# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
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    lowercase_    :
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42

							# setable values
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		None



							@classmethod
							def        UpperCamelCase       (						cls      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													return cls(
													    common=lowercase_      ,				final_alpha_cumprod=lowercase_      ,				init_noise_sigma=lowercase_      ,				timesteps=lowercase_      ,				)




@dataclass
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		42




class    lowercase_    (     __snake_case						, __snake_case		):
							_lowerCamelCase						=		[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowerCamelCase						=		42



							@property
							def        UpperCamelCase       (						self				):
													return True



							@register_to_config
							def __init__(						self      ,				lowercase_ = 1_000      ,				lowercase_ = 0.0_001      ,				lowercase_ = 0.02      ,				lowercase_ = "linear"      ,				lowercase_ = None      ,				lowercase_ = True      ,				lowercase_ = 0      ,				lowercase_ = "epsilon"      ,				lowercase_ = jnp.floataa      ,				):
													_snake_case						:     List[str]				=    dtype



							def        UpperCamelCase       (						self      ,				lowercase_ = None				):
													if common is None:
																			_snake_case						:     Any				=    CommonSchedulerState.create(self				)

													# At every step in ddim, we are looking into the previous alphas_cumprod
													# For the final step, there is no previous alphas_cumprod because we are already at 0
													# `set_alpha_to_one` decides whether we set this parameter simply to one or
													# whether we use the final alpha of the "non-previous" one.
													_snake_case						:     Union[str, Any]				=    (
													    jnp.array(1.0      ,				dtype=self.dtype				) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
													)

													# standard deviation of the initial noise distribution
													_snake_case						:     Optional[Any]				=    jnp.array(1.0      ,				dtype=self.dtype				)

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

													return DDIMSchedulerState.create(
													    common=lowercase_      ,				final_alpha_cumprod=lowercase_      ,				init_noise_sigma=lowercase_      ,				timesteps=lowercase_      ,				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_ = None				):
													return sample



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_ = ()				):
													_snake_case						:     int				=    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
													_snake_case						:     Optional[Any]				=    (jnp.arange(0      ,				lowercase_				) * step_ratio).round()[::-1] + self.config.steps_offset

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



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													_snake_case						:     str				=    state.common.alphas_cumprod[timestep]
													_snake_case						:     int				=    jnp.where(
													    prev_timestep >= 0      ,				state.common.alphas_cumprod[prev_timestep]      ,				state.final_alpha_cumprod				)
													_snake_case						:     Optional[int]				=    1 - alpha_prod_t
													_snake_case						:     List[str]				=    1 - alpha_prod_t_prev

													_snake_case						:     List[Any]				=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

													return variance



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_ = 0.0      ,				lowercase_ = True      ,				):
													if state.num_inference_steps is None:
																			raise ValueError(
																			    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"				)

													# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
													# Ideally, read DDIM paper in-detail understanding

													# Notation (<variable name> -> <name in paper>
													# - pred_noise_t -> e_theta(x_t, t)
													# - pred_original_sample -> f_theta(x_t, t) or x_0
													# - std_dev_t -> sigma_t
													# - eta -> η
													# - pred_sample_direction -> "direction pointing to x_t"
													# - pred_prev_sample -> "x_t-1"

													# 1. get previous step value (=t-1)
													_snake_case						:     Union[str, Any]				=    timestep - self.config.num_train_timesteps // state.num_inference_steps

													_snake_case						:     Tuple				=    state.common.alphas_cumprod
													_snake_case						:     Optional[Any]				=    state.final_alpha_cumprod

													# 2. compute alphas, betas
													_snake_case						:     Dict				=    alphas_cumprod[timestep]
													_snake_case						:     List[Any]				=    jnp.where(prev_timestep >= 0      ,				alphas_cumprod[prev_timestep]      ,				lowercase_				)

													_snake_case						:     int				=    1 - alpha_prod_t

													# 3. compute predicted original sample from predicted noise also called
													# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													if self.config.prediction_type == "epsilon":
																			_snake_case						:     Tuple				=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																			_snake_case						:     Optional[int]				=    model_output
													elif self.config.prediction_type == "sample":
																			_snake_case						:     Optional[Any]				=    model_output
																			_snake_case						:     List[str]				=    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
													elif self.config.prediction_type == "v_prediction":
																			_snake_case						:     Dict				=    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																			_snake_case						:     Optional[Any]				=    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
																			    " `v_prediction`"				)

													# 4. compute variance: "sigma_t(η)" -> see formula (16)
													# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
													_snake_case						:     int				=    self._get_variance(lowercase_      ,				lowercase_      ,				lowercase_				)
													_snake_case						:     Any				=    eta * variance ** 0.5

													# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													_snake_case						:     Union[str, Any]				=    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

													# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													_snake_case						:     Optional[int]				=    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

													if not return_dict:
																			return (prev_sample, state)

													return FlaxDDIMSchedulerOutput(prev_sample=lowercase_      ,				state=lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													return add_noise_common(state.common      ,				lowercase_      ,				lowercase_      ,				lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													return get_velocity_common(state.common      ,				lowercase_      ,				lowercase_      ,				lowercase_				)



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





from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

           if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__	)
                 lowerCamelCase__					     = image.to(self.device	)
           else:
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__		,     device=self.device	)

           # set step values
           self.scheduler.set_timesteps(A__	)

           for t in self.progress_bar(self.scheduler.timesteps	):
                 # 1. predict noise model_output
                 lowerCamelCase__					     = self.unet(A__		,     A__	).sample

                 # 2. compute previous image: x_t -> x_t-1
                 lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__		,     generator=A__	).prev_sample

           lowerCamelCase__					     = (image / 2 + 0.5).clamp(0		,     1	)
           lowerCamelCase__					     = image.cpu().permute(0		,     2		,     3		,     1	).numpy()
           if output_type == "pil":
                 lowerCamelCase__					     = self.numpy_to_pil(A__	)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)