





# 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					:
							snake_case     :      CommonSchedulerState

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






							@classmethod
							def 				_lowerCamelCase		(						cls  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														return cls(common=__lowerCAmelCase  , init_noise_sigma=__lowerCAmelCase  , timesteps=__lowerCAmelCase  )






@dataclass
class        __SCREAMING_SNAKE_CASE					(     _a       ):
							snake_case     :      DDPMSchedulerState






class        __SCREAMING_SNAKE_CASE					(     _a   ,		_a       ):
							snake_case     :      Any						     =       [e.name for e in FlaxKarrasDiffusionSchedulers]

							snake_case     :      jnp.dtype






							@property
							def 				_lowerCamelCase		(						self  ):
														return True






							@register_to_config
							def __init__(						self  , __lowerCAmelCase = 1000  , __lowerCAmelCase = 0.0001  , __lowerCAmelCase = 0.02  , __lowerCAmelCase = "linear"  , __lowerCAmelCase = None  , __lowerCAmelCase = "fixed_small"  , __lowerCAmelCase = True  , __lowerCAmelCase = "epsilon"  , __lowerCAmelCase = jnp.floataa  , ):
														UpperCamelCase__				     =			dtype






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase = None  ):
														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=__lowerCAmelCase  , init_noise_sigma=__lowerCAmelCase  , timesteps=__lowerCAmelCase  , )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = None  ):
														return sample






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = ()  ):
														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  , __lowerCAmelCase  ) * step_ratio).round()[::-1]

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






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






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , ):
														return add_noise_common(state.common  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , ):
														return get_velocity_common(state.common  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )







							def __len__(						self  ):
														return self.config.num_train_timesteps











import inspect
import unittest

class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):






							def 				_lowerCamelCase		(						self  ):
														try:
																					import diffusers  # noqa: F401
														except ImportError:
																					assert False







							def 				_lowerCamelCase		(						self  ):
														import diffusers
														from diffusers.dependency_versions_table import deps

														UpperCamelCase__				     =			inspect.getmembers(__lowerCAmelCase  , inspect.isclass  )

														for cls_name, cls_module in all_classes:
																					if "dummy_" in cls_module.__module__:
																												for backend in cls_module._backends:
																																			if backend == "k_diffusion":
																																										UpperCamelCase__				     =			"""k-diffusion"""
																																			elif backend == "invisible_watermark":
																																										UpperCamelCase__				     =			"""invisible-watermark"""
																																			assert backend in deps, f"""{backend} is not in the deps table!"""




