




"""simple docstring"""





import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule



class      lowerCamelCase__				(	__magic_name__   ):
			lowercase_    =	4_2
			lowercase_    =	jnp.floataa
			lowercase_    =	True


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						super().setup()
						__UpperCAmelCase	: Dict		=					nn.Dense(5      ,  dtype=self.dtype)


			def __call__(      self      :    List[Any]      ,  *UpperCamelCase_      :    Any      ,  **UpperCamelCase_      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					super().__call__(*__a      ,  **__a)
						__UpperCAmelCase	: List[Any]		=					self.cls(outputs[2])
						return outputs[:2] + (cls_out,)






class      lowerCamelCase__				(	__magic_name__   ):
			lowercase_    =	FlaxBigBirdForNaturalQuestionsModule






def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[str]:


			"""simple docstring"""





			def cross_entropy(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase=None							):
						__UpperCAmelCase	: Optional[Any]		=					logits.shape[-1]
						__UpperCAmelCase	: Union[str, Any]		=					(labels[..., None] == jnp.arange(__snake_case							)[None]).astype("f4"							)
						__UpperCAmelCase	: Dict		=					jax.nn.log_softmax(__snake_case				,       axis=-1							)
						__UpperCAmelCase	: Dict		=					-jnp.sum(labels * logits				,       axis=-1							)
						if reduction is not None:
									__UpperCAmelCase	: Optional[int]		=					reduction(__snake_case							)
						return loss

			__UpperCAmelCase	: Any		=					partial(__snake_case				,       reduction=jnp.mean							)
			__UpperCAmelCase	: str		=					cross_entropy(__snake_case				,       __snake_case							)
			__UpperCAmelCase	: List[str]		=					cross_entropy(__snake_case				,       __snake_case							)
			__UpperCAmelCase	: Tuple		=					cross_entropy(__snake_case				,       __snake_case							)
			return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class      lowerCamelCase__				:
			lowercase_    =	"google/bigbird-roberta-base"
			lowercase_    =	3_0_0_0
			lowercase_    =	1_0_5_0_0

			lowercase_    =	1_2_8
			lowercase_    =	3

			lowercase_    =	1
			lowercase_    =	5

			# tx_args
			lowercase_    =	3E-5
			lowercase_    =	0.0
			lowercase_    =	2_0_0_0_0
			lowercase_    =	0.00_95

			lowercase_    =	"bigbird-roberta-natural-questions"
			lowercase_    =	"training-expt"
			lowercase_    =	"data/nq-training.jsonl"
			lowercase_    =	"data/nq-validation.jsonl"


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						os.makedirs(self.base_dir      ,  exist_ok=__a)
						__UpperCAmelCase	: Optional[Any]		=					os.path.join(self.base_dir      ,  self.save_dir)
						__UpperCAmelCase	: Dict		=					self.batch_size_per_device * jax.device_count()






@dataclass
class      lowerCamelCase__				:
			lowercase_    =	4_2
			lowercase_    =	4_0_9_6  # no dynamic padding on TPUs


			def __call__(      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					self.collate_fn(__a)
						__UpperCAmelCase	: Tuple		=					jax.tree_util.tree_map(__a      ,  __a)
						return batch


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					self.fetch_inputs(features["input_ids"])
						__UpperCAmelCase	: Union[str, Any]		=					{
						    "input_ids": jnp.array(__a      ,  dtype=jnp.intaa),
						    "attention_mask": jnp.array(__a      ,  dtype=jnp.intaa),
						    "start_labels": jnp.array(features["start_token"]      ,  dtype=jnp.intaa),
						    "end_labels": jnp.array(features["end_token"]      ,  dtype=jnp.intaa),
						    "pooled_labels": jnp.array(features["category"]      ,  dtype=jnp.intaa),
						}
						return batch


			def       a_     (      self      :    Dict      ,  UpperCamelCase_      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					[self._fetch_inputs(__a) for ids in input_ids]
						return zip(*__a)


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					[1 for _ in range(len(__a))]
						while len(__a) < self.max_length:
									input_ids.append(self.pad_id)
									attention_mask.append(0)
						return input_ids, attention_mask






def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase=None							)			->			Optional[Any]:


			"""simple docstring"""





			if seed is not None:
						__UpperCAmelCase	: Tuple		=					dataset.shuffle(seed=__snake_case							)
			for i in range(len(__snake_case							) // batch_size							):
						__UpperCAmelCase	: Tuple		=					dataset[i * batch_size : (i + 1) * batch_size]
						yield dict(__snake_case							)

@partial(jax.pmap				,       axis_name="batch"							)
def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       **UpperCamelCase							)			->			Union[str, Any]:


			"""simple docstring"""





			def loss_fn(UpperCamelCase							):
						__UpperCAmelCase	: str		=					model_inputs.pop("start_labels"							)
						__UpperCAmelCase	: str		=					model_inputs.pop("end_labels"							)
						__UpperCAmelCase	: List[Any]		=					model_inputs.pop("pooled_labels"							)

						__UpperCAmelCase	: List[str]		=					state.apply_fn(**__snake_case				,       params=__snake_case				,       dropout_rng=__snake_case				,       train=__snake_case							)
						__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: Dict		=					outputs

						return state.loss_fn(
						    __snake_case				,       __snake_case				,       __snake_case				,       __snake_case				,       __snake_case				,       __snake_case				,       )

			__UpperCAmelCase	,     __UpperCAmelCase	: str		=					jax.random.split(__snake_case							)
			__UpperCAmelCase	: Union[str, Any]		=					jax.value_and_grad(__snake_case							)
			__UpperCAmelCase	,     __UpperCAmelCase	: Optional[int]		=					grad_fn(state.params							)
			__UpperCAmelCase	: int		=					jax.lax.pmean({"loss": loss}				,       axis_name="batch"							)
			__UpperCAmelCase	: Optional[Any]		=					jax.lax.pmean(__snake_case				,       "batch"							)

			__UpperCAmelCase	: Any		=					state.apply_gradients(grads=__snake_case							)
			return state, metrics, new_drp_rng

@partial(jax.pmap				,       axis_name="batch"							)
def 					_UpperCamelCase    (			UpperCamelCase				,       **UpperCamelCase							)			->			Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					model_inputs.pop("start_labels"							)
			__UpperCAmelCase	: Optional[int]		=					model_inputs.pop("end_labels"							)
			__UpperCAmelCase	: Dict		=					model_inputs.pop("pooled_labels"							)

			__UpperCAmelCase	: Dict		=					state.apply_fn(**__snake_case				,       params=state.params				,       train=__snake_case							)
			__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: Union[str, Any]		=					outputs

			__UpperCAmelCase	: Optional[Any]		=					state.loss_fn(__snake_case				,       __snake_case				,       __snake_case				,       __snake_case				,       __snake_case				,       __snake_case							)
			__UpperCAmelCase	: Optional[Any]		=					jax.lax.pmean({"loss": loss}				,       axis_name="batch"							)
			return metrics



class      lowerCamelCase__				(	train_state.TrainState   ):
			lowercase_    =	struct.field(pytree_node=__magic_name__   )






@dataclass
class      lowerCamelCase__				:
			lowercase_    =	4_2
			lowercase_    =	4_2
			lowercase_    =	4_2
			lowercase_    =	4_2
			lowercase_    =	4_2
			lowercase_    =	4_2
			lowercase_    =	None


			def       a_     (      self      :    Dict      ,  UpperCamelCase_      :    List[Any]      ,  UpperCamelCase_      :    Any      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    List[Any]=None):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					model.params
						__UpperCAmelCase	: Tuple		=					TrainState.create(
						    apply_fn=model.__call__      ,  params=__a      ,  tx=__a      ,  loss_fn=__a      ,  )
						if ckpt_dir is not None:
									__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: Union[str, Any]		=					restore_checkpoint(__a      ,  __a)
									__UpperCAmelCase	: List[Any]		=					{
									    "lr": args.lr,
									    "init_lr": args.init_lr,
									    "warmup_steps": args.warmup_steps,
									    "num_train_steps": num_train_steps,
									    "weight_decay": args.weight_decay,
									}
									__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					build_tx(**__a)
									__UpperCAmelCase	: str		=					train_state.TrainState(
									    step=__a      ,  apply_fn=model.__call__      ,  params=__a      ,  tx=__a      ,  opt_state=__a      ,  )
									__UpperCAmelCase	: Tuple		=					args
									__UpperCAmelCase	: int		=					data_collator
									__UpperCAmelCase	: List[Any]		=					lr
									__UpperCAmelCase	: Optional[Any]		=					params
						__UpperCAmelCase	: Any		=					jax_utils.replicate(__a)
						return state


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    Any      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					self.args
						__UpperCAmelCase	: str		=					len(__a) // args.batch_size

						__UpperCAmelCase	: str		=					jax.random.PRNGKey(0)
						__UpperCAmelCase	: Any		=					jax.random.split(__a      ,  jax.device_count())
						for epoch in range(args.max_epochs):
									__UpperCAmelCase	: Dict		=					jnp.array(0      ,  dtype=jnp.floataa)
									__UpperCAmelCase	: Union[str, Any]		=					get_batched_dataset(__a      ,  args.batch_size      ,  seed=__a)
									__UpperCAmelCase	: Any		=					0
									for batch in tqdm(__a      ,  total=__a      ,  desc=F"Running EPOCH-{epoch}"):
												__UpperCAmelCase	: Optional[int]		=					self.data_collator(__a)
												__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: Optional[int]		=					self.train_step_fn(__a      ,  __a      ,  **__a)
												running_loss += jax_utils.unreplicate(metrics["loss"])
												i += 1
												if i % args.logging_steps == 0:
															__UpperCAmelCase	: int		=					jax_utils.unreplicate(state.step)
															__UpperCAmelCase	: Union[str, Any]		=					running_loss.item() / i
															__UpperCAmelCase	: Any		=					self.scheduler_fn(state_step - 1)

															__UpperCAmelCase	: Dict		=					self.evaluate(__a      ,  __a)
															__UpperCAmelCase	: int		=					{
															    "step": state_step.item(),
															    "eval_loss": eval_loss.item(),
															    "tr_loss": tr_loss,
															    "lr": lr.item(),
															}
															tqdm.write(str(__a))
															self.logger.log(__a      ,  commit=__a)

												if i % args.save_steps == 0:
															self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}"      ,  state=__a)


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Union[str, Any]      ,  UpperCamelCase_      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					get_batched_dataset(__a      ,  self.args.batch_size)
						__UpperCAmelCase	: Optional[Any]		=					len(__a) // self.args.batch_size
						__UpperCAmelCase	: Optional[int]		=					jnp.array(0      ,  dtype=jnp.floataa)
						__UpperCAmelCase	: Union[str, Any]		=					0
						for batch in tqdm(__a      ,  total=__a      ,  desc="Evaluating ... "):
									__UpperCAmelCase	: int		=					self.data_collator(__a)
									__UpperCAmelCase	: int		=					self.val_step_fn(__a      ,  **__a)
									running_loss += jax_utils.unreplicate(metrics["loss"])
									i += 1
						return running_loss / i


			def       a_     (      self      :    Dict      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					jax_utils.unreplicate(__a)
						print(F"SAVING CHECKPOINT IN {save_dir}"      ,  end=" ... ")
						self.model_save_fn(__a      ,  params=state.params)
						with open(os.path.join(__a      ,  "opt_state.msgpack")      ,  "wb") as f:
									f.write(to_bytes(state.opt_state))
						joblib.dump(self.args      ,  os.path.join(__a      ,  "args.joblib"))
						joblib.dump(self.data_collator      ,  os.path.join(__a      ,  "data_collator.joblib"))
						with open(os.path.join(__a      ,  "training_state.json")      ,  "w") as f:
									json.dump({"step": state.step.item()}      ,  __a)
						print("DONE")






def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			print(f"RESTORING CHECKPOINT FROM {save_dir}"				,       end=" ... "							)
			with open(os.path.join(__snake_case				,       "flax_model.msgpack"							)				,       "rb"							) as f:
						__UpperCAmelCase	: Any		=					from_bytes(state.params				,       f.read()							)

			with open(os.path.join(__snake_case				,       "opt_state.msgpack"							)				,       "rb"							) as f:
						__UpperCAmelCase	: int		=					from_bytes(state.opt_state				,       f.read()							)

			__UpperCAmelCase	: Optional[int]		=					joblib.load(os.path.join(__snake_case				,       "args.joblib"							)							)
			__UpperCAmelCase	: str		=					joblib.load(os.path.join(__snake_case				,       "data_collator.joblib"							)							)

			with open(os.path.join(__snake_case				,       "training_state.json"							)				,       "r"							) as f:
						__UpperCAmelCase	: Dict		=					json.load(__snake_case							)
			__UpperCAmelCase	: List[Any]		=					training_state["step"]

			print("DONE"							)
			return params, opt_state, step, args, data_collator

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[str]:


			"""simple docstring"""
			__UpperCAmelCase	: Tuple		=					num_train_steps - warmup_steps
			__UpperCAmelCase	: Optional[Any]		=					optax.linear_schedule(init_value=__snake_case				,       end_value=__snake_case				,       transition_steps=__snake_case							)
			__UpperCAmelCase	: str		=					optax.linear_schedule(init_value=__snake_case				,       end_value=1e-7				,       transition_steps=__snake_case							)
			__UpperCAmelCase	: Union[str, Any]		=					optax.join_schedules(schedules=[warmup_fn, decay_fn]				,       boundaries=[warmup_steps]							)
			return lr





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""





			def weight_decay_mask(UpperCamelCase							):
						__UpperCAmelCase	: Union[str, Any]		=					traverse_util.flatten_dict(__snake_case							)
						__UpperCAmelCase	: List[str]		=					{k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
						return traverse_util.unflatten_dict(__snake_case							)

			__UpperCAmelCase	: Tuple		=					scheduler_fn(__snake_case				,       __snake_case				,       __snake_case				,       __snake_case							)

			__UpperCAmelCase	: Any		=					optax.adamw(learning_rate=__snake_case				,       weight_decay=__snake_case				,       mask=__snake_case							)
			return tx, lr








"""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      a__				:
			lowercase_    =	42

			# setable values
			lowercase_    =	42
			lowercase_    =	42
			lowercase_    =	None


			@classmethod
			def       a_     (      cls      :    List[str]      ,  UpperCamelCase_      :    CommonSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray):


						"""simple docstring"""




						return cls(common=UpperCamelCase_      ,  init_noise_sigma=UpperCamelCase_      ,  timesteps=UpperCamelCase_)






@dataclass
class      a__				(	__magic_name__   ):
			lowercase_    =	42






class      a__				(	__magic_name__							,    __magic_name__   ):
			lowercase_    =	[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowercase_    =	42


			@property
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						return True


			@register_to_config
			def __init__(      self      :    str      ,  UpperCamelCase_      :    int = 1000      ,  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      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					dtype


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Optional[CommonSchedulerState] = None):


						"""simple docstring"""




						if common is None:
									__UpperCAmelCase	: Tuple		=					CommonSchedulerState.create(self)

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

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

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


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    Optional[int] = None):


						"""simple docstring"""




						return sample


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Tuple = ()):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					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	: List[str]		=					(jnp.arange(0      ,  UpperCamelCase_) * step_ratio).round()[::-1]

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


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Union[str, Any]=None      ,  UpperCamelCase_      :    Optional[int]=None):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					state.common.alphas_cumprod[t]
						__UpperCAmelCase	: Optional[int]		=					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	: Tuple		=					(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

						if variance_type is None:
									__UpperCAmelCase	: Optional[Any]		=					self.config.variance_type

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

						return variance


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    Optional[jax.random.KeyArray] = None      ,  UpperCamelCase_      :    bool = True      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					timestep

						if key is None:
									__UpperCAmelCase	: List[str]		=					jax.random.PRNGKey(0)

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

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

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

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

						__UpperCAmelCase	: Tuple		=					pred_prev_sample + variance

						if not return_dict:
									return (pred_prev_sample, state)

						return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase_      ,  state=UpperCamelCase_)


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  ):


						"""simple docstring"""




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


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  ):


						"""simple docstring"""




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


			def __len__(      self      :    int):


						"""simple docstring"""




						return self.config.num_train_timesteps


