              out_features=__UpperCAmelCase     ,					out_indices=__UpperCAmelCase     ,					stage_names=self.stage_names				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          # the current backbones will output the number of channels for each stage
          # even if that stage is not in the out_features list.
          return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names				)}


     @property
     def 				_UpperCAmelCase   (   self				)							->				List[Any]:
          return [self.out_feature_channels[name] for name in self.out_features]


     def 				_UpperCAmelCase   (   self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)							->				Optional[Any]:
          _a     						=					dict(inspect.signature(self.forward				).parameters				)
          _a     						=					{k: v for k, v in kwargs.items() if k in signature}
          return self(*__UpperCAmelCase     ,					**__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = None     ,					)							->				List[str]:
          raise NotImplementedError('''This method should be implemented by the derived class.'''				)





     def 				_UpperCAmelCase   (   self				)							->				List[Any]:
          _a     						=					super().to_dict()
          _a     						=					output.pop('''_out_features'''				)
          _a     						=					output.pop('''_out_indices'''				)
          return output







class      __lowerCamelCase	:

     '''simple docstring'''


     @property
     def 				_UpperCAmelCase   (   self				)							->				int:
          return self._out_features


     @out_features.setter
     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				List[Any]:
          _a						,   _a     						=					get_aligned_output_features_output_indices(
              out_features=__UpperCAmelCase     ,					out_indices=__UpperCAmelCase     ,					stage_names=self.stage_names				)


     @property
     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          return self._out_indices


     @out_indices.setter
     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				List[Any]:
          _a						,   _a     						=					get_aligned_output_features_output_indices(
              out_features=__UpperCAmelCase     ,					out_indices=__UpperCAmelCase     ,					stage_names=self.stage_names				)





     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					super().to_dict()
          _a     						=					output.pop('''_out_features'''				)
          _a     						=					output.pop('''_out_indices'''				)
          return output



"""simple docstring"""






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class      __lowerCamelCase	(       a__					):

     '''simple docstring'''
     A_							:						Tuple     =		(DDIMScheduler,)
     A_							:						Tuple     =		(('eta', 0.0), ('num_inference_steps', 50))


     def 				_UpperCAmelCase   (   self     ,					**__UpperCAmelCase				)							->				str:
          _a     						=					{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.0001,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''clip_sample''': True,
          }

          config.update(**__UpperCAmelCase				)
          return config


     def 				_UpperCAmelCase   (   self     ,					**__UpperCAmelCase				)							->				List[Any]:
          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config(**__UpperCAmelCase				)
          _a     						=					scheduler_class(**__UpperCAmelCase				)

          _a						,   _a     						=					10, 0.0

          _a     						=					self.dummy_model()
          _a     						=					self.dummy_sample_deter

          scheduler.set_timesteps(__UpperCAmelCase				)

          for t in scheduler.timesteps:
               _a     						=					model(__UpperCAmelCase     ,					__UpperCAmelCase				)
               _a     						=					scheduler.step(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				).prev_sample

          return sample


     def 				_UpperCAmelCase   (   self				)							->				str:
          for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Dict:
          for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__UpperCAmelCase				)

          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config(steps_offset=1				)
          _a     						=					scheduler_class(**__UpperCAmelCase				)
          scheduler.set_timesteps(5				)
          assert torch.equal(scheduler.timesteps     ,					torch.LongTensor([801, 601, 401, 201, 1]				)				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]     ,					[0.002, 0.02, 0.2, 2]				):
               self.check_over_configs(beta_start=__UpperCAmelCase     ,					beta_end=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          self.check_over_configs(thresholding=__UpperCAmelCase				)
          for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__UpperCAmelCase     ,					prediction_type=__UpperCAmelCase     ,					sample_max_value=__UpperCAmelCase     ,					)


     def 				_UpperCAmelCase   (   self				)							->				int:
          for t in [1, 10, 49]:
               self.check_over_forward(time_step=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          for t, num_inference_steps in zip([1, 10, 50]     ,					[10, 50, 500]				):
               self.check_over_forward(time_step=__UpperCAmelCase     ,					num_inference_steps=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Optional[Any]:
          for t, eta in zip([1, 10, 49]     ,					[0.0, 0.5, 1.0]				):
               self.check_over_forward(time_step=__UpperCAmelCase     ,					eta=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          _a     						=					self.scheduler_classes[0]
          _a     						=					self.get_scheduler_config()
          _a     						=					scheduler_class(**__UpperCAmelCase				)

          assert torch.sum(torch.abs(scheduler._get_variance(0     ,					0				) - 0.0				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420     ,					400				) - 0.14771				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980     ,					960				) - 0.32460				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0     ,					0				) - 0.0				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487     ,					486				) - 0.00979				)				) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999     ,					998				) - 0.02				)				) < 1e-5


     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          _a     						=					self.full_loop()

          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 172.0067				) < 1e-2
          assert abs(result_mean.item() - 0.223967				) < 1e-3


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					self.full_loop(prediction_type='''v_prediction'''				)

          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 52.5302				) < 1e-2
          assert abs(result_mean.item() - 0.0684				) < 1e-3


     def 				_UpperCAmelCase   (   self				)							->				List[str]:
          # We specify different beta, so that the first alpha is 0.99
          _a     						=					self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01				)
          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 149.8295				) < 1e-2
          assert abs(result_mean.item() - 0.1951				) < 1e-3





     def 				_UpperCAmelCase   (   self				)							->				int:
          # We specify different beta, so that the first alpha is 0.99
          _a     						=					self.full_loop(set_alpha_to_one=__UpperCAmelCase     ,					beta_start=0.01				)
          _a     						=					torch.sum(torch.abs(__UpperCAmelCase				)				)
          _a     						=					torch.mean(torch.abs(__UpperCAmelCase				)				)

          assert abs(result_sum.item() - 149.0784				) < 1e-2
          assert abs(result_mean.item() - 0.1941				) < 1e-3