
'''simple docstring'''
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

__SCREAMING_SNAKE_CASE				:							Tuple		       =		logging.get_logger(__name__)







def 			a_							(  ):
							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 				__lowerCAmelCase      (      __lowerCamelCase	):

						"""simple docstring"""






						def __init__(     self       :			str   ,     lowerCAmelCase       :			Union[str, Any]  ):
													set_hf_deepspeed_config(self  )
													dep_version_check("accelerate"  )
													dep_version_check("deepspeed"  )
													super().__init__(lowerCAmelCase  )






class 				__lowerCAmelCase      (      __lowerCamelCase	):

						"""simple docstring"""






						def __init__(     self       :			str   ,     lowerCAmelCase       :			List[Any]  ):
													super().__init__(lowerCAmelCase  )
													A_ 						=		None
													A_ 						=		[]






						def 							_UpperCAmelCase					(     self       :			Dict  ):






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






						def 							_UpperCAmelCase					(     self       :			List[Any]   ,     lowerCAmelCase       :			str  ):
													A_ 						=		self.get_value(lowerCAmelCase  )
													if val is None:
																				return False
													else:
																				return val == "auto"






						def 							_UpperCAmelCase					(     self       :			int   ,     lowerCAmelCase       :			List[str]   ,     lowerCAmelCase       :			str   ,     lowerCAmelCase       :			Any=None   ,     lowerCAmelCase       :			Optional[Any]=True  ):
													A_						,				A_ 						=		self.find_config_node(lowerCAmelCase  )
													if config is None:
																				return

													if config.get(lowerCAmelCase  ) == "auto":
																				A_ 						=		hf_val
																				return

													if not must_match:
																				return

													A_ 						=		config.get(lowerCAmelCase  )
													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}"  )

						_UpperCAmelCase		:					Union[str, Any]      	=partialmethod(__lowerCamelCase							, must_match=__lowerCamelCase	)






						def 							_UpperCAmelCase					(     self       :			List[str]   ,     lowerCAmelCase       :			Any  ):
													A_ 						=		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"   ,     lowerCAmelCase   ,     "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:
																				A_ 						=		"apex" if args.fpaa_backend == "apex" else "amp"
													else:
																				A_ 						=		None

													if args.save_on_each_node:
																				# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
																				A_ 						=		self.config.get("checkpoint"   ,     {}  )
																				A_ 						=		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"  ):
																				A_ 						=		torch.bfloataa
													elif self.is_false("fp16.enabled"  ):
																				A_ 						=		torch.floataa
													else:
																				A_ 						=		torch.floataa






						def 							_UpperCAmelCase					(     self       :			Any   ,     lowerCAmelCase       :			Any   ,     lowerCAmelCase       :			str   ,     lowerCAmelCase       :			Any  ):
													A_ 						=		[
													    "zero_optimization.reduce_bucket_size",
													    "zero_optimization.stage3_prefetch_bucket_size",
													    "zero_optimization.stage3_param_persistence_threshold",
													]
													A_ 						=		[x for x in hidden_size_based_keys if self.is_auto(lowerCAmelCase  )]

													if len(lowerCAmelCase  ) > 0:
																				if hasattr(model.config   ,     "hidden_size"  ):
																											A_ 						=		model.config.hidden_size
																				elif hasattr(model.config   ,     "hidden_sizes"  ):
																											# if there are many hidden sizes pick the largest one
																											A_ 						=		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"   ,     lowerCAmelCase   ,     "num_training_steps (calculated)"  )
													self.fill_match("scheduler.params.warmup_num_steps"   ,     args.get_warmup_steps(lowerCAmelCase  )   ,     "warmup_steps"  )

													if len(self.mismatches  ) > 0:
																				A_ 						=		"\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'."  )



__SCREAMING_SNAKE_CASE				:							Any		       =		None







def 			a_							(  UpperCamelCase_   ):
							global _hf_deepspeed_config_weak_ref
							# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
							A_ 						=		weakref.ref(UpperCamelCase_   )







def 			a_							(  ):
							global _hf_deepspeed_config_weak_ref
							A_ 						=		None







def 			a_							(  ):


							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 			a_							(  ):


							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 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
							from accelerate.utils import DummyOptim, DummyScheduler

							A_ 						=		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)

							A_ 						=		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."   )
														A_ 						=		DummyOptim(params=UpperCamelCase_   )
							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.
														A_ 						=		trainer.create_optimizer()
														# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
														A_ 						=		True

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

							return optimizer, lr_scheduler







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

							A_ 						=		trainer.model
							A_ 						=		trainer.args

							A_ 						=		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(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )

							# 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"   )
														A_						,				A_ 						=		None, None
														A_ 						=		None
							else:
														A_ 						=		None  # important for when deepspeed_init is used as re-init
														A_ 						=		list(filter(lambda UpperCamelCase_   : p.requires_grad	,     model.parameters()   )   )
														A_						,				A_ 						=		deepspeed_optim_sched(
														    UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   )

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

							return optimizer, lr_scheduler







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							import glob

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

							if len(UpperCamelCase_   ) > 0:
														logger.info(f"Attempting to resume from {checkpoint_path}"   )
														# this magically updates self.optimizer and self.lr_scheduler
														A_						,				A_ 						=		deepspeed_engine.load_checkpoint(
														    UpperCamelCase_	,     load_optimizer_states=UpperCamelCase_	,     load_lr_scheduler_states=UpperCamelCase_   )
														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}"   )









'''simple docstring'''
import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf

							from transformers.activations_tf import get_tf_activation






@require_tf
class 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		tf.constant([-1_00, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]  )
													A_ 						=		get_tf_activation("gelu"  )
													A_ 						=		get_tf_activation("gelu_10"  )

													A_ 						=		gelu(lowerCAmelCase  )
													A_ 						=		geluaa(lowerCAmelCase  )

													A_ 						=		tf.where(y_gelu_aa < 1_0.0   ,     1.0   ,     0.0  )

													self.assertEqual(tf.math.reduce_max(lowerCAmelCase  ).numpy().item()   ,     1_0.0  )
													self.assertTrue(np.allclose(y_gelu * clipped_mask   ,     y_gelu_aa * clipped_mask  )  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													get_tf_activation("gelu"  )
													get_tf_activation("gelu_10"  )
													get_tf_activation("gelu_fast"  )
													get_tf_activation("gelu_new"  )
													get_tf_activation("glu"  )
													get_tf_activation("mish"  )
													get_tf_activation("quick_gelu"  )
													get_tf_activation("relu"  )
													get_tf_activation("sigmoid"  )
													get_tf_activation("silu"  )
													get_tf_activation("swish"  )
													get_tf_activation("tanh"  )
													with self.assertRaises(lowerCAmelCase  ):
																				get_tf_activation("bogus"  )
													with self.assertRaises(lowerCAmelCase  ):
																				get_tf_activation(lowerCAmelCase  )





