


# 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 						_UpperCAmelCase      :
		lowerCamelCase_	:			CommonSchedulerState

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






		@classmethod
		def _snake_case		(						cls				:     List[str]   ,  UpperCAmelCase				:     CommonSchedulerState   ,  UpperCAmelCase				:     jnp.ndarray   ,  UpperCAmelCase				:     jnp.ndarray):
				return cls(common=UpperCAmelCase   ,  init_noise_sigma=UpperCAmelCase   ,  timesteps=UpperCAmelCase)




@dataclass
class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			DDPMSchedulerState




class 						_UpperCAmelCase      (     lowercase		,		lowercase							):
		lowerCamelCase_	:			Any       			=  [e.name for e in FlaxKarrasDiffusionSchedulers]

		lowerCamelCase_	:			jnp.dtype






		@property
		def _snake_case		(						self				:     Optional[Any]):
				return True






		@register_to_config
		def __init__(						self				:     Dict   ,  UpperCAmelCase				:     int = 10_00   ,  UpperCAmelCase				:     float = 0.0001   ,  UpperCAmelCase				:     float = 0.02   ,  UpperCAmelCase				:     str = "linear"   ,  UpperCAmelCase				:     Optional[jnp.ndarray] = None   ,  UpperCAmelCase				:     str = "fixed_small"   ,  UpperCAmelCase				:     bool = True   ,  UpperCAmelCase				:     str = "epsilon"   ,  UpperCAmelCase				:     jnp.dtype = jnp.floataa   ,  ):
				SCREAMING_SNAKE_CASE_				:Any				     =						dtype






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Optional[CommonSchedulerState] = None):
				if common is None:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						CommonSchedulerState.create(self)

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

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

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






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     DDPMSchedulerState   ,  UpperCAmelCase				:     jnp.ndarray   ,  UpperCAmelCase				:     Optional[int] = None):
				return sample






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     DDPMSchedulerState   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     Tuple = ()):
				SCREAMING_SNAKE_CASE_				:Any				     =						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_				:Optional[Any]				     =						(jnp.arange(0   ,  UpperCAmelCase) * step_ratio).round()[::-1]

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






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     DDPMSchedulerState   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     str=None   ,  UpperCAmelCase				:     List[str]=None):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						state.common.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE_				:Tuple				     =						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_				:Optional[Any]				     =						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

				if variance_type is None:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						self.config.variance_type

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

				return variance






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     DDPMSchedulerState   ,  UpperCAmelCase				:     jnp.ndarray   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     jnp.ndarray   ,  UpperCAmelCase				:     Optional[jax.random.KeyArray] = None   ,  UpperCAmelCase				:     bool = True   ,  ):
				SCREAMING_SNAKE_CASE_				:int				     =						timestep

				if key is None:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						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_				:Any				     =						jnp.split(UpperCAmelCase   ,  sample.shape[1]   ,  axis=1)
				else:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						None

				# 1. compute alphas, betas
				SCREAMING_SNAKE_CASE_				:Any				     =						state.common.alphas_cumprod[t]
				SCREAMING_SNAKE_CASE_				:Dict				     =						jnp.where(t > 0   ,  state.common.alphas_cumprod[t - 1]   ,  jnp.array(1.0   ,  dtype=self.dtype))
				SCREAMING_SNAKE_CASE_				:List[str]				     =						1 - alpha_prod_t
				SCREAMING_SNAKE_CASE_				:Tuple				     =						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_				:Union[str, Any]				     =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
						SCREAMING_SNAKE_CASE_				:int				     =						model_output
				elif self.config.prediction_type == "v_prediction":
						SCREAMING_SNAKE_CASE_				:Any				     =						(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_				:Optional[int]				     =						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
				SCREAMING_SNAKE_CASE_				:Any				     =						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
				SCREAMING_SNAKE_CASE_				:List[str]				     =						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_				:List[str]				     =						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				def random_variance():
						SCREAMING_SNAKE_CASE_				:int				     =						jax.random.split(UpperCAmelCase   ,  num=1)
						SCREAMING_SNAKE_CASE_				:List[str]				     =						jax.random.normal(UpperCAmelCase   ,  shape=model_output.shape   ,  dtype=self.dtype)
						return (self._get_variance(UpperCAmelCase   ,  UpperCAmelCase   ,  predicted_variance=UpperCAmelCase) ** 0.5) * noise

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						jnp.where(t > 0   ,  random_variance()   ,  jnp.zeros(model_output.shape   ,  dtype=self.dtype))

				SCREAMING_SNAKE_CASE_				:Any				     =						pred_prev_sample + variance

				if not return_dict:
						return (pred_prev_sample, state)

				return FlaxDDPMSchedulerOutput(prev_sample=UpperCAmelCase   ,  state=UpperCAmelCase)






		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     DDPMSchedulerState   ,  UpperCAmelCase				:     jnp.ndarray   ,  UpperCAmelCase				:     jnp.ndarray   ,  UpperCAmelCase				:     jnp.ndarray   ,  ):
				return add_noise_common(state.common   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     DDPMSchedulerState   ,  UpperCAmelCase				:     jnp.ndarray   ,  UpperCAmelCase				:     jnp.ndarray   ,  UpperCAmelCase				:     jnp.ndarray   ,  ):
				return get_velocity_common(state.common   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)







		def __len__(						self				:     Optional[int]):
				return self.config.num_train_timesteps










import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE__     					=			False



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Dict=32):
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Dict				     =						UNetaDModel(sample_size=UpperCAmelCase   ,  in_channels=3   ,  out_channels=3)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.optim.SGD(model.parameters()   ,  lr=0.0001)
				return model, optimizer







		@slow
		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				SCREAMING_SNAKE_CASE_				:Any				     =						DDPMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:str				     =						DDIMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Any				     =						[torch.randn((4, 3, 32, 32)).clip(-1   ,  1).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:str				     =						[torch.randn((4, 3, 32, 32)).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[torch.randint(0   ,  10_00   ,  (4,)).long().to(UpperCAmelCase) for _ in range(4)]

				# train with a DDPM scheduler
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						ddpm_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.nn.functional.mse_loss(UpperCAmelCase   ,  noise[i])
						loss.backward()
						optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[str]				     =						ddim_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.nn.functional.mse_loss(UpperCAmelCase   ,  noise[i])
						loss.backward()
						optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase   ,  UpperCAmelCase   ,  atol=1E-5))
				self.assertTrue(torch.allclose(UpperCAmelCase   ,  UpperCAmelCase   ,  atol=1E-5))






