






# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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    _lowerCAmelCase			:



    """simple docstring"""



    _lowercase				:   CommonSchedulerState

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



    @classmethod
    def 					__magic_name__							(      cls    :    Optional[int]			,		UpperCamelCase__    :    CommonSchedulerState			,		UpperCamelCase__    :    jnp.ndarray			,		UpperCamelCase__    :    jnp.ndarray):






           '''simple docstring'''







           return cls(common=UpperCamelCase__			,		init_noise_sigma=UpperCamelCase__			,		timesteps=UpperCamelCase__)


@dataclass
class    _lowerCAmelCase			(		lowercase_      ):



    """simple docstring"""



    _lowercase				:   DDPMSchedulerState


class    _lowerCAmelCase			(		lowercase_     ,      lowercase_      ):



    """simple docstring"""



    _lowercase				:   List[Any]      =  [e.name for e in FlaxKarrasDiffusionSchedulers]

    _lowercase				:   jnp.dtype



    @property
    def 					__magic_name__							(      self    :    Dict):






           '''simple docstring'''







           return True



    @register_to_config
    def __init__(      self    :    int			,		UpperCamelCase__    :    int = 1_0_0_0			,		UpperCamelCase__    :    float = 0.00_01			,		UpperCamelCase__    :    float = 0.02			,		UpperCamelCase__    :    str = "linear"			,		UpperCamelCase__    :    Optional[jnp.ndarray] = None			,		UpperCamelCase__    :    str = "fixed_small"			,		UpperCamelCase__    :    bool = True			,		UpperCamelCase__    :    str = "epsilon"			,		UpperCamelCase__    :    jnp.dtype = jnp.floataa			,		):






           '''simple docstring'''







           snake_case__	  =       dtype



    def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    Optional[CommonSchedulerState] = None):






           '''simple docstring'''







           if common is None:
                  snake_case__	  =       CommonSchedulerState.create(self)

           # 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 DDPMSchedulerState.create(
               common=UpperCamelCase__			,		init_noise_sigma=UpperCamelCase__			,		timesteps=UpperCamelCase__			,		)



    def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    DDPMSchedulerState			,		UpperCamelCase__    :    jnp.ndarray			,		UpperCamelCase__    :    Optional[int] = None):






           '''simple docstring'''







           return sample



    def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    DDPMSchedulerState			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    Tuple = ()):






           '''simple docstring'''







           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]

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



    def 					__magic_name__							(      self    :    Dict			,		UpperCamelCase__    :    DDPMSchedulerState			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    List[Any]=None			,		UpperCamelCase__    :    List[str]=None):






           '''simple docstring'''







           snake_case__	  =       state.common.alphas_cumprod[t]
           snake_case__	  =       jnp.where(t > 0			,		state.common.alphas_cumprod[t - 1]			,		jnp.array(1.0			,		dtype=self.dtype))

           # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
           # and sample from it to get previous sample
           # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
           snake_case__	  =       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

           if variance_type is None:
                  snake_case__	  =       self.config.variance_type

           # hacks - were probably added for training stability
           if variance_type == "fixed_small":
                  snake_case__	  =       jnp.clip(UpperCamelCase__			,		a_min=1E-20)
           # for rl-diffuser https://arxiv.org/abs/2205.09991
           elif variance_type == "fixed_small_log":
                  snake_case__	  =       jnp.log(jnp.clip(UpperCamelCase__			,		a_min=1E-20))
           elif variance_type == "fixed_large":
                  snake_case__	  =       state.common.betas[t]
           elif variance_type == "fixed_large_log":
                  # Glide max_log
                  snake_case__	  =       jnp.log(state.common.betas[t])
           elif variance_type == "learned":
                  return predicted_variance
           elif variance_type == "learned_range":
                  snake_case__	  =       variance
                  snake_case__	  =       state.common.betas[t]
                  snake_case__	  =       (predicted_variance + 1) / 2
                  snake_case__	  =       frac * max_log + (1 - frac) * min_log

           return variance



    def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    DDPMSchedulerState			,		UpperCamelCase__    :    jnp.ndarray			,		UpperCamelCase__    :    int			,		UpperCamelCase__    :    jnp.ndarray			,		UpperCamelCase__    :    Optional[jax.random.KeyArray] = None			,		UpperCamelCase__    :    bool = True			,		):






           '''simple docstring'''







           snake_case__	  =       timestep

           if key is None:
                  snake_case__	  =       jax.random.PRNGKey(0)

           if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
                  snake_case__  , snake_case__	  =       jnp.split(UpperCamelCase__			,		sample.shape[1]			,		axis=1)
           else:
                  snake_case__	  =       None

           # 1. compute alphas, betas
           snake_case__	  =       state.common.alphas_cumprod[t]
           snake_case__	  =       jnp.where(t > 0			,		state.common.alphas_cumprod[t - 1]			,		jnp.array(1.0			,		dtype=self.dtype))
           snake_case__	  =       1 - alpha_prod_t
           snake_case__	  =       1 - alpha_prod_t_prev

           # 2. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
           if self.config.prediction_type == "epsilon":
                  snake_case__	  =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           elif self.config.prediction_type == "sample":
                  snake_case__	  =       model_output
           elif self.config.prediction_type == "v_prediction":
                  snake_case__	  =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           else:
                  raise ValueError(
                      F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
                      """ for the FlaxDDPMScheduler.""")

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                  snake_case__	  =       jnp.clip(UpperCamelCase__			,		-1			,		1)

           # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           snake_case__	  =       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
           snake_case__	  =       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

           # 5. Compute predicted previous sample µ_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           snake_case__	  =       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

           # 6. Add noise
           def random_variance():
                  snake_case__	  =       jax.random.split(UpperCamelCase__			,		num=1)
                  snake_case__	  =       jax.random.normal(UpperCamelCase__			,		shape=model_output.shape			,		dtype=self.dtype)
                  return (self._get_variance(UpperCamelCase__			,		UpperCamelCase__			,		predicted_variance=UpperCamelCase__) ** 0.5) * noise

           snake_case__	  =       jnp.where(t > 0			,		random_variance()			,		jnp.zeros(model_output.shape			,		dtype=self.dtype))

           snake_case__	  =       pred_prev_sample + variance

           if not return_dict:
                  return (pred_prev_sample, state)

           return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase__			,		state=UpperCamelCase__)



    def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    DDPMSchedulerState			,		UpperCamelCase__    :    jnp.ndarray			,		UpperCamelCase__    :    jnp.ndarray			,		UpperCamelCase__    :    jnp.ndarray			,		):






           '''simple docstring'''







           return add_noise_common(state.common			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__)



    def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    DDPMSchedulerState			,		UpperCamelCase__    :    jnp.ndarray			,		UpperCamelCase__    :    jnp.ndarray			,		UpperCamelCase__    :    jnp.ndarray			,		):






           '''simple docstring'''







           return get_velocity_common(state.common			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__)



    def __len__(      self    :    Any):






           '''simple docstring'''







           return self.config.num_train_timesteps








from __future__ import annotations


class    _lowerCAmelCase			:



				"""simple docstring"""



				def __init__(      self    :    Optional[int]			,		UpperCamelCase__    :    int):






											'''simple docstring'''







											snake_case__	  =       data
											snake_case__	  =       None
											snake_case__	  =       None


def _UpperCAmelCase   (	a						:	Node | None ):  # In Order traversal of the tree
							if tree:
														display(tree.left )
														print(tree.data )
														display(tree.right )

def _UpperCAmelCase   (	a						:	Node | None ):
							return 1 + max(depth_of_tree(tree.left )  ,					depth_of_tree(tree.right ) ) if tree else 0

def _UpperCAmelCase   (	a						:	Node ):
							if not tree:
														return True
							if tree.left and tree.right:
														return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
							else:
														return not tree.left and not tree.right

def _UpperCAmelCase   (	):  # Main function for testing.
							snake_case__	  =       Node(1 )
							snake_case__	  =       Node(2 )
							snake_case__	  =       Node(3 )
							snake_case__	  =       Node(4 )
							snake_case__	  =       Node(5 )
							snake_case__	  =       Node(6 )
							snake_case__	  =       Node(7 )
							snake_case__	  =       Node(8 )
							snake_case__	  =       Node(9 )

							print(is_full_binary_tree(a ) )
							print(depth_of_tree(a ) )
							print("""Tree is: """ )
							display(a )


if __name__ == "__main__":
	main()
