
"""simple docstring"""
# 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    :
							lowercase_  :  Tuple      =				42

							# setable values
							lowercase_  :  Union[str, Any]      =				42
							lowercase_  :  str      =				42
							lowercase_  :  str      =				None



							@classmethod
							def    _a (   cls			,    a_			,    a_			,    a_ )				->				int:
								return cls(common=a_			,    init_noise_sigma=a_			,    timesteps=a_ )





@dataclass
class  _lowerCAmelCase    (    SCREAMING_SNAKE_CASE__    ):
							lowercase_  :  str      =				42





class  _lowerCAmelCase    (    SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__    ):
							lowercase_  :  Any      =				[e.name for e in FlaxKarrasDiffusionSchedulers]

							lowercase_  :  Union[str, Any]      =				42



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



							@register_to_config
							def __init__(   self			,    a_ = 1000			,    a_ = 0.0001			,    a_ = 0.02			,    a_ = "linear"			,    a_ = None			,    a_ = "fixed_small"			,    a_ = True			,    a_ = "epsilon"			,    a_ = jnp.floataa			,    )				->				Optional[Any]:
								_UpperCAmelCase			=    dtype



							def    _a (   self			,    a_ = None )				->				DDPMSchedulerState:



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

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



							def    _a (   self			,    a_			,    a_			,    a_ = None )				->				jnp.ndarray:
								return sample



							def    _a (   self			,    a_			,    a_			,    a_ = () )				->				DDPMSchedulerState:
								_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]

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



							def    _a (   self			,    a_			,    a_			,    a_=None			,    a_=None )				->				Any:
								_UpperCAmelCase			=    state.common.alphas_cumprod[t]
								_UpperCAmelCase			=    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
								_UpperCAmelCase			=    (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

								return variance



							def    _a (   self			,    a_			,    a_			,    a_			,    a_			,    a_ = None			,    a_ = True			,    )				->				Union[FlaxDDPMSchedulerOutput, Tuple]:
								_UpperCAmelCase			=    timestep

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

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

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

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

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

								_UpperCAmelCase			=    pred_prev_sample + variance

								if not return_dict:
									return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=a_			,    state=a_ )



							def    _a (   self			,    a_			,    a_			,    a_			,    a_			,    )				->				jnp.ndarray:
								return add_noise_common(state.common			,    a_			,    a_			,    a_ )



							def    _a (   self			,    a_			,    a_			,    a_			,    a_			,    )				->				jnp.ndarray:
								return get_velocity_common(state.common			,    a_			,    a_			,    a_ )



							def __len__(   self )				->				List[str]:
								return self.config.num_train_timesteps










from __future__ import annotations

from typing import Any

def lowercase    (  SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    []       ,    0 )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			if index == len(SCREAMING_SNAKE_CASE ):
						print(SCREAMING_SNAKE_CASE )
						return

			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.append(sequence[index] )
			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.pop()


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     list[Any]		    =       [3, 1, 2, 4]
				generate_all_subsequences(seq)

				seq.clear()
				seq.extend(["A", "B", "C"])
				generate_all_subsequences(seq)
