



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

class    _SCREAMING_SNAKE_CASE						(enum.Enum			):
     lowerCAmelCase       =      """timm"""
     lowerCAmelCase       =      """transformers"""






def 						_lowerCAmelCase    (					__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 						_lowerCAmelCase    (					__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:
  __SCREAMING_SNAKE_CASE							:     List[str]							=						[len(__lowerCamelCase  ) - 1]
  __SCREAMING_SNAKE_CASE							:     Dict							=						[stage_names[-1]]
 elif out_indices is None and out_features is not None:
  __SCREAMING_SNAKE_CASE							:     int							=						[stage_names.index(__lowerCamelCase  ) for layer in out_features]
 elif out_features is None and out_indices is not None:
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[stage_names[idx] for idx in out_indices]
 return out_features, out_indices






def 						_lowerCAmelCase    (					__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  )
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     int							=						_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    _SCREAMING_SNAKE_CASE						:
     lowerCAmelCase       =      None





     def 						__snake_case     (							self			:				Optional[int]  ,	UpperCamelCase			:				Optional[int]					)->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']
      __SCREAMING_SNAKE_CASE							:     Tuple							=						[stage["module"] for stage in self._backbone.feature_info.info]
      __SCREAMING_SNAKE_CASE							:     Any							=						[stage["num_chs"] for stage in self._backbone.feature_info.info]
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self._backbone.feature_info.out_indices
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						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					)
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						out_features, out_indices





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				Tuple					)->None:
      __SCREAMING_SNAKE_CASE							:     Dict							=						getattr(UpperCamelCase  ,	"stage_names"					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						getattr(UpperCamelCase  ,	"out_features"  ,	UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						getattr(UpperCamelCase  ,	"out_indices"  ,	UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Dict							=						stage_names
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[int]							=						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
      __SCREAMING_SNAKE_CASE							:     str							=						None





     def 						__snake_case     (							self			:				Dict  ,	UpperCamelCase			:				Optional[int]					)->None:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						config

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						getattr(UpperCamelCase  ,	"use_timm_backbone"  ,	UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     int							=						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 						__snake_case     (							self			:				str					)->List[Any]:
      return self._out_features





     @out_features.setter
     def 						__snake_case     (							self			:				List[str]  ,	UpperCamelCase			:				List[str]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)





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





     @out_indices.setter
     def 						__snake_case     (							self			:				Union[str, Any]  ,	UpperCamelCase			:				Union[Tuple[int], List[int]]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)





     @property
     def 						__snake_case     (							self			:				Union[str, Any]					)->int:
      # 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 						__snake_case     (							self			:				Tuple					)->Optional[Any]:
      return [self.out_feature_channels[name] for name in self.out_features]





     def 						__snake_case     (							self			:				Optional[int]  ,	*UpperCamelCase			:				str  ,	**UpperCamelCase			:				Union[str, Any]					)->str:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						dict(inspect.signature(self.forward					).parameters					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						{k: v for k, v in kwargs.items() if k in signature}
      return self(*UpperCamelCase  ,	**UpperCamelCase					)





     def 						__snake_case     (							self			:				int  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Optional[bool] = None  ,	UpperCamelCase			:				Optional[bool] = None  ,	UpperCamelCase			:				Optional[bool] = None  ,	)->Dict:
      raise NotImplementedError("This method should be implemented by the derived class."					)






     def 						__snake_case     (							self			:				List[str]					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						super().to_dict()
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						output.pop("_out_features"					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						output.pop("_out_indices"					)
      return output






class    _SCREAMING_SNAKE_CASE						:





     @property
     def 						__snake_case     (							self			:				List[str]					)->Any:
      return self._out_features





     @out_features.setter
     def 						__snake_case     (							self			:				Optional[Any]  ,	UpperCamelCase			:				List[str]					)->str:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[int]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)





     @property
     def 						__snake_case     (							self			:				Dict					)->List[str]:
      return self._out_indices





     @out_indices.setter
     def 						__snake_case     (							self			:				str  ,	UpperCamelCase			:				Union[Tuple[int], List[int]]					)->str:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[Any]							=						get_aligned_output_features_output_indices(
          out_features=UpperCamelCase  ,	out_indices=UpperCamelCase  ,	stage_names=self.stage_names					)






     def 						__snake_case     (							self			:				int					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						super().to_dict()
      __SCREAMING_SNAKE_CASE							:     Tuple							=						output.pop("_out_features"					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						output.pop("_out_indices"					)
      return output







"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)
@flax.struct.dataclass
class      UpperCamelCase__:
						lowerCAmelCase__	:    CommonSchedulerState

						# setable values
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    jnp.ndarray
						lowerCAmelCase__	:    Optional[int]    				=		None

						# running values
						lowerCAmelCase__	:    Optional[jnp.ndarray]    				=		None





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Optional[Any]:
													return cls(common=__UpperCAmelCase		,init_noise_sigma=__UpperCAmelCase		,timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase  )






@dataclass
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    LMSDiscreteSchedulerState






class      UpperCamelCase__(      __A		,    __A			):
						lowerCAmelCase__	:    List[str]    				=		[e.name for e in FlaxKarrasDiffusionSchedulers]

						lowerCAmelCase__	:    jnp.dtype





						@property
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													return True





						@register_to_config
						def __init__(							self		,__UpperCAmelCase = 10_00		,__UpperCAmelCase = 0.0_0_0_1		,__UpperCAmelCase = 0.0_2		,__UpperCAmelCase = "linear"		,__UpperCAmelCase = None		,__UpperCAmelCase = "epsilon"		,__UpperCAmelCase = jnp.floataa		,)       ->					str:
													A__						  =				dtype





						def 					snake_case__						(							self		,__UpperCAmelCase = None  )       ->					LMSDiscreteSchedulerState:
													if common is None:
																				A__						  =				CommonSchedulerState.create(self  )

													A__						  =				jnp.arange(0		,self.config.num_train_timesteps  ).round()[::-1]
													A__						  =				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

													# standard deviation of the initial noise distribution
													A__						  =				sigmas.max()

													return LMSDiscreteSchedulerState.create(
													    common=__UpperCAmelCase		,init_noise_sigma=__UpperCAmelCase		,timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					jnp.ndarray:
													((A__)  ,	)						  =				jnp.where(state.timesteps == timestep		,size=1  )
													A__						  =				step_index[0]

													A__						  =				state.sigmas[step_index]
													A__						  =				sample / ((sigma**2 + 1) ** 0.5)
													return sample





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Optional[int]:

													def lms_derivative(__UpperCAmelCase  ):
																				A__						  =				1.0
																				for k in range(__UpperCAmelCase  ):
																											if current_order == k:
																																		continue
																											prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																				return prod

													A__						  =				integrate.quad(__UpperCAmelCase		,state.sigmas[t]		,state.sigmas[t + 1]		,epsrel=1e-4  )[0]

													return integrated_coeff





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = ()  )       ->					LMSDiscreteSchedulerState:
													A__						  =				jnp.linspace(self.config.num_train_timesteps - 1		,0		,__UpperCAmelCase		,dtype=self.dtype  )

													A__						  =				jnp.floor(__UpperCAmelCase  ).astype(jnp.intaa  )
													A__						  =				jnp.ceil(__UpperCAmelCase  ).astype(jnp.intaa  )

													A__						  =				jnp.mod(__UpperCAmelCase		,1.0  )

													A__						  =				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
													A__						  =				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
													A__						  =				jnp.concatenate([sigmas, jnp.array([0.0]		,dtype=self.dtype  )]  )

													A__						  =				timesteps.astype(jnp.intaa  )

													# initial running values
													A__						  =				jnp.zeros((0,) + shape		,dtype=self.dtype  )

													return state.replace(
													    timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase		,num_inference_steps=__UpperCAmelCase		,derivatives=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = 4		,__UpperCAmelCase = True		,)       ->					Union[FlaxLMSSchedulerOutput, Tuple]:
													if state.num_inference_steps is None:
																				raise ValueError(
																				    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'  )

													A__						  =				state.sigmas[timestep]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																				A__						  =				sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																				# * c_out + input * c_skip
																				A__						  =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													else:
																				raise ValueError(
																				    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

													# 2. Convert to an ODE derivative
													A__						  =				(sample - pred_original_sample) / sigma
													A__						  =				state.replace(derivatives=jnp.append(state.derivatives		,__UpperCAmelCase  )  )
													if len(state.derivatives  ) > order:
																				A__						  =				state.replace(derivatives=jnp.delete(state.derivatives		,0  )  )

													# 3. Compute linear multistep coefficients
													A__						  =				min(timestep + 1		,__UpperCAmelCase  )
													A__						  =				[self.get_lms_coefficient(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ) for curr_order in range(__UpperCAmelCase  )]

													# 4. Compute previous sample based on the derivatives path
													A__						  =				sample + sum(
													    coeff * derivative for coeff, derivative in zip(__UpperCAmelCase		,reversed(state.derivatives  )  )  )

													if not return_dict:
																				return (prev_sample, state)

													return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase		,state=__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,)       ->					jnp.ndarray:
													A__						  =				state.sigmas[timesteps].flatten()
													A__						  =				broadcast_to_shape_from_left(__UpperCAmelCase		,noise.shape  )

													A__						  =				original_samples + noise * sigma

													return noisy_samples





						def __len__(							self  )       ->					Optional[Any]:
													return self.config.num_train_timesteps





