



import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
	import torch

UpperCAmelCase				:							Dict								=logging.get_logger(__name__)



def 				_lowerCAmelCase      ():
					return importlib.util.find_spec("deepspeed") is not None


if is_accelerate_available() and is_deepspeed_available():
	from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
	# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
	# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
	from builtins import object as DeepSpeedConfig


class  _lowercase				(a_ ):
				'''simple docstring'''







				def __init__(	self       ,	snake_case__      ):

									'''simple docstring'''


									set_hf_deepspeed_config(self      )
									dep_version_check("accelerate"      )
									dep_version_check("deepspeed"      )
									super().__init__(snake_case__      )




class  _lowercase				(a_ ):
				'''simple docstring'''







				def __init__(	self       ,	snake_case__      ):

									'''simple docstring'''


									super().__init__(snake_case__      )
									UpperCamelCase_											=				None
									UpperCamelCase_											=				[]

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									if self._dtype is None:
														raise ValueError("trainer_config_process() wasn't called yet to tell dtype"      )
									return self._dtype

				def  _lowerCamelCase   (	self       ,	snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.get_value(snake_case__      )
									if val is None:
														return False
									else:
														return val == "auto"

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__=None       ,	snake_case__=True      ):

									'''simple docstring'''


									UpperCamelCase_					,      UpperCamelCase_											=				self.find_config_node(snake_case__      )
									if config is None:
														return

									if config.get(snake_case__      ) == "auto":
														UpperCamelCase_											=				hf_val
														return

									if not must_match:
														return

									UpperCamelCase_											=				config.get(snake_case__      )
									if ds_val is not None and ds_val != hf_val:
														self.mismatches.append(F"""- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"""      )

				lowercase__				  =  partialmethod(a_ ,				must_match=a_ )

				def  _lowerCamelCase   (	self       ,	snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
									self.fill_match(
									    "train_micro_batch_size_per_gpu"       ,	args.per_device_train_batch_size       ,	"per_device_train_batch_size"      )
									self.fill_match("gradient_accumulation_steps"       ,	args.gradient_accumulation_steps       ,	"gradient_accumulation_steps"      )
									self.fill_match("train_batch_size"       ,	snake_case__       ,	"train_batch_size (calculated)"      )
									self.fill_match("gradient_clipping"       ,	args.max_grad_norm       ,	"max_grad_norm"      )

									self.fill_match("optimizer.params.lr"       ,	args.learning_rate       ,	"learning_rate"      )
									self.fill_match("optimizer.params.betas"       ,	[args.adam_betaa, args.adam_betaa]       ,	"adam_beta1+adam_beta2"      )
									self.fill_match("optimizer.params.eps"       ,	args.adam_epsilon       ,	"adam_epsilon"      )
									self.fill_match("optimizer.params.weight_decay"       ,	args.weight_decay       ,	"weight_decay"      )

									self.fill_only("scheduler.params.warmup_min_lr"       ,	0      )  # not a trainer arg
									self.fill_match("scheduler.params.warmup_max_lr"       ,	args.learning_rate       ,	"learning_rate"      )
									# total_num_steps - will get set in trainer_config_finalize

									# fp16
									if args.fpaa or args.fpaa_full_eval:
														UpperCamelCase_											=				"apex" if args.fpaa_backend == "apex" else "amp"
									else:
														UpperCamelCase_											=				None

									if args.save_on_each_node:
														# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
														UpperCamelCase_											=				self.config.get("checkpoint"       ,	{}      )
														UpperCamelCase_											=				args.save_on_each_node

									# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
									# any here unless the user did the work
									self.fill_match(
									    "fp16.enabled"       ,	((args.fpaa or args.fpaa_full_eval) and fpaa_backend == "amp")       ,	"fp16|fp16_full_eval+fp16_backend(amp)"       ,	)

									# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
									# ZeRO features
									self.fill_match("amp.enabled"       ,	fpaa_backend == "apex"       ,	"fp16+fp16_backend(apex)"      )
									self.fill_match("amp.opt_level"       ,	args.fpaa_opt_level       ,	"fp16_opt_level"      )

									self.fill_match("bf16.enabled"       ,	(args.bfaa or args.bfaa_full_eval)       ,	"bf16|bf16_full_eval"      )

									# deepspeed's default mode is fp16 unless there is a config that says differently
									if self.is_true("bf16.enabled"      ):
														UpperCamelCase_											=				torch.bfloataa
									elif self.is_false("fp16.enabled"      ):
														UpperCamelCase_											=				torch.floataa
									else:
														UpperCamelCase_											=				torch.floataa

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				[
									    "zero_optimization.reduce_bucket_size",
									    "zero_optimization.stage3_prefetch_bucket_size",
									    "zero_optimization.stage3_param_persistence_threshold",
									]
									UpperCamelCase_											=				[x for x in hidden_size_based_keys if self.is_auto(snake_case__      )]

									if len(snake_case__      ) > 0:
														if hasattr(model.config       ,	"hidden_size"      ):
																			UpperCamelCase_											=				model.config.hidden_size
														elif hasattr(model.config       ,	"hidden_sizes"      ):
																			# if there are many hidden sizes pick the largest one
																			UpperCamelCase_											=				max(model.config.hidden_sizes      )
														else:
																			raise ValueError(
																			    "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
																			    "therefore it's not possible to automatically fill out the following `auto` entries "
																			    F"""in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing """
																			    "`auto` values for these keys with an integer value of your choice."      )

														self.fill_only("zero_optimization.reduce_bucket_size"       ,	hidden_size * hidden_size      )
														if self.is_zeroa():
																			# automatically assign the optimal config values based on model config
																			self.fill_only("zero_optimization.stage3_prefetch_bucket_size"       ,	0.9 * hidden_size * hidden_size      )
																			self.fill_only("zero_optimization.stage3_param_persistence_threshold"       ,	10 * hidden_size      )

        # scheduler
									self.fill_match("scheduler.params.total_num_steps"       ,	snake_case__       ,	"num_training_steps (calculated)"      )
									self.fill_match("scheduler.params.warmup_num_steps"       ,	args.get_warmup_steps(snake_case__      )       ,	"warmup_steps"      )

									if len(self.mismatches      ) > 0:
														UpperCamelCase_											=				"\n".join(self.mismatches      )
														raise ValueError(
														    "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
														    F""" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."""      )




UpperCAmelCase				:							Optional[Any]								=None



def 				_lowerCAmelCase      (_lowerCAmelCase):
					# this is a special weakref global object to allow us to get to Deepspeed config from APIs
					# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
					global _hf_deepspeed_config_weak_ref
					# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
					UpperCamelCase_											=				weakref.ref(_lowerCAmelCase)



def 				_lowerCAmelCase      ():
					# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
					global _hf_deepspeed_config_weak_ref
					UpperCamelCase_											=				None



def 				_lowerCAmelCase      ():
					if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
										return _hf_deepspeed_config_weak_ref().is_zeroa()
					else:
										return False



def 				_lowerCAmelCase      ():
					if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
										return _hf_deepspeed_config_weak_ref().config
					else:
										return None



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase):
					from accelerate.utils import DummyOptim, DummyScheduler

					UpperCamelCase_											=				hf_deepspeed_config.config

					# Optimizer + Scheduler
					# Currently supported combos:
					# 1. DS scheduler + DS optimizer: Yes
					# 2. HF scheduler + HF optimizer: Yes
					# 3. DS scheduler + HF optimizer: Yes
					# 4. HF scheduler + DS optimizer: No
					#
					# Unless Offload is enabled in which case it's:
					# 1. DS scheduler + DS optimizer: Yes
					# 2. HF scheduler + HF optimizer: Mostly*
					# 3. DS scheduler + HF optimizer: Mostly*
					# 4. HF scheduler + DS optimizer: No
					#
					# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

					UpperCamelCase_											=				None
					if "optimizer" in config:
										if args.adafactor:
															raise ValueError(
															    "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
															    "Only one optimizer can be configured.")
										UpperCamelCase_											=				DummyOptim(params=_lowerCAmelCase)
					else:
										if hf_deepspeed_config.is_offload():
															logger.info(
															    "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
															    " custom optimizer has both CPU and GPU implementation (except LAMB)")

										# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
										# But trainer uses AdamW by default.
										UpperCamelCase_											=				trainer.create_optimizer()
										# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
										UpperCamelCase_											=				True

					UpperCamelCase_											=				None
					if "scheduler" in config:
										UpperCamelCase_											=				DummyScheduler(_lowerCAmelCase)
					else:
										if isinstance(_lowerCAmelCase				,		_lowerCAmelCase):
															raise ValueError(
															    "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
															    "Please configure a scheduler in the DeepSpeed config.")
										UpperCamelCase_											=				trainer.create_scheduler(num_training_steps=_lowerCAmelCase				,		optimizer=_lowerCAmelCase)

					return optimizer, lr_scheduler



def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase=False):
					from deepspeed.utils import logger as ds_logger

					UpperCamelCase_											=				trainer.model
					UpperCamelCase_											=				trainer.args

					UpperCamelCase_											=				trainer.accelerator.state.deepspeed_plugin.hf_ds_config

					# resume config update - some bits like `model` and `num_training_steps` only become available during train
					hf_deepspeed_config.trainer_config_finalize(_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase)

					# set the Deepspeed log level consistent with the Trainer
					ds_logger.setLevel(args.get_process_log_level())

					if inference:
										# only Z3 makes sense for the inference
										if not hf_deepspeed_config.is_zeroa():
															raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config")

										# in case the training config is re-used for inference
										hf_deepspeed_config.del_config_sub_tree("optimizer")
										hf_deepspeed_config.del_config_sub_tree("lr_scheduler")
										UpperCamelCase_					,      UpperCamelCase_											=				None, None
										UpperCamelCase_											=				None
					else:
										UpperCamelCase_											=				None  # important for when deepspeed_init is used as re-init
										UpperCamelCase_											=				list(filter(lambda _lowerCAmelCase: p.requires_grad				,		model.parameters()))
										UpperCamelCase_					,      UpperCamelCase_											=				deepspeed_optim_sched(
										    _lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase				,		_lowerCAmelCase)

					# keep for quick debug:
					# from pprint import pprint; pprint(config)

					return optimizer, lr_scheduler






def 				_lowerCAmelCase      (_lowerCAmelCase				,		_lowerCAmelCase):
					# it's possible that the user is trying to resume from model_path, which doesn't necessarily
					# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
					# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
					# path contains what looks like a deepspeed checkpoint
					import glob

					UpperCamelCase_											=				sorted(glob.glob(f"""{checkpoint_path}/global_step*"""))

					if len(_lowerCAmelCase) > 0:
										logger.info(f"""Attempting to resume from {checkpoint_path}""")
										# this magically updates self.optimizer and self.lr_scheduler
										UpperCamelCase_					,      UpperCamelCase_											=				deepspeed_engine.load_checkpoint(
										    _lowerCAmelCase				,		load_optimizer_states=_lowerCAmelCase				,		load_lr_scheduler_states=_lowerCAmelCase)
										if load_path is None:
															raise ValueError(f"""[deepspeed] failed to resume from checkpoint {checkpoint_path}""")
					else:
										raise ValueError(f"""Can't find a valid checkpoint at {checkpoint_path}""")





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase				:							Optional[int]								={
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Optional[int]								=["""RealmTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Union[str, Any]								=[
	    """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RealmEmbedder""",
	    """RealmForOpenQA""",
	    """RealmKnowledgeAugEncoder""",
	    """RealmPreTrainedModel""",
	    """RealmReader""",
	    """RealmScorer""",
	    """load_tf_weights_in_realm""",
	]
	UpperCAmelCase				:							List[str]								=["""RealmRetriever"""]


if TYPE_CHECKING:
	from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
	from .tokenization_realm import RealmTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_realm import RealmTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_realm import (
		    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RealmEmbedder,
		    RealmForOpenQA,
		    RealmKnowledgeAugEncoder,
		    RealmPreTrainedModel,
		    RealmReader,
		    RealmScorer,
		    load_tf_weights_in_realm,
		)
		from .retrieval_realm import RealmRetriever


else:
	import sys

	UpperCAmelCase				:							str								=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
