






# 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     __SCREAMING_SNAKE_CASE					:
						_SCREAMING_SNAKE_CASE	:       CommonSchedulerState

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






						@classmethod
						def       UpperCamelCase__      (	cls     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												return cls(common=_UpperCamelCase     ,						init_noise_sigma=_UpperCamelCase     ,						timesteps=_UpperCamelCase   )




@dataclass
class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       DDPMSchedulerState




class     __SCREAMING_SNAKE_CASE					(   __lowercase		,					__lowercase):
						_SCREAMING_SNAKE_CASE	:       Optional[int]   =					[e.name for e in FlaxKarrasDiffusionSchedulers]

						_SCREAMING_SNAKE_CASE	:       jnp.dtype






						@property
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return True






						@register_to_config
						def __init__(	self     ,						_UpperCamelCase = 10_00     ,						_UpperCamelCase = 0.00_01     ,						_UpperCamelCase = 0.02     ,						_UpperCamelCase = "linear"     ,						_UpperCamelCase = None     ,						_UpperCamelCase = "fixed_small"     ,						_UpperCamelCase = True     ,						_UpperCamelCase = "epsilon"     ,						_UpperCamelCase = jnp.floataa     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						dtype






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase = 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=_UpperCamelCase     ,						init_noise_sigma=_UpperCamelCase     ,						timesteps=_UpperCamelCase     ,						)






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = None   ):






												"""simple docstring"""
												return sample






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = ()   ):






												"""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     ,						_UpperCamelCase   ) * step_ratio).round()[::-1]

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






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase=None     ,						_UpperCamelCase=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(_UpperCamelCase     ,						a_min=1E-20   )
												# for rl-diffuser https://arxiv.org/abs/2205.09991
												elif variance_type == "fixed_small_log":
																		lowerCAmelCase__    						=						jnp.log(jnp.clip(_UpperCamelCase     ,						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       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = None     ,						_UpperCamelCase = 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(_UpperCamelCase     ,						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(_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
												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(_UpperCamelCase     ,						num=1   )
																		lowerCAmelCase__    						=						jax.random.normal(_UpperCamelCase     ,						shape=model_output.shape     ,						dtype=self.dtype   )
																		return (self._get_variance(_UpperCamelCase     ,						_UpperCamelCase     ,						predicted_variance=_UpperCamelCase   ) ** 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=_UpperCamelCase     ,						state=_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						):






												"""simple docstring"""
												return add_noise_common(state.common     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						):






												"""simple docstring"""
												return get_velocity_common(state.common     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )







						def __len__(	self   ):






												"""simple docstring"""
												return self.config.num_train_timesteps













from collections import deque

from .hash_table import HashTable


class     __SCREAMING_SNAKE_CASE					(   __lowercase):






						def __init__(	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												super().__init__(*_UpperCamelCase     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						deque([]   ) if self.values[key] is None else self.values[key]
												self.values[key].appendleft(_UpperCamelCase   )
												lowerCAmelCase__    						=						self.values[key]






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return (
												    sum(self.charge_factor - len(_UpperCamelCase   ) for slot in self.values   )
												    / self.size_table
												    * self.charge_factor
												)







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												if not (
												    len(self.values[key]   ) == self.charge_factor and self.values.count(_UpperCamelCase   ) == 0
												):
																		return key
												return super()._collision_resolution(_UpperCamelCase     ,						_UpperCamelCase   )





