


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     UpperCamelCase(  _a   ):
					snake_case_		:				BigBirdConfig
					snake_case_		:				jnp.dtype										=	jnp.floataa
					snake_case_		:				bool										=	True


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Any:




											'''simple docstring'''

											super().setup()
											__snake_case				=		nn.Dense(5     ,    dtype=self.dtype						)






					def __call__(						self	:		Optional[int]     ,    *SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		List[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		super().__call__(*SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.cls(outputs[2]						)
											return outputs[:2] + (cls_out,)






class     UpperCamelCase(  _a   ):
					snake_case_		:				Optional[Any]										=	FlaxBigBirdForNaturalQuestionsModule





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






						def cross_entropy(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=None ):
												__snake_case				=		logits.shape[-1]
												__snake_case				=		(labels[..., None] == jnp.arange(_lowerCAmelCase )[None]).astype("f4" )
												__snake_case				=		jax.nn.log_softmax(_lowerCAmelCase      ,	axis=-1 )
												__snake_case				=		-jnp.sum(labels * logits      ,	axis=-1 )
												if reduction is not None:
																		__snake_case				=		reduction(_lowerCAmelCase )
												return loss

						__snake_case				=		partial(_lowerCAmelCase      ,	reduction=jnp.mean )
						__snake_case				=		cross_entropy(_lowerCAmelCase      ,	_lowerCAmelCase )
						__snake_case				=		cross_entropy(_lowerCAmelCase      ,	_lowerCAmelCase )
						__snake_case				=		cross_entropy(_lowerCAmelCase      ,	_lowerCAmelCase )
						return (start_loss + end_loss + pooled_loss) / 3






@dataclass
class     UpperCamelCase:
					snake_case_		:				str										=	"google/bigbird-roberta-base"
					snake_case_		:				int										=	3000
					snake_case_		:				int										=	1_0500

					snake_case_		:				int										=	128
					snake_case_		:				int										=	3

					snake_case_		:				int										=	1
					snake_case_		:				int										=	5

					# tx_args
					snake_case_		:				float										=	3E-5
					snake_case_		:				float										=	0.0
					snake_case_		:				int										=	2_0000
					snake_case_		:				float										=	0.0095

					snake_case_		:				str										=	"bigbird-roberta-natural-questions"
					snake_case_		:				str										=	"training-expt"
					snake_case_		:				str										=	"data/nq-training.jsonl"
					snake_case_		:				str										=	"data/nq-validation.jsonl"


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




											'''simple docstring'''

											os.makedirs(self.base_dir     ,    exist_ok=SCREAMING_SNAKE_CASE						)
											__snake_case				=		os.path.join(self.base_dir     ,    self.save_dir						)
											__snake_case				=		self.batch_size_per_device * jax.device_count()






@dataclass
class     UpperCamelCase:
					snake_case_		:				int
					snake_case_		:				int										=	4096  # no dynamic padding on TPUs


					def __call__(						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.collate_fn(SCREAMING_SNAKE_CASE						)
											__snake_case				=		jax.tree_util.tree_map(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											return batch


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.fetch_inputs(features["input_ids"]						)
											__snake_case				=		{
											    "input_ids": jnp.array(SCREAMING_SNAKE_CASE     ,    dtype=jnp.intaa						),
											    "attention_mask": jnp.array(SCREAMING_SNAKE_CASE     ,    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 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		list						)			->	str:




											'''simple docstring'''

											__snake_case				=		[self._fetch_inputs(SCREAMING_SNAKE_CASE						) for ids in input_ids]
											return zip(*SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		list						)			->	Optional[Any]:




											'''simple docstring'''

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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase=None )						->	List[Any]:


						'''simple docstring'''






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





@partial(jax.pmap      ,	axis_name="batch" )
def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	**_lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






						def loss_fn(_lowerCAmelCase ):
												__snake_case				=		model_inputs.pop("start_labels" )
												__snake_case				=		model_inputs.pop("end_labels" )
												__snake_case				=		model_inputs.pop("pooled_labels" )

												__snake_case				=		state.apply_fn(**_lowerCAmelCase      ,	params=_lowerCAmelCase      ,	dropout_rng=_lowerCAmelCase      ,	train=_lowerCAmelCase )
												__snake_case		,					__snake_case		,					__snake_case				=		outputs

												return state.loss_fn(
												    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	)

						__snake_case		,					__snake_case				=		jax.random.split(_lowerCAmelCase )
						__snake_case				=		jax.value_and_grad(_lowerCAmelCase )
						__snake_case		,					__snake_case				=		grad_fn(state.params )
						__snake_case				=		jax.lax.pmean({"loss": loss}      ,	axis_name="batch" )
						__snake_case				=		jax.lax.pmean(_lowerCAmelCase      ,	"batch" )

						__snake_case				=		state.apply_gradients(grads=_lowerCAmelCase )
						return state, metrics, new_drp_rng





@partial(jax.pmap      ,	axis_name="batch" )
def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	**_lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






						__snake_case				=		model_inputs.pop("start_labels" )
						__snake_case				=		model_inputs.pop("end_labels" )
						__snake_case				=		model_inputs.pop("pooled_labels" )

						__snake_case				=		state.apply_fn(**_lowerCAmelCase      ,	params=state.params      ,	train=_lowerCAmelCase )
						__snake_case		,					__snake_case		,					__snake_case				=		outputs

						__snake_case				=		state.loss_fn(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )
						__snake_case				=		jax.lax.pmean({"loss": loss}      ,	axis_name="batch" )
						return metrics






class     UpperCamelCase(  train_state.TrainState   ):
					snake_case_		:				Callable										=	struct.field(pytree_node=_a   )






@dataclass
class     UpperCamelCase:
					snake_case_		:				Args
					snake_case_		:				Callable
					snake_case_		:				Callable
					snake_case_		:				Callable
					snake_case_		:				Callable
					snake_case_		:				wandb
					snake_case_		:				Callable										=	None


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple=None						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		model.params
											__snake_case				=		TrainState.create(
											    apply_fn=model.__call__     ,    params=SCREAMING_SNAKE_CASE     ,    tx=SCREAMING_SNAKE_CASE     ,    loss_fn=SCREAMING_SNAKE_CASE     ,    )
											if ckpt_dir is not None:
																	__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case				=		restore_checkpoint(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																	__snake_case				=		{
																	    "lr": args.lr,
																	    "init_lr": args.init_lr,
																	    "warmup_steps": args.warmup_steps,
																	    "num_train_steps": num_train_steps,
																	    "weight_decay": args.weight_decay,
																	}
																	__snake_case		,					__snake_case				=		build_tx(**SCREAMING_SNAKE_CASE						)
																	__snake_case				=		train_state.TrainState(
																	    step=SCREAMING_SNAKE_CASE     ,    apply_fn=model.__call__     ,    params=SCREAMING_SNAKE_CASE     ,    tx=SCREAMING_SNAKE_CASE     ,    opt_state=SCREAMING_SNAKE_CASE     ,    )
																	__snake_case				=		args
																	__snake_case				=		data_collator
																	__snake_case				=		lr
																	__snake_case				=		params
											__snake_case				=		jax_utils.replicate(SCREAMING_SNAKE_CASE						)
											return state


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		int						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.args
											__snake_case				=		len(SCREAMING_SNAKE_CASE						) // args.batch_size

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

																													__snake_case				=		self.evaluate(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																													__snake_case				=		{
																													    "step": state_step.item(),
																													    "eval_loss": eval_loss.item(),
																													    "tr_loss": tr_loss,
																													    "lr": lr.item(),
																													}
																													tqdm.write(str(SCREAMING_SNAKE_CASE						)						)
																													self.logger.log(SCREAMING_SNAKE_CASE     ,    commit=SCREAMING_SNAKE_CASE						)

																							if i % args.save_steps == 0:
																													self.save_checkpoint(args.save_dir + f'''-e{epoch}-s{i}'''     ,    state=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	Any:




											'''simple docstring'''

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	Any:




											'''simple docstring'''

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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase )						->	Any:


						'''simple docstring'''






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

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

						__snake_case				=		joblib.load(os.path.join(_lowerCAmelCase      ,	"args.joblib" ) )
						__snake_case				=		joblib.load(os.path.join(_lowerCAmelCase      ,	"data_collator.joblib" ) )

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

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





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	str:


						'''simple docstring'''






						__snake_case				=		num_train_steps - warmup_steps
						__snake_case				=		optax.linear_schedule(init_value=_lowerCAmelCase      ,	end_value=_lowerCAmelCase      ,	transition_steps=_lowerCAmelCase )
						__snake_case				=		optax.linear_schedule(init_value=_lowerCAmelCase      ,	end_value=1E-7      ,	transition_steps=_lowerCAmelCase )
						__snake_case				=		optax.join_schedules(schedules=[warmup_fn, decay_fn]      ,	boundaries=[warmup_steps] )
						return lr





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






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

						__snake_case				=		scheduler_fn(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )

						__snake_case				=		optax.adamw(learning_rate=_lowerCAmelCase      ,	weight_decay=_lowerCAmelCase      ,	mask=_lowerCAmelCase )
						return tx, lr









import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class     UpperCamelCase:


					def __init__(						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	str:




											'''simple docstring'''

											if isinstance(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						):
																	# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																	# modified it, it will not be accepted here again, since `auto` values would have been overridden
																	__snake_case				=		deepcopy(SCREAMING_SNAKE_CASE						)
											elif os.path.exists(SCREAMING_SNAKE_CASE						):
																	with io.open(SCREAMING_SNAKE_CASE     ,    "r"     ,    encoding="utf-8"						) as f:
																							__snake_case				=		json.load(SCREAMING_SNAKE_CASE						)
											else:
																	try:
																							__snake_case				=		baseaa.urlsafe_baadecode(SCREAMING_SNAKE_CASE						).decode("utf-8"						)
																							__snake_case				=		json.loads(SCREAMING_SNAKE_CASE						)
																	except (UnicodeDecodeError, AttributeError, ValueError):
																							raise ValueError(
																							    f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''						)

											__snake_case				=		config

											self.set_stage_and_offload()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.get_value("zero_optimization.stage"     ,    -1						)

											# offload
											__snake_case				=		False
											if self.is_zeroa() or self.is_zeroa():
																	__snake_case				=		set(["cpu", "nvme"]						)
																	__snake_case				=		set(
																	    [
																	        self.get_value("zero_optimization.offload_optimizer.device"						),
																	        self.get_value("zero_optimization.offload_param.device"						),
																	    ]						)
																	if len(offload_devices & offload_devices_valid						) > 0:
																							__snake_case				=		True


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.config

											# find the config node of interest if it exists
											__snake_case				=		ds_key_long.split("."						)
											__snake_case				=		nodes.pop()
											for node in nodes:
																	__snake_case				=		config.get(SCREAMING_SNAKE_CASE						)
																	if config is None:
																							return None, ds_key

											return config, ds_key


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Tuple=None						)			->	List[str]:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.find_config_node(SCREAMING_SNAKE_CASE						)
											if config is None:
																	return default
											return config.get(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Tuple=False						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.config

											# find the config node of interest if it exists
											__snake_case				=		ds_key_long.split("."						)
											for node in nodes:
																	__snake_case				=		config
																	__snake_case				=		config.get(SCREAMING_SNAKE_CASE						)
																	if config is None:
																							if must_exist:
																													raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''						)
																							else:
																													return

        # if found remove it
											if parent_config is not None:
																	parent_config.pop(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.get_value(SCREAMING_SNAKE_CASE						)
											return False if value is None else bool(SCREAMING_SNAKE_CASE						)


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




											'''simple docstring'''

											__snake_case				=		self.get_value(SCREAMING_SNAKE_CASE						)
											return False if value is None else not bool(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											return self._stage == 2


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											return self._stage == 3






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Optional[Any]:




											'''simple docstring'''

											return self._offload






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		engine






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		str						)			->	List[str]:




											'''simple docstring'''

											self.engine.backward(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											# Deepspeed's `engine.step` performs the following operations:
											# - gradient accumulation check
											# - gradient clipping
											# - optimizer step
											# - zero grad
											# - checking overflow
											# - lr_scheduler step (only if engine.lr_scheduler is not None)
											self.engine.step()
											# and this plugin overrides the above calls with no-ops when Accelerate runs under
											# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
											# training loop that works transparently under many training regimes.






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	str:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE     ,    device_placement=SCREAMING_SNAKE_CASE     ,    scaler=SCREAMING_SNAKE_CASE						)
											__snake_case				=		hasattr(self.optimizer     ,    "overflow"						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]=None						)			->	str:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Any:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Any:




											'''simple docstring'''

											if self.__has_overflow__:
																	return self.optimizer.overflow
											return False






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Union[str, Any]:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Any:




											'''simple docstring'''

											pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.001     ,    SCREAMING_SNAKE_CASE	:		Tuple=0     ,    **SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		params
											__snake_case				=		lr
											__snake_case				=		weight_decay
											__snake_case				=		kwargs






class     UpperCamelCase:


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict=None     ,    SCREAMING_SNAKE_CASE	:		Tuple=0     ,    **SCREAMING_SNAKE_CASE	:		Tuple						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		optimizer
											__snake_case				=		total_num_steps
											__snake_case				=		warmup_num_steps
											__snake_case				=		kwargs





