





# 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 					__A    :

			'''simple docstring'''







			lowerCAmelCase_							     =						42

			# setable values
			lowerCAmelCase_							     =						42
			lowerCAmelCase_							     =						42
			lowerCAmelCase_							     =						None





			@classmethod
			def 		__lowerCamelCase				(    cls       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







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






@dataclass
class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						42






class 					__A    (    lowerCAmelCase			,				lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCAmelCase_							     =						42





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return True





			@register_to_config
			def __init__(    self       ,  __lowerCAmelCase = 1_0_0_0       ,  __lowerCAmelCase = 0.0001       ,  __lowerCAmelCase = 0.02       ,  __lowerCAmelCase = "linear"       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = "fixed_small"       ,  __lowerCAmelCase = True       ,  __lowerCAmelCase = "epsilon"       ,  __lowerCAmelCase = jnp.floataa       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		dtype





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







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

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

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

				return DDPMSchedulerState.create(
				    common=__lowerCAmelCase       ,  init_noise_sigma=__lowerCAmelCase       ,  timesteps=__lowerCAmelCase       ,  )





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				return sample





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = ()							):



				'''simple docstring'''







				lowerCamelCase__								=		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
				lowerCamelCase__								=		(jnp.arange(0       ,  __lowerCAmelCase							) * step_ratio).round()[::-1]

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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase=None       ,  __lowerCAmelCase=None							):



				'''simple docstring'''







				lowerCamelCase__								=		state.common.alphas_cumprod[t]
				lowerCamelCase__								=		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
				lowerCamelCase__								=		(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

				return variance





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = True       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		timestep

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

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

				# 1. compute alphas, betas
				lowerCamelCase__								=		state.common.alphas_cumprod[t]
				lowerCamelCase__								=		jnp.where(t > 0       ,  state.common.alphas_cumprod[t - 1]       ,  jnp.array(1.0       ,  dtype=self.dtype							)							)
				lowerCamelCase__								=		1 - alpha_prod_t
				lowerCamelCase__								=		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":
					lowerCamelCase__								=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
					lowerCamelCase__								=		model_output
				elif self.config.prediction_type == "v_prediction":
					lowerCamelCase__								=		(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:
					lowerCamelCase__								=		jnp.clip(__lowerCAmelCase       ,  -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
				lowerCamelCase__								=		(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
				lowerCamelCase__								=		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
				lowerCamelCase__								=		pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

				lowerCamelCase__								=		pred_prev_sample + variance

				if not return_dict:
					return (pred_prev_sample, state)

				return FlaxDDPMSchedulerOutput(prev_sample=__lowerCAmelCase       ,  state=__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  ):



				'''simple docstring'''







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





			def __len__(    self							):



				'''simple docstring'''







				return self.config.num_train_timesteps










from __future__ import annotations

from random import random






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__								=		value
				lowerCamelCase__								=		random()
				lowerCamelCase__								=		None
				lowerCamelCase__								=		None





			def __repr__(    self							):



				'''simple docstring'''







				from pprint import pformat

				if self.left is None and self.right is None:
					return F'\'{self.value}: {self.prior:.5}\''
				else:
					return pformat(
					    {F'{self.value}: {self.prior:.5}': (self.left, self.right)}       ,  indent=1							)





			def __str__(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		str(self.value							) + ''' '''
				lowerCamelCase__								=		str(self.left or ''''''							)
				lowerCamelCase__								=		str(self.right or ''''''							)
				return value + left + right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      tuple[Node | None, Node | None]:







	'''simple docstring'''







	if root is None:  # None tree is split into 2 Nones
		return None, None
	elif root.value is None:
		return None, None
	else:
		if value < root.value:
			lowerCamelCase__				,	lowerCamelCase__								=		split(root.left       ,__snake_case							)
			return left, root
		else:
			lowerCamelCase__				,	lowerCamelCase__								=		split(root.right       ,__snake_case							)
			return root, right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	if (not left) or (not right):  # If one node is None, return the other
		return left or right
	elif left.prior < right.prior:
		lowerCamelCase__								=		merge(left.right       ,__snake_case							)
		return left
	else:
		lowerCamelCase__								=		merge(__snake_case       ,right.left							)
		return right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	lowerCamelCase__								=		Node(__snake_case							)
	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,__snake_case							)
	return merge(merge(__snake_case       ,__snake_case							)       ,__snake_case							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,value - 1							)
	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,__snake_case							)
	return merge(__snake_case       ,__snake_case							)





def lowerCAmelCase__(__snake_case							)				->      None:







	'''simple docstring'''







	if not root:  # None
		return
	else:
		inorder(root.left							)
		print(root.value       ,end=''','''							)
		inorder(root.right							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	for arg in args.split():
		if arg[0] == "+":
			lowerCamelCase__								=		insert(__snake_case       ,int(arg[1:]							)							)

		elif arg[0] == "-":
			lowerCamelCase__								=		erase(__snake_case       ,int(arg[1:]							)							)

		else:
			print('''Unknown command'''							)

	return root





def lowerCAmelCase__()				->      None:







	'''simple docstring'''







	lowerCamelCase__								=		None
	print(
	    '''enter numbers to create a tree, + value to add value into treap, '''
	    '''- value to erase all nodes with value. \'q\' to quit. '''							)

	lowerCamelCase__								=		input()
	while args != "q":
		lowerCamelCase__								=		interact_treap(__snake_case       ,__snake_case							)
		print(__snake_case							)
		lowerCamelCase__								=		input()

	print('''good by!'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



