

# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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     __magic_name__     :
 UpperCAmelCase				     =    42
 UpperCAmelCase				     =    42

 # setable values
 UpperCAmelCase				     =    42
 UpperCAmelCase				     =    42
 UpperCAmelCase				     =    None


 @classmethod
 def _UpperCamelCase						(    cls		:			List[str]      ,    lowerCAmelCase__		:			CommonSchedulerState      ,    lowerCAmelCase__		:			jnp.ndarray      ,    lowerCAmelCase__		:			jnp.ndarray      ,    lowerCAmelCase__		:			jnp.ndarray      ,    )     ->   Any:
   return cls(
       common=a_      ,    final_alpha_cumprod=a_      ,    init_noise_sigma=a_      ,    timesteps=a_      ,    )






@dataclass
class     __magic_name__     (							__UpperCamelCase     ):
 UpperCAmelCase				     =    42






class     __magic_name__     (							__UpperCamelCase						,    __UpperCamelCase     ):
 UpperCAmelCase				     =    [e.name for e in FlaxKarrasDiffusionSchedulers]

 UpperCAmelCase				     =    42


 @property
 def _UpperCamelCase						(    self		:			List[str]     )     ->   str:
   return True


 @register_to_config
 def __init__(    self		:			Any      ,    lowerCAmelCase__		:			int = 1_0_0_0      ,    lowerCAmelCase__		:			float = 0.0_001      ,    lowerCAmelCase__		:			float = 0.02      ,    lowerCAmelCase__		:			str = "linear"      ,    lowerCAmelCase__		:			Optional[jnp.ndarray] = None      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			int = 0      ,    lowerCAmelCase__		:			str = "epsilon"      ,    lowerCAmelCase__		:			jnp.dtype = jnp.floataa      ,    )     ->   Tuple:
   UpperCAmelCase          =	dtype


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			Optional[CommonSchedulerState] = None     )     ->   List[Any]:





   if common is None:
     UpperCAmelCase          =	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.
   UpperCAmelCase          =	(
       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
   UpperCAmelCase          =	jnp.array(1.0      ,    dtype=self.dtype     )

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

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


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			DDIMSchedulerState      ,    lowerCAmelCase__		:			jnp.ndarray      ,    lowerCAmelCase__		:			Optional[int] = None     )     ->   str:
   return sample


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			DDIMSchedulerState      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			Tuple = ()     )     ->   Dict:
   UpperCAmelCase          =	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
   UpperCAmelCase          =	(jnp.arange(0      ,    a_     ) * step_ratio).round()[::-1] + self.config.steps_offset

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


 def _UpperCamelCase						(    self		:			Dict      ,    lowerCAmelCase__		:			DDIMSchedulerState      ,    lowerCAmelCase__		:			List[Any]      ,    lowerCAmelCase__		:			Optional[Any]     )     ->   Union[str, Any]:
   UpperCAmelCase          =	state.common.alphas_cumprod[timestep]
   UpperCAmelCase          =	jnp.where(
       prev_timestep >= 0      ,    state.common.alphas_cumprod[prev_timestep]      ,    state.final_alpha_cumprod     )
   UpperCAmelCase          =	1 - alpha_prod_t
   UpperCAmelCase          =	1 - alpha_prod_t_prev

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

   return variance


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			DDIMSchedulerState      ,    lowerCAmelCase__		:			jnp.ndarray      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			jnp.ndarray      ,    lowerCAmelCase__		:			float = 0.0      ,    lowerCAmelCase__		:			bool = True      ,    )     ->   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"     )

   # 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)
   UpperCAmelCase          =	timestep - self.config.num_train_timesteps // state.num_inference_steps

   UpperCAmelCase          =	state.common.alphas_cumprod
   UpperCAmelCase          =	state.final_alpha_cumprod

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

   UpperCAmelCase          =	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":
     UpperCAmelCase          =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     UpperCAmelCase          =	model_output
   elif self.config.prediction_type == "sample":
     UpperCAmelCase          =	model_output
     UpperCAmelCase          =	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
   elif self.config.prediction_type == "v_prediction":
     UpperCAmelCase          =	(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
     UpperCAmelCase          =	(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)
   UpperCAmelCase          =	self._get_variance(a_      ,    a_      ,    a_     )
   UpperCAmelCase          =	eta * variance ** 0.5

   # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   UpperCAmelCase          =	(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
   UpperCAmelCase          =	alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

   if not return_dict:
     return (prev_sample, state)

   return FlaxDDIMSchedulerOutput(prev_sample=a_      ,    state=a_     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			DDIMSchedulerState      ,    lowerCAmelCase__		:			jnp.ndarray      ,    lowerCAmelCase__		:			jnp.ndarray      ,    lowerCAmelCase__		:			jnp.ndarray      ,    )     ->   Optional[int]:
   return add_noise_common(state.common      ,    a_      ,    a_      ,    a_     )


 def _UpperCamelCase						(    self		:			Optional[int]      ,    lowerCAmelCase__		:			DDIMSchedulerState      ,    lowerCAmelCase__		:			jnp.ndarray      ,    lowerCAmelCase__		:			jnp.ndarray      ,    lowerCAmelCase__		:			jnp.ndarray      ,    )     ->   List[Any]:
   return get_velocity_common(state.common      ,    a_      ,    a_      ,    a_     )


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








class 						UpperCAmelCase__     :




			'''simple docstring'''






			def __init__(  self				:					List[Any]			,			a_				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      data
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None
					__UpperCAmelCase       :	BinaryTreeNode | None						=      None

def 			a		( _UpperCAmelCase				:   BinaryTreeNode | None					,  _UpperCAmelCase				:   int		):







		'''simple docstring'''





		if node is None:
				__UpperCAmelCase       :	List[Any]						=      BinaryTreeNode(_UpperCAmelCase		)
				return node

		# binary search tree is not empty,
		# so we will insert it into the tree
		# if new_value is less than value of data in node,
		#  add it to left subtree and proceed recursively
		if new_value < node.data:
				__UpperCAmelCase       :	int						=      insert(node.left_child					,  _UpperCAmelCase		)
		else:
				# if new_value is greater than value of data in node,
				#  add it to right subtree and proceed recursively
				__UpperCAmelCase       :	str						=      insert(node.right_child					,  _UpperCAmelCase		)
		return node

def 			a		( _UpperCAmelCase				:   None | BinaryTreeNode		):  # if node is None,return







		'''simple docstring'''





		if node:
				__UpperCAmelCase       :	Optional[int]						=      inorder(node.left_child		)
				__UpperCAmelCase       :	int						=      [*inorder_array, node.data]
				__UpperCAmelCase       :	Optional[Any]						=      inorder_array + inorder(node.right_child		)
		else:
				__UpperCAmelCase       :	List[Any]						=      []
		return inorder_array

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Any						=      insert(_UpperCAmelCase					,  15		)
		insert(_UpperCAmelCase					,  10		)
		insert(_UpperCAmelCase					,  25		)
		insert(_UpperCAmelCase					,  6		)
		insert(_UpperCAmelCase					,  14		)
		insert(_UpperCAmelCase					,  20		)
		insert(_UpperCAmelCase					,  60		)
		return root

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      make_tree()
		print('''Printing values of binary search tree in Inorder Traversal.'''		)
		inorder(_UpperCAmelCase		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()





