






import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 	lowercase_      (			lowercase     ):






      '''simple docstring'''




      __snake_case  						=						(LMSDiscreteScheduler,)
      __snake_case  						=						10







      def    __lowerCAmelCase      (   self   :		Any				,      **__UpperCAmelCase   :		Optional[Any]						)					->Dict:




       """simple docstring"""


       a							=   {
           '''num_train_timesteps''': 1_100,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**__UpperCAmelCase						)
       return config







      def    __lowerCAmelCase      (   self   :		Optional[int]						)					->Any:




       """simple docstring"""


       for timesteps in [10, 50, 100, 1_000]:
        self.check_over_configs(num_train_timesteps=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		int						)					->List[Any]:




       """simple docstring"""


       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,      [0.0002, 0.002, 0.02]						):
        self.check_over_configs(beta_start=__UpperCAmelCase				,      beta_end=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		Any						)					->Union[str, Any]:




       """simple docstring"""


       for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		Tuple						)					->List[Any]:




       """simple docstring"""


       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		Tuple						)					->Any:




       """simple docstring"""


       for t in [0, 500, 800]:
        self.check_over_forward(time_step=__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[Any]						)					->List[str]:




       """simple docstring"""


       a							=   self.scheduler_classes[0]
       a							=   self.get_scheduler_config()
       a							=   scheduler_class(**__UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       a							=   self.dummy_model()
       a							=   self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(scheduler.timesteps						):
        a							=   scheduler.scale_model_input(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   model(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   scheduler.step(__UpperCAmelCase				,      __UpperCAmelCase				,      __UpperCAmelCase						)
        a							=   output.prev_sample

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

       assert abs(result_sum.item() - 1006.388						) < 1e-2
       assert abs(result_mean.item() - 1.31						) < 1e-3







      def    __lowerCAmelCase      (   self   :		Optional[Any]						)					->Any:




       """simple docstring"""


       a							=   self.scheduler_classes[0]
       a							=   self.get_scheduler_config(prediction_type='''v_prediction'''						)
       a							=   scheduler_class(**__UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps						)

       a							=   self.dummy_model()
       a							=   self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(scheduler.timesteps						):
        a							=   scheduler.scale_model_input(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   model(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   scheduler.step(__UpperCAmelCase				,      __UpperCAmelCase				,      __UpperCAmelCase						)
        a							=   output.prev_sample

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

       assert abs(result_sum.item() - 0.0017						) < 1e-2
       assert abs(result_mean.item() - 2.2_6_7_6e-0_6						) < 1e-3







      def    __lowerCAmelCase      (   self   :		Optional[Any]						)					->str:




       """simple docstring"""


       a							=   self.scheduler_classes[0]
       a							=   self.get_scheduler_config()
       a							=   scheduler_class(**__UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps				,      device=__UpperCAmelCase						)

       a							=   self.dummy_model()
       a							=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       a							=   sample.to(__UpperCAmelCase						)

       for i, t in enumerate(scheduler.timesteps						):
        a							=   scheduler.scale_model_input(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   model(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   scheduler.step(__UpperCAmelCase				,      __UpperCAmelCase				,      __UpperCAmelCase						)
        a							=   output.prev_sample

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

       assert abs(result_sum.item() - 1006.388						) < 1e-2
       assert abs(result_mean.item() - 1.31						) < 1e-3







      def    __lowerCAmelCase      (   self   :		Dict						)					->Tuple:




       """simple docstring"""


       a							=   self.scheduler_classes[0]
       a							=   self.get_scheduler_config()
       a							=   scheduler_class(**__UpperCAmelCase				,      use_karras_sigmas=__UpperCAmelCase						)

       scheduler.set_timesteps(self.num_inference_steps				,      device=__UpperCAmelCase						)

       a							=   self.dummy_model()
       a							=   self.dummy_sample_deter.to(__UpperCAmelCase						) * scheduler.init_noise_sigma
       a							=   sample.to(__UpperCAmelCase						)

       for t in scheduler.timesteps:
        a							=   scheduler.scale_model_input(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   model(__UpperCAmelCase				,      __UpperCAmelCase						)

        a							=   scheduler.step(__UpperCAmelCase				,      __UpperCAmelCase				,      __UpperCAmelCase						)
        a							=   output.prev_sample

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

       assert abs(result_sum.item() - 3812.9927						) < 2e-2
       assert abs(result_mean.item() - 4.9648						) < 1e-3










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






class 	lowercase_      (			enum.Enum     ):






      '''simple docstring'''




      __snake_case  						=						'''timm'''
      __snake_case  						=						'''transformers'''
def 		_a					(   a				:Optional[Iterable[str]]			,							a				:Optional[Iterable[int]]			,							a				:Optional[Iterable[str]]				)							->    Optional[Any]:
 if stage_names is None:
  raise ValueError('''Stage_names must be set for transformers backbones'''				)

 if out_features is not None:
  if not isinstance(a			,							(list,)				):
   raise ValueError(F"""out_features must be a list {type(a				)}"""				)
  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(a			,							(list, tuple)				):
   raise ValueError(F"""out_indices must be a list or tuple, got {type(a				)}"""				)
  if any(idx >= len(a				) 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(a				) != len(a				):
   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					(   a				:Optional[List[str]]			,							a				:Optional[Union[List[int], Tuple[int]]]			,							a				:List[str]			,							)							->    Union[str, Any]:
 if out_indices is None and out_features is None:
  a							=   [len(a				) - 1]
  a							=   [stage_names[-1]]
 elif out_indices is None and out_features is not None:
  a							=   [stage_names.index(a				) 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					(   a				:Optional[List[str]]			,							a				:Optional[Union[List[int], Tuple[int]]]			,							a				:List[str]			,							)							->    Tuple[List[str], List[int]]:
 verify_out_features_out_indices(out_features=a			,							out_indices=a			,							stage_names=a				)
 a						,  a							=   _align_output_features_output_indices(
     out_features=a			,							out_indices=a			,							stage_names=a				)
 # Verify that the aligned out_features and out_indices are valid
 verify_out_features_out_indices(out_features=a			,							out_indices=a			,							stage_names=a				)
 return output_features, output_indices






class 	lowercase_      :






      '''simple docstring'''




      __snake_case  						=						None







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		Tuple						)					->None:




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		Tuple						)					->None:




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		str				,      __UpperCAmelCase   :		Union[str, Any]						)					->None:




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		Dict						)					->Optional[Any]:




       """simple docstring"""


       return self._out_features







      @out_features.setter
      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		List[str]						)					->List[Any]:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		Dict						)					->int:




       """simple docstring"""


       return self._out_indices







      @out_indices.setter
      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		Union[Tuple[int], List[int]]						)					->List[str]:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		Tuple						)					->str:




       """simple docstring"""


       return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names						)}







      @property
      def    __lowerCAmelCase      (   self   :		int						)					->Optional[int]:




       """simple docstring"""


       return [self.out_feature_channels[name] for name in self.out_features]







      def    __lowerCAmelCase      (   self   :		Optional[Any]				,      *__UpperCAmelCase   :		List[str]				,      **__UpperCAmelCase   :		Optional[Any]						)					->Optional[int]:




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		Dict				,      __UpperCAmelCase   :		Optional[bool] = None				,      __UpperCAmelCase   :		Optional[bool] = None				,      __UpperCAmelCase   :		Optional[bool] = None				,      )					->int:




       """simple docstring"""


       raise NotImplementedError('''This method should be implemented by the derived class.'''						)







      def    __lowerCAmelCase      (   self   :		Union[str, Any]						)					->Optional[Any]:




       """simple docstring"""


       a							=   super().to_dict()
       a							=   output.pop('''_out_features'''						)
       a							=   output.pop('''_out_indices'''						)
       return output






class 	lowercase_      :






      '''simple docstring'''




      @property
      def    __lowerCAmelCase      (   self   :		List[str]						)					->Optional[int]:




       """simple docstring"""


       return self._out_features







      @out_features.setter
      def    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		List[str]						)					->int:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->str:




       """simple docstring"""


       return self._out_indices







      @out_indices.setter
      def    __lowerCAmelCase      (   self   :		str				,      __UpperCAmelCase   :		Union[Tuple[int], List[int]]						)					->int:




       """simple docstring"""


       a						,  a							=   get_aligned_output_features_output_indices(
           out_features=__UpperCAmelCase				,      out_indices=__UpperCAmelCase				,      stage_names=self.stage_names						)







      def    __lowerCAmelCase      (   self   :		Dict						)					->List[Any]:




       """simple docstring"""


       a							=   super().to_dict()
       a							=   output.pop('''_out_features'''						)
       a							=   output.pop('''_out_indices'''						)
       return output


