






'''simple docstring'''




import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


lowerCamelCase_							      =      getLogger(__name__)


lowerCamelCase_							      =      '''cuda''' if torch.cuda.is_available() else '''cpu'''

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase = 8     ,						__lowercase = DEFAULT_DEVICE     ,						__lowercase=False     ,						__lowercase="summarization"     ,						__lowercase=None     ,						**__lowercase     ,						) ->		Dict:

      '''simple docstring'''


      _A      =			Path(__lowercase ).open("w"     ,						encoding="utf-8" )
      _A      =			str(__lowercase )
      _A      =			AutoModelForSeqaSeqLM.from_pretrained(__lowercase ).to(__lowercase )
      if fpaa:
            _A      =			model.half()

      _A      =			AutoTokenizer.from_pretrained(__lowercase )
      logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}''' )  # if this is wrong, check config.model_type.

      _A      =			time.time()
      # update config with task specific params
      use_task_specific_params(__lowercase     ,						__lowercase )
      if prefix is None:
            _A      =			prefix or getattr(model.config     ,						"prefix"     ,						"" ) or ""
      for examples_chunk in tqdm(list(chunks(__lowercase     ,						__lowercase ) ) ):
            _A      =			[prefix + text for text in examples_chunk]
            _A      =			tokenizer(__lowercase     ,						return_tensors="pt"     ,						truncation=__lowercase     ,						padding="longest" ).to(__lowercase )
            _A      =			model.generate(
                input_ids=batch.input_ids     ,						attention_mask=batch.attention_mask     ,						**__lowercase     ,						)
            _A      =			tokenizer.batch_decode(__lowercase     ,						skip_special_tokens=__lowercase     ,						clean_up_tokenization_spaces=__lowercase )
            for hypothesis in dec:
                  fout.write(hypothesis + "\n" )
                  fout.flush()
      fout.close()
      _A      =			int(time.time() - start_time )  # seconds
      _A      =			len(__lowercase )
      return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs     ,						4 )}

def       __lowercase		(    ) ->		Tuple:

      '''simple docstring'''


      return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )

def       __lowercase		(    __lowercase=True ) ->		str:

      '''simple docstring'''


      _A      =			argparse.ArgumentParser()
      parser.add_argument("model_name"     ,						type=__lowercase     ,						help="like facebook/bart-large-cnn,t5-base, etc." )
      parser.add_argument("input_path"     ,						type=__lowercase     ,						help="like cnn_dm/test.source" )
      parser.add_argument("save_path"     ,						type=__lowercase     ,						help="where to save summaries" )
      parser.add_argument("--reference_path"     ,						type=__lowercase     ,						required=__lowercase     ,						help="like cnn_dm/test.target" )
      parser.add_argument("--score_path"     ,						type=__lowercase     ,						required=__lowercase     ,						default="metrics.json"     ,						help="where to save metrics" )
      parser.add_argument("--device"     ,						type=__lowercase     ,						required=__lowercase     ,						default=__lowercase     ,						help="cuda, cuda:1, cpu etc." )
      parser.add_argument(
          "--prefix"     ,						type=__lowercase     ,						required=__lowercase     ,						default=__lowercase     ,						help="will be added to the begininng of src examples" )
      parser.add_argument("--task"     ,						type=__lowercase     ,						default="summarization"     ,						help="used for task_specific_params + metrics" )
      parser.add_argument("--bs"     ,						type=__lowercase     ,						default=8     ,						required=__lowercase     ,						help="batch size" )
      parser.add_argument(
          "--n_obs"     ,						type=__lowercase     ,						default=-1     ,						required=__lowercase     ,						help="How many observations. Defaults to all." )
      parser.add_argument("--fp16"     ,						action="store_true" )
      parser.add_argument("--dump-args"     ,						action="store_true"     ,						help="print the custom hparams with the results" )
      parser.add_argument(
          "--info"     ,						nargs="?"     ,						type=__lowercase     ,						const=datetime_now()     ,						help=(
              "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
              " lang=en-ru. If no value is passed, the current datetime string will be used."
          )     ,						)
      # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
      _A  ,			_A      =			parser.parse_known_args()
      _A      =			parse_numeric_n_bool_cl_kwargs(__lowercase )
      if parsed_args and verbose:
            print(F'''parsed the following generate kwargs: {parsed_args}''' )
      with open(args.input_path ) as f:
            _A      =			[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
      if args.n_obs > 0:
            _A      =			examples[: args.n_obs]
      Path(args.save_path ).parent.mkdir(exist_ok=__lowercase )
      if args.reference_path is None and Path(args.score_path ).exists():
            warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.''' )
      _A      =			generate_summaries_or_translations(
          __lowercase     ,						args.save_path     ,						args.model_name     ,						batch_size=args.bs     ,						device=args.device     ,						fpaa=args.fpaa     ,						task=args.task     ,						prefix=args.prefix     ,						**__lowercase     ,						)

      if args.reference_path is None:
            return {}

      # Compute scores
      _A      =			calculate_bleu if "translation" in args.task else calculate_rouge
      _A      =			[x.rstrip() for x in open(args.save_path ).readlines()]
      _A      =			[x.rstrip() for x in open(args.reference_path ).readlines()][: len(__lowercase )]
      _A      =			score_fn(__lowercase     ,						__lowercase )
      scores.update(__lowercase )

      if args.dump_args:
            scores.update(__lowercase )
      if args.info:
            _A      =			args.info

      if verbose:
            print(__lowercase )

      if args.score_path is not None:
            json.dump(__lowercase     ,						open(args.score_path     ,						"w" ) )

      return scores


if __name__ == "__main__":
    # Usage for MT:
    # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
    run_generate(verbose=True)













'''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

lowerCamelCase_							      =      logging.get_logger(__name__)

def       __lowercase		(    ) ->		Optional[Any]:

      '''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      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			Dict   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





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





class      _UpperCAmelCase    (  snake_case_		):




   """simple docstring"""

   def __init__(	self				:			List[str]   ,   __UpperCAmelCase				:			List[str]	):





         '''simple docstring'''





         super().__init__(__UpperCAmelCase	)
         _A      =			None
         _A      =			[]

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''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				:			Dict   ,   __UpperCAmelCase				:			Dict	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			List[Any]   ,   __UpperCAmelCase				:			int   ,   __UpperCAmelCase				:			Optional[int]   ,   __UpperCAmelCase				:			Any=None   ,   __UpperCAmelCase				:			Union[str, Any]=True	):





         '''simple docstring'''





         _A  ,			_A      =			self.find_config_node(__UpperCAmelCase	)
         if config is None:
               return

         if config.get(__UpperCAmelCase	) == "auto":
               _A      =			hf_val
               return

         if not must_match:
               return

         _A      =			config.get(__UpperCAmelCase	)
         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}'''	)

   snake_case					       =		partialmethod(snake_case_							,					must_match=snake_case_		)

   def 		lowerCAmelCase				(	self				:			str   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _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"   ,   __UpperCAmelCase   ,   "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 		lowerCAmelCase				(	self				:			Optional[int]   ,   __UpperCAmelCase				:			Dict   ,   __UpperCAmelCase				:			str   ,   __UpperCAmelCase				:			Tuple	):





         '''simple docstring'''





         _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(__UpperCAmelCase	)]

         if len(__UpperCAmelCase	) > 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"   ,   __UpperCAmelCase   ,   "num_training_steps (calculated)"	)
         self.fill_match("scheduler.params.warmup_num_steps"   ,   args.get_warmup_steps(__UpperCAmelCase	)   ,   "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\'.'''	)





lowerCamelCase_							      =      None

def       __lowercase		(    __lowercase ) ->		Optional[Any]:

      '''simple docstring'''


      global _hf_deepspeed_config_weak_ref
      # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
      _A      =			weakref.ref(__lowercase )

def       __lowercase		(    ) ->		str:

      '''simple docstring'''


      global _hf_deepspeed_config_weak_ref
      _A      =			None

def       __lowercase		(    ) ->		Optional[int]:

      '''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       __lowercase		(    ) ->		str:

      '''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       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase     ,						__lowercase ) ->		int:

      '''simple docstring'''


      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=__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.
            _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(__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." )
            _A      =			trainer.create_scheduler(num_training_steps=__lowercase     ,						optimizer=__lowercase )

      return optimizer, lr_scheduler

def       __lowercase		(    __lowercase     ,						__lowercase     ,						__lowercase=False ) ->		Tuple:

      '''simple docstring'''


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

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

      return optimizer, lr_scheduler


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

      '''simple docstring'''


      import glob

      _A      =			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
            _A  ,			_A      =			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}''' )





