






"""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(  ):
       """simple docstring"""





       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   (						__a    ):





   '''simple docstring'''






   def __init__(    self ,      UpperCAmelCase_)       ->					Dict:




          '''simple docstring'''




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







class 		_a   (						__a    ):





   '''simple docstring'''






   def __init__(    self ,      UpperCAmelCase_)       ->					Optional[Any]:




          '''simple docstring'''




          super().__init__(lowerCAmelCase__)
          lowercase__:				Tuple			  =			None
          lowercase__:				List[Any]			  =			[]





   def __lowercase				(    self)       ->					str:




          '''simple docstring'''




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





   def __lowercase				(    self ,      UpperCAmelCase_)       ->					Optional[int]:




          '''simple docstring'''




          lowercase__:				str			  =			self.get_value(lowerCAmelCase__)
          if val is None:
                 return False
          else:
                 return val == "auto"





   def __lowercase				(    self ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_=None ,      UpperCAmelCase_=True)       ->					int:




          '''simple docstring'''




          lowercase__:				Any			  =			self.find_config_node(lowerCAmelCase__)
          if config is None:
                 return

          if config.get(lowerCAmelCase__) == "auto":
                 lowercase__:				Any			  =			hf_val
                 return

          if not must_match:
                 return

          lowercase__:				Optional[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(__a				,     must_match=__a    )





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




          '''simple docstring'''




          lowercase__:				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:
                 lowercase__:				Dict			  =			"""apex""" if args.fpaa_backend == """apex""" else """amp"""
          else:
                 lowercase__:				Dict			  =			None

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





   def __lowercase				(    self ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_)       ->					List[str]:




          '''simple docstring'''




          lowercase__:				Optional[Any]			  =			[
              """zero_optimization.reduce_bucket_size""",
              """zero_optimization.stage3_prefetch_bucket_size""",
              """zero_optimization.stage3_param_persistence_threshold""",
          ]
          lowercase__:				Any			  =			[x for x in hidden_size_based_keys if self.is_auto(lowerCAmelCase__)]

          if len(lowerCAmelCase__) > 0:
                 if hasattr(model.config ,      "hidden_size"):
                        lowercase__:				Tuple			  =			model.config.hidden_size
                 elif hasattr(model.config ,      "hidden_sizes"):
                        # if there are many hidden sizes pick the largest one
                        lowercase__:				Tuple			  =			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:
                 lowercase__:				Union[str, Any]			  =			"""\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(  snake_case_		):
       """simple docstring"""





       global _hf_deepspeed_config_weak_ref
       # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
       lowercase__:				Any			  =			weakref.ref(lowerCAmelCase_		)






def 				A(  ):
       """simple docstring"""





       global _hf_deepspeed_config_weak_ref
       lowercase__:				List[Any]			  =			None






def 				A(  ):
       """simple docstring"""





       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(  ):
       """simple docstring"""





       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(  snake_case_						,   snake_case_						,   snake_case_						,   snake_case_						,   snake_case_		):
       """simple docstring"""





       from accelerate.utils import DummyOptim, DummyScheduler

       lowercase__:				List[str]			  =			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)

       lowercase__:				Dict			  =			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."		)
              lowercase__:				int			  =			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.
              lowercase__:				Union[str, Any]			  =			trainer.create_optimizer()
              # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
              lowercase__:				List[Any]			  =			True

       lowercase__:				Dict			  =			None
       if "scheduler" in config:
              lowercase__:				Optional[int]			  =			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."		)
              lowercase__:				int			  =			trainer.create_scheduler(num_training_steps=lowerCAmelCase_						,   optimizer=lowerCAmelCase_		)

       return optimizer, lr_scheduler






def 				A(  snake_case_						,   snake_case_						,   snake_case_=False		):
       """simple docstring"""





       from deepspeed.utils import logger as ds_logger

       lowercase__:				Optional[Any]			  =			trainer.model
       lowercase__:				Optional[int]			  =			trainer.args

       lowercase__:				List[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(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"		)
              lowercase__:				Tuple			  =			None, None
              lowercase__:				List[Any]			  =			None
       else:
              lowercase__:				Any			  =			None  # important for when deepspeed_init is used as re-init
              lowercase__:				str			  =			list(filter(lambda snake_case_		: p.requires_grad						,   model.parameters()		)		)
              lowercase__:				Optional[int]			  =			deepspeed_optim_sched(
                  lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_						,   lowerCAmelCase_		)

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

       return optimizer, lr_scheduler







def 				A(  snake_case_						,   snake_case_		):
       """simple docstring"""





       import glob

       lowercase__:				Any			  =			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
              lowercase__:				Optional[Any]			  =			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}"""		)








'''simple docstring'''

import io
import json
import subprocess


lowerCAmelCase_			:			Tuple			   =     [
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

lowerCAmelCase_			:			str			   =     8







def __A  (    lowerCAmelCase_ ,       lowerCAmelCase_       ):
     _UpperCAmelCase					:  Union[str, Any]         =    {}
     for src, tgt in pairs:
          _UpperCAmelCase					:  Union[str, Any]         =    f"{src}-{tgt}"
          _UpperCAmelCase					:  List[Any]         =    f"sacrebleu -t wmt19 -l {pair} --echo src".split()
          _UpperCAmelCase					:  List[str]         =    subprocess.run(lowerCAmelCase_ ,       stdout=subprocess.PIPE       ).stdout.decode("""utf-8"""       ).splitlines()
          _UpperCAmelCase					:  str         =    f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
          _UpperCAmelCase					:  List[Any]         =    subprocess.run(lowerCAmelCase_ ,       stdout=subprocess.PIPE       ).stdout.decode("""utf-8"""       ).splitlines()
          _UpperCAmelCase					:  int         =    {"""src""": src_lines[:n_objs], """tgt""": tgt_lines[:n_objs]}
     return text


lowerCAmelCase_			:			Optional[int]			   =     get_all_data(pairs, n_objs)
lowerCAmelCase_			:			str			   =     '''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
   lowerCAmelCase_			:			Tuple			   =     json.dump(text, f, indent=2, ensure_ascii=False)


