






# 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__       :
	a_             =42
	a_             =42

	# setable values
	a_             =42
	a_             =42
	a_             =None

	@classmethod
	def 						UpperCAmelCase					(							cls						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      )->   List[Any]:







							'''simple docstring'''





							return cls(
							    common=__UpperCAmelCase						,      final_alpha_cumprod=__UpperCAmelCase						,      init_noise_sigma=__UpperCAmelCase						,      timesteps=__UpperCAmelCase						,      )





@dataclass
class        lowercase__       (     _UpperCAmelCase		):
	a_             =42





class        lowercase__       (     _UpperCAmelCase,   _UpperCAmelCase		):
	a_             =[e.name for e in FlaxKarrasDiffusionSchedulers]

	a_             =42

	@property
	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							return True

	@register_to_config
	def __init__(							self						,      __UpperCAmelCase = 1000						,      __UpperCAmelCase = 0.0_001						,      __UpperCAmelCase = 0.02						,      __UpperCAmelCase = "linear"						,      __UpperCAmelCase = None						,      __UpperCAmelCase = True						,      __UpperCAmelCase = 0						,      __UpperCAmelCase = "epsilon"						,      __UpperCAmelCase = jnp.floataa						,      )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  dtype

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase = None    )->   DDIMSchedulerState:







							'''simple docstring'''





							if common is None:
													lowerCAmelCase__	 =  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.
							lowerCAmelCase__	 =  (
							    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
							lowerCAmelCase__	 =  jnp.array(1.0						,      dtype=self.dtype    )

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

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

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase = None    )->   jnp.ndarray:







							'''simple docstring'''





							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase = ()    )->   DDIMSchedulerState:







							'''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] + self.config.steps_offset

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

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  state.common.alphas_cumprod[timestep]
							lowerCAmelCase__	 =  jnp.where(
							    prev_timestep >= 0						,      state.common.alphas_cumprod[prev_timestep]						,      state.final_alpha_cumprod    )
							lowerCAmelCase__	 =  1 - alpha_prod_t
							lowerCAmelCase__	 =  1 - alpha_prod_t_prev

							lowerCAmelCase__	 =  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase = 0.0						,      __UpperCAmelCase = True						,      )->   Union[FlaxDDIMSchedulerOutput, Tuple]:







							'''simple docstring'''





							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)
							lowerCAmelCase__	 =  timestep - self.config.num_train_timesteps // state.num_inference_steps

							lowerCAmelCase__	 =  state.common.alphas_cumprod
							lowerCAmelCase__	 =  state.final_alpha_cumprod

							# 2. compute alphas, betas
							lowerCAmelCase__	 =  alphas_cumprod[timestep]
							lowerCAmelCase__	 =  jnp.where(prev_timestep >= 0						,      alphas_cumprod[prev_timestep]						,      __UpperCAmelCase    )

							lowerCAmelCase__	 =  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":
													lowerCAmelCase__	 =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
													lowerCAmelCase__	 =  model_output
							elif self.config.prediction_type == "sample":
													lowerCAmelCase__	 =  model_output
													lowerCAmelCase__	 =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
													lowerCAmelCase__	 =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
													lowerCAmelCase__	 =  (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)
							lowerCAmelCase__	 =  self._get_variance(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )
							lowerCAmelCase__	 =  eta * variance ** 0.5

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							lowerCAmelCase__	 =  (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
							lowerCAmelCase__	 =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
													return (prev_sample, state)

							return FlaxDDIMSchedulerOutput(prev_sample=__UpperCAmelCase						,      state=__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      )->   jnp.ndarray:







							'''simple docstring'''





							return add_noise_common(state.common						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      )->   jnp.ndarray:







							'''simple docstring'''





							return get_velocity_common(state.common						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )


	def __len__(							self    )->   Tuple:







							'''simple docstring'''





							return self.config.num_train_timesteps














import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class        lowercase__       (     unittest.TestCase		):

	@property
	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  1
							lowerCAmelCase__	 =  3
							lowerCAmelCase__	 =  8
							lowerCAmelCase__	 =  8

							lowerCAmelCase__	 =  torch.rand((batch_size, num_channels, height, width)    )

							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							if isinstance(image[0]						,      PIL.Image.Image    ):
													return np.stack([np.array(__UpperCAmelCase    ) for i in image]						,      axis=0    )
							elif isinstance(__UpperCAmelCase						,      torch.Tensor    ):
													return image.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							return image

	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(
													    image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase						,      )
													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )
							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )

													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							lowerCAmelCase__	 =  image_processor.numpy_to_pil(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )
													for i, o in zip(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  np.array(__UpperCAmelCase    )
																			lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    ) if output_type == "pil" else (self.to_np(__UpperCAmelCase    ) * 255).round()
																			assert (
																			    np.abs(in_np - out_np    ).max() < 1E-6
																			), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  input_pt_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							lowerCAmelCase__	 =  input_np_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_ad    ).max() < 1E-6


	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_list    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_list    ).max() < 1E-6






