






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										=     42

     # setable values
     __lowerCamelCase										=     42
     __lowerCamelCase										=     42
     __lowerCamelCase										=     42
     __lowerCamelCase										=     None

     # running values
     __lowerCamelCase										=     None




     @classmethod
     def      __UpperCAmelCase     (							cls  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          return cls(common=_lowerCAmelCase  ,	init_noise_sigma=_lowerCAmelCase  ,	timesteps=_lowerCAmelCase  ,	sigmas=_lowerCAmelCase		)







@dataclass
class 							UpperCAmelCase_     (		__lowerCamelCase		):
     __lowerCamelCase										=     42







class 							UpperCAmelCase_     (		__lowerCamelCase       ,			__lowerCamelCase		):
     __lowerCamelCase										=     [e.name for e in FlaxKarrasDiffusionSchedulers]

     __lowerCamelCase										=     42




     @property
     def      __UpperCAmelCase     (							self		):
          return True




     @register_to_config
     def __init__(							self  ,	_lowerCAmelCase = 1000  ,	_lowerCAmelCase = 0.0_0_0_1  ,	_lowerCAmelCase = 0.0_2  ,	_lowerCAmelCase = "linear"  ,	_lowerCAmelCase = None  ,	_lowerCAmelCase = "epsilon"  ,	_lowerCAmelCase = jnp.floataa  ,	):
          UpperCAmelCase__			:     Optional[Any]										= dtype




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase = None		):
          if common is None:
               UpperCAmelCase__			:     Optional[int]										= CommonSchedulerState.create(self		)

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

          # standard deviation of the initial noise distribution
          UpperCAmelCase__			:     List[str]										= sigmas.max()

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




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          (UpperCAmelCase__		)			:     Optional[int]										= jnp.where(state.timesteps == timestep  ,	size=1		)
          UpperCAmelCase__			:     Tuple										= step_index[0]

          UpperCAmelCase__			:     List[str]										= state.sigmas[step_index]
          UpperCAmelCase__			:     Optional[Any]										= sample / ((sigma**2 + 1) ** 0.5)
          return sample




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):

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

          UpperCAmelCase__			:     Optional[Any]										= integrate.quad(_lowerCAmelCase  ,	state.sigmas[t]  ,	state.sigmas[t + 1]  ,	epsrel=1e-4		)[0]

          return integrated_coeff




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase = ()		):
          UpperCAmelCase__			:     List[Any]										= jnp.linspace(self.config.num_train_timesteps - 1  ,	0  ,	_lowerCAmelCase  ,	dtype=self.dtype		)

          UpperCAmelCase__			:     Tuple										= jnp.floor(_lowerCAmelCase		).astype(jnp.intaa		)
          UpperCAmelCase__			:     Optional[Any]										= jnp.ceil(_lowerCAmelCase		).astype(jnp.intaa		)

          UpperCAmelCase__			:     List[Any]										= jnp.mod(_lowerCAmelCase  ,	1.0		)

          UpperCAmelCase__			:     int										= ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
          UpperCAmelCase__			:     Union[str, Any]										= (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
          UpperCAmelCase__			:     str										= jnp.concatenate([sigmas, jnp.array([0.0]  ,	dtype=self.dtype		)]		)

          UpperCAmelCase__			:     str										= timesteps.astype(jnp.intaa		)

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

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




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase = 4  ,	_lowerCAmelCase = True  ,	):
          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"""		)

          UpperCAmelCase__			:     Union[str, Any]										= state.sigmas[timestep]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
               UpperCAmelCase__			:     Optional[Any]										= sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               UpperCAmelCase__			:     Dict										= 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
          UpperCAmelCase__			:     Dict										= (sample - pred_original_sample) / sigma
          UpperCAmelCase__			:     Union[str, Any]										= state.replace(derivatives=jnp.append(state.derivatives  ,	_lowerCAmelCase		)		)
          if len(state.derivatives		) > order:
               UpperCAmelCase__			:     Optional[int]										= state.replace(derivatives=jnp.delete(state.derivatives  ,	0		)		)

          # 3. Compute linear multistep coefficients
          UpperCAmelCase__			:     Union[str, Any]										= min(timestep + 1  ,	_lowerCAmelCase		)
          UpperCAmelCase__			:     List[Any]										= [self.get_lms_coefficient(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		) for curr_order in range(_lowerCAmelCase		)]

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

          if not return_dict:
               return (prev_sample, state)

          return FlaxLMSSchedulerOutput(prev_sample=_lowerCAmelCase  ,	state=_lowerCAmelCase		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	):
          UpperCAmelCase__			:     int										= state.sigmas[timesteps].flatten()
          UpperCAmelCase__			:     Dict										= broadcast_to_shape_from_left(_lowerCAmelCase  ,	noise.shape		)

          UpperCAmelCase__			:     Any										= original_samples + noise * sigma

          return noisy_samples




     def __len__(							self		):
          return self.config.num_train_timesteps














from __future__ import annotations







class 							UpperCAmelCase_     :




     def __init__(							self  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     List[Any]										= value
          UpperCAmelCase__			:     Node | None										= None
          UpperCAmelCase__			:     Node | None										= None







class 							UpperCAmelCase_     :
     __lowerCamelCase										=     42




     def __init__(							self		):
          UpperCAmelCase__			:     Any										= 0




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
          if node is None:
               return

          if path_sum == self.target:
               self.paths += 1

          if node.left:
               self.depth_first_search(node.left  ,	path_sum + node.left.value		)
          if node.right:
               self.depth_first_search(node.right  ,	path_sum + node.right.value		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase = None		):
          if node is None:
               return 0
          if target is not None:
               UpperCAmelCase__			:     List[Any]										= target

          self.depth_first_search(_lowerCAmelCase  ,	node.value		)
          self.path_sum(node.left		)
          self.path_sum(node.right		)

          return self.paths


if __name__ == "__main__":
      import doctest

      doctest.testmod()






