"""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		:






					'''simple docstring'''


					a   	=		42

					# setable values
					a   	=		42
					a   	=		42
					a   	=		None



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				str   , _snake_case			:				CommonSchedulerState   , _snake_case			:				jnp.ndarray   , _snake_case			:				jnp.ndarray		)							->       Optional[int]:
									return cls(common=_snake_case   , init_noise_sigma=_snake_case   , timesteps=_snake_case		)



@dataclass
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		[e.name for e in FlaxKarrasDiffusionSchedulers]

					a   	=		42



					@property
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Union[str, Any]:
									return True



					@register_to_config
					def __init__(						self			:				Tuple   , _snake_case			:				int = 1000   , _snake_case			:				float = 0.0001   , _snake_case			:				float = 0.02   , _snake_case			:				str = "linear"   , _snake_case			:				Optional[jnp.ndarray] = None   , _snake_case			:				str = "fixed_small"   , _snake_case			:				bool = True   , _snake_case			:				str = "epsilon"   , _snake_case			:				jnp.dtype = jnp.floataa   , )							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				dtype



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[CommonSchedulerState] = None		)							->       DDPMSchedulerState:
									if common is None:
													SCREAMING_SNAKE_CASE__		  =				CommonSchedulerState.create(self		)

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

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

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



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				DDPMSchedulerState   , _snake_case			:				jnp.ndarray   , _snake_case			:				Optional[int] = None		)							->       jnp.ndarray:
									return sample



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				DDPMSchedulerState   , _snake_case			:				int   , _snake_case			:				Tuple = ()		)							->       DDPMSchedulerState:
									SCREAMING_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
									SCREAMING_SNAKE_CASE__		  =				(jnp.arange(0   , _snake_case		) * step_ratio).round()[::-1]

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



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				DDPMSchedulerState   , _snake_case			:				Dict   , _snake_case			:				List[Any]=None   , _snake_case			:				int=None		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				state.common.alphas_cumprod[t]
									SCREAMING_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
									SCREAMING_SNAKE_CASE__		  =				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

									return variance



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				DDPMSchedulerState   , _snake_case			:				jnp.ndarray   , _snake_case			:				int   , _snake_case			:				jnp.ndarray   , _snake_case			:				Optional[jax.random.KeyArray] = None   , _snake_case			:				bool = True   , )							->       Union[FlaxDDPMSchedulerOutput, Tuple]:
									SCREAMING_SNAKE_CASE__		  =				timestep

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

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

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

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

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

									SCREAMING_SNAKE_CASE__		  =				pred_prev_sample + variance

									if not return_dict:
													return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=_snake_case   , state=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				DDPMSchedulerState   , _snake_case			:				jnp.ndarray   , _snake_case			:				jnp.ndarray   , _snake_case			:				jnp.ndarray   , )							->       jnp.ndarray:
									return add_noise_common(state.common   , _snake_case   , _snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				DDPMSchedulerState   , _snake_case			:				jnp.ndarray   , _snake_case			:				jnp.ndarray   , _snake_case			:				jnp.ndarray   , )							->       jnp.ndarray:
									return get_velocity_common(state.common   , _snake_case   , _snake_case   , _snake_case		)



					def __len__(						self			:				Union[str, Any]		)							->       Optional[int]:
									return self.config.num_train_timesteps





"""simple docstring"""






from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A   					=    {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_A   					=    [
						    'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'VanForImageClassification',
						    'VanModel',
						    'VanPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						_A   					=    _LazyModule(__name__, globals()['__file__'], _import_structure)




