





"""simple docstring"""




import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property




class 				_a		(   unittest.TestCase       ):






				@cached_property
				def        snake_case			(				self	:					Tuple							)			->   List[str]:




										'''simple docstring'''



										return BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''							)






				@cached_property
				def        snake_case			(				self	:					Dict							)			->   Optional[Any]:




										'''simple docstring'''



										return BlenderbotTokenizerFast.from_pretrained('''facebook/blenderbot-3B'''							)






				def        snake_case			(				self	:					Optional[int]							)			->   Optional[int]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					self.tokenizer_ab
										_UpperCamelCase    :      Optional[int]						     =					''' I am a small frog.'''
										_UpperCamelCase    :      Tuple						     =					tok([src_text],				padding=lowerCAmelCase__,				truncation=lowerCAmelCase__							)['''input_ids''']
										_UpperCamelCase    :      List[str]						     =					tok.batch_decode(lowerCAmelCase__,				skip_special_tokens=lowerCAmelCase__,				clean_up_tokenization_spaces=lowerCAmelCase__							)[0]
										assert src_text == decoded






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




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					self.rust_tokenizer_ab
										_UpperCamelCase    :      Any						     =					''' I am a small frog.'''
										_UpperCamelCase    :      int						     =					tok([src_text],				padding=lowerCAmelCase__,				truncation=lowerCAmelCase__							)['''input_ids''']
										_UpperCamelCase    :      int						     =					tok.batch_decode(lowerCAmelCase__,				skip_special_tokens=lowerCAmelCase__,				clean_up_tokenization_spaces=lowerCAmelCase__							)[0]
										assert src_text == decoded






				def        snake_case			(				self	:					int							)			->   Optional[Any]:




										'''simple docstring'''



										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([''' Sam''', '''Sam''']							).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]






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




										'''simple docstring'''



										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([''' Sam''', '''Sam''']							).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]










"""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

UpperCamelCase_     =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 				_a		(   _lowerCAmelCase       ):






				def __init__(				self	:					Optional[Any],				lowerCAmelCase__	:					Tuple							)			->   List[Any]:




										'''simple docstring'''



										set_hf_deepspeed_config(self							)
										dep_version_check('''accelerate'''							)
										dep_version_check('''deepspeed'''							)
										super().__init__(lowerCAmelCase__							)




class 				_a		(   _lowerCAmelCase       ):






				def __init__(				self	:					Any,				lowerCAmelCase__	:					int							)			->   Union[str, Any]:




										'''simple docstring'''



										super().__init__(lowerCAmelCase__							)
										_UpperCamelCase    :      Any						     =					None
										_UpperCamelCase    :      List[str]						     =					[]






				def        snake_case			(				self	:					Optional[Any]							)			->   Optional[Any]:




										'''simple docstring'''



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






				def        snake_case			(				self	:					int,				lowerCAmelCase__	:					Any							)			->   List[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					self.get_value(lowerCAmelCase__							)
										if val is None:
																return False
										else:
																return val == "auto"






				def        snake_case			(				self	:					Optional[Any],				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					int,				lowerCAmelCase__	:					List[str]=None,				lowerCAmelCase__	:					Dict=True							)			->   int:




										'''simple docstring'''



										_UpperCamelCase     ,							_UpperCamelCase    :      Tuple						     =					self.find_config_node(lowerCAmelCase__							)
										if config is None:
																return

										if config.get(lowerCAmelCase__							) == "auto":
																_UpperCamelCase    :      int						     =					hf_val
																return

										if not must_match:
																return

										_UpperCamelCase    :      Any						     =					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			=					partialmethod(_lowerCAmelCase			,       must_match=_lowerCAmelCase       )






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					List[str]							)			->   int:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					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:
																_UpperCamelCase    :      Union[str, Any]						     =					'''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
										else:
																_UpperCamelCase    :      List[str]						     =					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    :      Tuple						     =					self.config.get('''checkpoint''',				{}							)
																_UpperCamelCase    :      str						     =					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    :      Optional[int]						     =					torch.bfloataa
										elif self.is_false('''fp16.enabled'''							):
																_UpperCamelCase    :      Optional[Any]						     =					torch.floataa
										else:
																_UpperCamelCase    :      str						     =					torch.floataa






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Tuple,				lowerCAmelCase__	:					str							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Tuple						     =					[
										    '''zero_optimization.reduce_bucket_size''',
										    '''zero_optimization.stage3_prefetch_bucket_size''',
										    '''zero_optimization.stage3_param_persistence_threshold''',
										]
										_UpperCamelCase    :      Dict						     =					[x for x in hidden_size_based_keys if self.is_auto(lowerCAmelCase__							)]

										if len(lowerCAmelCase__							) > 0:
																if hasattr(model.config,				'''hidden_size'''							):
																						_UpperCamelCase    :      str						     =					model.config.hidden_size
																elif hasattr(model.config,				'''hidden_sizes'''							):
																						# if there are many hidden sizes pick the largest one
																						_UpperCamelCase    :      Union[str, Any]						     =					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''',				1_0 * 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:
																_UpperCamelCase    :      int						     =					'''\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_     =None





def  a_       (							_lowercase     ):
						# 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    :      int						     =					weakref.ref(_lowercase     )





def  a_       (							):
						# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
						global _hf_deepspeed_config_weak_ref
						_UpperCamelCase    :      Optional[int]						     =					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_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     ):
						from accelerate.utils import DummyOptim, DummyScheduler

						_UpperCamelCase    :      Tuple						     =					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    :      str						     =					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    :      Optional[int]						     =					DummyOptim(params=_lowercase     )
						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    :      Union[str, Any]						     =					trainer.create_optimizer()
												# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
												_UpperCamelCase    :      Tuple						     =					True

						_UpperCamelCase    :      Optional[int]						     =					None
						if "scheduler" in config:
												_UpperCamelCase    :      List[Any]						     =					DummyScheduler(_lowercase     )
						else:
												if isinstance(_lowercase ,		_lowercase     ):
																		raise ValueError(
																		    '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
																		    '''Please configure a scheduler in the DeepSpeed config.'''     )
												_UpperCamelCase    :      Any						     =					trainer.create_scheduler(num_training_steps=_lowercase ,		optimizer=_lowercase     )

						return optimizer, lr_scheduler





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

						_UpperCamelCase    :      Tuple						     =					trainer.model
						_UpperCamelCase    :      List[str]						     =					trainer.args

						_UpperCamelCase    :      str						     =					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(_lowercase ,		_lowercase ,		_lowercase     )

						# 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    :      Tuple						     =					None, None
												_UpperCamelCase    :      Optional[int]						     =					None
						else:
												_UpperCamelCase    :      Any						     =					None  # important for when deepspeed_init is used as re-init
												_UpperCamelCase    :      Any						     =					list(filter(lambda _lowercase     : p.requires_grad ,		model.parameters()     )     )
												_UpperCamelCase     ,							_UpperCamelCase    :      Dict						     =					deepspeed_optim_sched(
												    _lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )

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

						return optimizer, lr_scheduler






def  a_       (							_lowercase ,		_lowercase     ):
						# 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    :      Tuple						     =					sorted(glob.glob(F"""{checkpoint_path}/global_step*"""     )     )

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



