




'''simple docstring'''





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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class      _a    :
     SCREAMING_SNAKE_CASE_							:					CommonSchedulerState
     SCREAMING_SNAKE_CASE_							:					jnp.ndarray

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






     @classmethod
     def        _lowercase			(				cls      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Optional[int]:
          return cls(
              common=UpperCamelCase__      ,final_alpha_cumprod=UpperCamelCase__      ,init_noise_sigma=UpperCamelCase__      ,timesteps=UpperCamelCase__      ,)







@dataclass
class      _a    (		_SCREAMING_SNAKE_CASE							):
     SCREAMING_SNAKE_CASE_							:					DDIMSchedulerState







class      _a    (		_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE							):
     SCREAMING_SNAKE_CASE_							:					Union[str, Any]					       =		[e.name for e in FlaxKarrasDiffusionSchedulers]

     SCREAMING_SNAKE_CASE_							:					jnp.dtype






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






     @register_to_config
     def __init__(				self      ,_SCREAMING_SNAKE_CASE = 1_000      ,_SCREAMING_SNAKE_CASE = 0.0_0_0_1      ,_SCREAMING_SNAKE_CASE = 0.0_2      ,_SCREAMING_SNAKE_CASE = "linear"      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = 0      ,_SCREAMING_SNAKE_CASE = "epsilon"      ,_SCREAMING_SNAKE_CASE = jnp.floataa      ,)							->    Dict:
          _snake_case							=   dtype






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE = None					)							->    int:
          if common is None:
               _snake_case							=   CommonSchedulerState.create(self					)

          # At every step in ddim, we are looking into the previous alphas_cumprod
          # For the final step, there is no previous alphas_cumprod because we are already at 0
          # `set_alpha_to_one` decides whether we set this parameter simply to one or
          # whether we use the final alpha of the "non-previous" one.
          _snake_case							=   (
              jnp.array(1.0      ,dtype=self.dtype					) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
          )

          # standard deviation of the initial noise distribution
          _snake_case							=   jnp.array(1.0      ,dtype=self.dtype					)

          _snake_case							=   jnp.arange(0      ,self.config.num_train_timesteps					).round()[::-1]

          return DDIMSchedulerState.create(
              common=UpperCamelCase__      ,final_alpha_cumprod=UpperCamelCase__      ,init_noise_sigma=UpperCamelCase__      ,timesteps=UpperCamelCase__      ,)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None					)							->    Union[str, Any]:
          return sample






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = ()					)							->    Union[str, Any]:
          _snake_case							=   self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          _snake_case							=   (jnp.arange(0      ,UpperCamelCase__					) * step_ratio).round()[::-1] + self.config.steps_offset

          return state.replace(
              num_inference_steps=UpperCamelCase__      ,timesteps=UpperCamelCase__      ,)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							=   state.common.alphas_cumprod[timestep]
          _snake_case							=   jnp.where(
              prev_timestep >= 0      ,state.common.alphas_cumprod[prev_timestep]      ,state.final_alpha_cumprod					)
          _snake_case							=   1 - alpha_prod_t
          _snake_case							=   1 - alpha_prod_t_prev

          _snake_case							=   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

          return variance






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = 0.0      ,_SCREAMING_SNAKE_CASE = True      ,)							->    Dict:
          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"					)

          # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
          # Ideally, read DDIM paper in-detail understanding

          # Notation (<variable name> -> <name in paper>
          # - pred_noise_t -> e_theta(x_t, t)
          # - pred_original_sample -> f_theta(x_t, t) or x_0
          # - std_dev_t -> sigma_t
          # - eta -> η
          # - pred_sample_direction -> "direction pointing to x_t"
          # - pred_prev_sample -> "x_t-1"

          # 1. get previous step value (=t-1)
          _snake_case							=   timestep - self.config.num_train_timesteps // state.num_inference_steps

          _snake_case							=   state.common.alphas_cumprod
          _snake_case							=   state.final_alpha_cumprod

          # 2. compute alphas, betas
          _snake_case							=   alphas_cumprod[timestep]
          _snake_case							=   jnp.where(prev_timestep >= 0      ,alphas_cumprod[prev_timestep]      ,UpperCamelCase__					)

          _snake_case							=   1 - alpha_prod_t

          # 3. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          if self.config.prediction_type == "epsilon":
               _snake_case							=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
               _snake_case							=   model_output
          elif self.config.prediction_type == "sample":
               _snake_case							=   model_output
               _snake_case							=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
          elif self.config.prediction_type == "v_prediction":
               _snake_case							=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
               _snake_case							=   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
          else:
               raise ValueError(
                   f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
                   " `v_prediction`"					)

          # 4. compute variance: "sigma_t(η)" -> see formula (16)
          # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
          _snake_case							=   self._get_variance(UpperCamelCase__      ,UpperCamelCase__      ,UpperCamelCase__					)
          _snake_case							=   eta * variance ** 0.5

          # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _snake_case							=   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

          # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _snake_case							=   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

          if not return_dict:
               return (prev_sample, state)

          return FlaxDDIMSchedulerOutput(prev_sample=UpperCamelCase__      ,state=UpperCamelCase__					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Optional[int]:
          return add_noise_common(state.common      ,UpperCamelCase__      ,UpperCamelCase__      ,UpperCamelCase__					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,)							->    Union[str, Any]:
          return get_velocity_common(state.common      ,UpperCamelCase__      ,UpperCamelCase__      ,UpperCamelCase__					)






     def __len__(				self					)							->    Tuple:
          return self.config.num_train_timesteps








'''simple docstring'''





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

class      _a       (		enum.Enum							):
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		"""timm"""
     SCREAMING_SNAKE_CASE_							:					int					       =		"""transformers"""







def 			__a			(     _UpperCamelCase:					Optional[Iterable[str]]      ,		_UpperCamelCase:					Optional[Iterable[int]]      ,		_UpperCamelCase:					Optional[Iterable[str]]					)		->				Union[str, Any]:

     """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(_UpperCamelCase      ,		(list,)					):
               raise ValueError(F"""out_features must be a list {type(_UpperCamelCase					)}"""					)
          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(_UpperCamelCase      ,		(list, tuple)					):
               raise ValueError(F"""out_indices must be a list or tuple, got {type(_UpperCamelCase					)}"""					)
          if any(idx >= len(_UpperCamelCase					) 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(_UpperCamelCase					) != len(_UpperCamelCase					):
               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			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Any:

     """simple docstring"""




     if out_indices is None and out_features is None:
          _snake_case							=   [len(_UpperCamelCase					) - 1]
          _snake_case							=   [stage_names[-1]]
     elif out_indices is None and out_features is not None:
          _snake_case							=   [stage_names.index(_UpperCamelCase					) for layer in out_features]
     elif out_features is None and out_indices is not None:
          _snake_case							=   [stage_names[idx] for idx in out_indices]
     return out_features, out_indices





def 			__a			(     _UpperCamelCase:					Optional[List[str]]      ,		_UpperCamelCase:					Optional[Union[List[int], Tuple[int]]]      ,		_UpperCamelCase:					List[str]      ,		)		->				Tuple[List[str], List[int]]:

     """simple docstring"""




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

class      _a       :
     SCREAMING_SNAKE_CASE_							:					Optional[BackboneType]					       =		None






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          if getattr(self      ,"_backbone"      ,_SCREAMING_SNAKE_CASE					) 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']
          _snake_case							=   [stage["module"] for stage in self._backbone.feature_info.info]
          _snake_case							=   [stage["num_chs"] for stage in self._backbone.feature_info.info]
          _snake_case							=   self._backbone.feature_info.out_indices
          _snake_case							=   self._backbone.feature_info.module_name()

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"stage_names"					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_features"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"out_indices"      ,_SCREAMING_SNAKE_CASE					)

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    None:
          _snake_case							=   config

          _snake_case							=   getattr(_SCREAMING_SNAKE_CASE      ,"use_timm_backbone"      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

          if self.backbone_type == BackboneType.TIMM:
               self._init_timm_backbone(_SCREAMING_SNAKE_CASE					)
          elif self.backbone_type == BackboneType.TRANSFORMERS:
               self._init_transformers_backbone(_SCREAMING_SNAKE_CASE					)
          else:
               raise ValueError(f"""backbone_type {self.backbone_type} not supported."""					)






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






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    Any:
          return self._out_indices






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    int:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






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






     def        _lowercase			(				self      ,*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							=   dict(inspect.signature(self.forward					).parameters					)
          _snake_case							=   {k: v for k, v in kwargs.items() if k in signature}
          return self(*_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,)							->    Any:
          raise NotImplementedError("This method should be implemented by the derived class."					)






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output







class      _a       :






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






     @out_features.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    str:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     @property
     def        _lowercase			(				self					)							->    Any:
          return self._out_indices






     @out_indices.setter
     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    Optional[Any]:
          _snake_case							,      _snake_case							=   get_aligned_output_features_output_indices(
              out_features=_SCREAMING_SNAKE_CASE      ,out_indices=_SCREAMING_SNAKE_CASE      ,stage_names=self.stage_names					)






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   super().to_dict()
          _snake_case							=   output.pop("_out_features"					)
          _snake_case							=   output.pop("_out_indices"					)
          return output


