






'''simple docstring'''







import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__A   :							Optional[Any]     	=						logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__A   :							Optional[int]     	=						{
    'base': AutoModel,
    'sequence-classification': AutoModelForSequenceClassification,
    'question-answering': AutoModelForQuestionAnswering,
    'pretraining': AutoModelForPreTraining,
    'token-classification': AutoModelForTokenClassification,
    'language-modeling': AutoModelWithLMHead,
    'summarization': AutoModelForSeqaSeqLM,
    'translation': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__A   :							Dict     	=						{
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__A   :							Optional[Any]     	=						sorted(arg_to_scheduler.keys())
__A   :							Optional[Any]     	=						'{' + ', '.join(arg_to_scheduler_choices) + '}'






class        a__     (      pl.LightningModule    ):
     '''simple docstring'''







     def __init__(self     :						Dict					,					lowercase__     :						argparse.Namespace					,					lowercase__     :						Dict=None					,					lowercase__     :						int="base"					,					lowercase__     :						Optional[Any]=None					,					lowercase__     :						List[Any]=None					,					lowercase__     :						List[str]=None					,					**lowercase__     :						int					,					)	->							Optional[int]:
           super().__init__()
           # TODO: move to self.save_hyperparameters()
           # self.save_hyperparameters()
           # can also expand arguments into trainer signature for easier reading

           self.save_hyperparameters(lowercase__       )
           __A		 =   0
           __A		 =   Path(self.hparams.output_dir       )
           __A		 =   self.hparams.cache_dir if self.hparams.cache_dir else None
           if config is None:
                 __A		 =   AutoConfig.from_pretrained(
                     self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,					**({"num_labels": num_labels} if num_labels is not None else {})					,					cache_dir=lowercase__					,					**lowercase__					,					)
           else:
                 __A		 =   config

           __A		 =   ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
           for p in extra_model_params:
                 if getattr(self.hparams					,					lowercase__					,					lowercase__       ):
                       assert hasattr(self.config					,					lowercase__       ), f"model config doesn't have a `{p}` attribute"
                       setattr(self.config					,					lowercase__					,					getattr(self.hparams					,					lowercase__       )       )

           if tokenizer is None:
                 __A		 =   AutoTokenizer.from_pretrained(
                     self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,					cache_dir=lowercase__					,					)
           else:
                 __A		 =   tokenizer
           __A		 =   MODEL_MODES[mode]
           if model is None:
                 __A		 =   self.model_type.from_pretrained(
                     self.hparams.model_name_or_path					,					from_tf=bool(".ckpt" in self.hparams.model_name_or_path       )					,					config=self.config					,					cache_dir=lowercase__					,					)
           else:
                 __A		 =   model






     def      _UpperCamelCase (self     :						List[Any]					,					*lowercase__     :						Optional[int]					,					**lowercase__     :						int       )	->							Union[str, Any]:
           __A		 =   self.model_type.from_pretrained(*lowercase__					,					**lowercase__       )






     def      _UpperCamelCase (self     :						Optional[Any]       )	->							Any:
           __A		 =   arg_to_scheduler[self.hparams.lr_scheduler]
           __A		 =   get_schedule_func(
               self.opt					,					num_warmup_steps=self.hparams.warmup_steps					,					num_training_steps=self.total_steps()       )
           __A		 =   {"scheduler": scheduler, "interval": "step", "frequency": 1}
           return scheduler






     def      _UpperCamelCase (self     :						Any       )	->							Tuple:
           __A		 =   self.model
           __A		 =   ["bias", "LayerNorm.weight"]
           __A		 =   [
               {
                   "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay       )],
                   "weight_decay": self.hparams.weight_decay,
               },
               {
                   "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay       )],
                   "weight_decay": 0.0,
               },
           ]
           if self.hparams.adafactor:
                 __A		 =   Adafactor(
                     lowercase__					,					lr=self.hparams.learning_rate					,					scale_parameter=lowercase__					,					relative_step=lowercase__       )

           else:
                 __A		 =   AdamW(
                     lowercase__					,					lr=self.hparams.learning_rate					,					eps=self.hparams.adam_epsilon       )
           __A		 =   optimizer

           __A		 =   self.get_lr_scheduler()

           return [optimizer], [scheduler]






     def      _UpperCamelCase (self     :						str					,					lowercase__     :						Union[str, Any]					,					lowercase__     :						Optional[int]       )	->							Union[str, Any]:
           return self.validation_step(lowercase__					,					lowercase__       )






     def      _UpperCamelCase (self     :						Any					,					lowercase__     :						Optional[int]       )	->							List[str]:
           return self.validation_end(lowercase__       )






     def      _UpperCamelCase (self     :						int       )	->							int:
           __A		 =   max(1					,					self.hparams.gpus       )  # TODO: consider num_tpu_cores
           __A		 =   self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
           return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






     def      _UpperCamelCase (self     :						Tuple					,					lowercase__     :						List[Any]       )	->							Union[str, Any]:
           if stage == "test":
                 __A		 =   len(self.test_dataloader().dataset       )
           else:
                 __A		 =   self.get_dataloader("train"					,					self.hparams.train_batch_size					,					shuffle=lowercase__       )
                 __A		 =   len(self.train_dataloader().dataset       )






     def      _UpperCamelCase (self     :						List[str]					,					lowercase__     :						str					,					lowercase__     :						int					,					lowercase__     :						bool = False       )	->							str:
           raise NotImplementedError("You must implement this for your task"       )






     def      _UpperCamelCase (self     :						Dict       )	->							Optional[Any]:
           return self.train_loader






     def      _UpperCamelCase (self     :						List[Any]       )	->							Optional[int]:
           return self.get_dataloader("dev"					,					self.hparams.eval_batch_size					,					shuffle=lowercase__       )






     def      _UpperCamelCase (self     :						Tuple       )	->							Optional[Any]:
           return self.get_dataloader("test"					,					self.hparams.eval_batch_size					,					shuffle=lowercase__       )






     def      _UpperCamelCase (self     :						str					,					lowercase__     :						Any       )	->							List[Any]:
           return os.path.join(
               self.hparams.data_dir					,					"cached_{}_{}_{}".format(
                   lowercase__					,					list(filter(lowercase__					,					self.hparams.model_name_or_path.split("/"       )       )       ).pop()					,					str(self.hparams.max_seq_length       )					,					)					,					)






     @pl.utilities.rank_zero_only
     def      _UpperCamelCase (self     :						Tuple					,					lowercase__     :						Dict[str, Any]       )	->							None:
           __A		 =   self.output_dir.joinpath("best_tfmr"       )
           __A		 =   self.step_count
           self.model.save_pretrained(lowercase__       )
           self.tokenizer.save_pretrained(lowercase__       )






     @staticmethod
     def      _UpperCamelCase (lowercase__     :						List[str]					,					lowercase__     :						List[Any]       )	->							Dict:
           parser.add_argument(
               "--model_name_or_path"					,					default=lowercase__					,					type=lowercase__					,					required=lowercase__					,					help="Path to pretrained model or model identifier from huggingface.co/models"					,					)
           parser.add_argument(
               "--config_name"					,					default=""					,					type=lowercase__					,					help="Pretrained config name or path if not the same as model_name"       )
           parser.add_argument(
               "--tokenizer_name"					,					default=lowercase__					,					type=lowercase__					,					help="Pretrained tokenizer name or path if not the same as model_name"					,					)
           parser.add_argument(
               "--cache_dir"					,					default=""					,					type=lowercase__					,					help="Where do you want to store the pre-trained models downloaded from huggingface.co"					,					)
           parser.add_argument(
               "--encoder_layerdrop"					,					type=lowercase__					,					help="Encoder layer dropout probability (Optional). Goes into model.config"					,					)
           parser.add_argument(
               "--decoder_layerdrop"					,					type=lowercase__					,					help="Decoder layer dropout probability (Optional). Goes into model.config"					,					)
           parser.add_argument(
               "--dropout"					,					type=lowercase__					,					help="Dropout probability (Optional). Goes into model.config"					,					)
           parser.add_argument(
               "--attention_dropout"					,					type=lowercase__					,					help="Attention dropout probability (Optional). Goes into model.config"					,					)
           parser.add_argument("--learning_rate"					,					default=5e-5					,					type=lowercase__					,					help="The initial learning rate for Adam."       )
           parser.add_argument(
               "--lr_scheduler"					,					default="linear"					,					choices=lowercase__					,					metavar=lowercase__					,					type=lowercase__					,					help="Learning rate scheduler"					,					)
           parser.add_argument("--weight_decay"					,					default=0.0					,					type=lowercase__					,					help="Weight decay if we apply some."       )
           parser.add_argument("--adam_epsilon"					,					default=1e-8					,					type=lowercase__					,					help="Epsilon for Adam optimizer."       )
           parser.add_argument("--warmup_steps"					,					default=0					,					type=lowercase__					,					help="Linear warmup over warmup_steps."       )
           parser.add_argument("--num_workers"					,					default=4					,					type=lowercase__					,					help="kwarg passed to DataLoader"       )
           parser.add_argument("--num_train_epochs"					,					dest="max_epochs"					,					default=3					,					type=lowercase__       )
           parser.add_argument("--train_batch_size"					,					default=32					,					type=lowercase__       )
           parser.add_argument("--eval_batch_size"					,					default=32					,					type=lowercase__       )
           parser.add_argument("--adafactor"					,					action="store_true"       )







class        a__     (      pl.Callback    ):
     '''simple docstring'''







     def      _UpperCamelCase (self     :						str					,					lowercase__     :						int					,					lowercase__     :						Optional[Any]       )	->							Dict:
           if (
               trainer.is_global_zero and trainer.global_rank == 0
           ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
                 pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class        a__     (      pl.Callback    ):
     '''simple docstring'''







     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						List[str]					,					lowercase__     :						Union[str, Any]       )	->							Optional[Any]:
           __A		 =   trainer.lr_schedulers[0]["scheduler"]
           __A		 =   {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
           pl_module.logger.log_metrics(lowercase__       )






     def      _UpperCamelCase (self     :						List[str]					,					lowercase__     :						pl.Trainer					,					lowercase__     :						pl.LightningModule       )	->							Dict:
           rank_zero_info("***** Validation results *****"       )
           __A		 =   trainer.callback_metrics
           # Log results
           for key in sorted(lowercase__       ):
                 if key not in ["log", "progress_bar"]:
                       rank_zero_info("{} = {}\n".format(lowercase__					,					str(metrics[key]       )       )       )






     def      _UpperCamelCase (self     :						Optional[int]					,					lowercase__     :						pl.Trainer					,					lowercase__     :						pl.LightningModule       )	->							List[Any]:
           rank_zero_info("***** Test results *****"       )
           __A		 =   trainer.callback_metrics
           # Log and save results to file
           __A		 =   os.path.join(pl_module.hparams.output_dir					,					"test_results.txt"       )
           with open(lowercase__					,					"w"       ) as writer:
                 for key in sorted(lowercase__       ):
                       if key not in ["log", "progress_bar"]:
                             rank_zero_info("{} = {}\n".format(lowercase__					,					str(metrics[key]       )       )       )
                             writer.write("{} = {}\n".format(lowercase__					,					str(metrics[key]       )       )       )







def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      #  To allow all pl args uncomment the following line
      #  parser = pl.Trainer.add_argparse_args(parser)
      parser.add_argument(
          "--output_dir"				,      default=snake_case__				,      type=snake_case__				,      required=snake_case__				,      help="The output directory where the model predictions and checkpoints will be written."				,      )
      parser.add_argument(
          "--fp16"				,      action="store_true"				,      help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"				,      )

      parser.add_argument(
          "--fp16_opt_level"				,      type=snake_case__				,      default="O2"				,      help=(
              "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
              "See details at https://nvidia.github.io/apex/amp.html"
          )				,      )
      parser.add_argument("--n_tpu_cores"				,      dest="tpu_cores"				,      type=snake_case__				)
      parser.add_argument("--max_grad_norm"				,      dest="gradient_clip_val"				,      default=1.0				,      type=snake_case__				,      help="Max gradient norm"				)
      parser.add_argument("--do_train"				,      action="store_true"				,      help="Whether to run training."				)
      parser.add_argument("--do_predict"				,      action="store_true"				,      help="Whether to run predictions on the test set."				)
      parser.add_argument(
          "--gradient_accumulation_steps"				,      dest="accumulate_grad_batches"				,      type=snake_case__				,      default=1				,      help="Number of updates steps to accumulate before performing a backward/update pass."				,      )
      parser.add_argument("--seed"				,      type=snake_case__				,      default=42				,      help="random seed for initialization"				)
      parser.add_argument(
          "--data_dir"				,      default=snake_case__				,      type=snake_case__				,      required=snake_case__				,      help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."				,      )





def     _UpperCamelCase				(  snake_case__				,      snake_case__				,      snake_case__=None				,      snake_case__=True				,      snake_case__=None				,      snake_case__=[]				,      snake_case__=None				,      snake_case__=None				,      **snake_case__				,      ):
      pl.seed_everything(args.seed				)

      # init model
      __A		 =   Path(model.hparams.output_dir				)
      odir.mkdir(exist_ok=snake_case__				)

      # add custom checkpoints
      if checkpoint_callback is None:
            __A		 =   pl.callbacks.ModelCheckpoint(
                filepath=args.output_dir				,      prefix="checkpoint"				,      monitor="val_loss"				,      mode="min"				,      save_top_k=1				)
      if early_stopping_callback:
            extra_callbacks.append(snake_case__				)
      if logging_callback is None:
            __A		 =   LoggingCallback()

      __A		 =   {}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            __A		 =   16
            # train_params["amp_level"] = args.fp16_opt_level

      if args.gpus > 1:
            __A		 =   "auto"  # "ddp"
            __A		 =   "ddp"

      __A		 =   args.accumulate_grad_batches
      __A		 =   None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
      __A		 =   "auto"

      __A		 =   pl.Trainer.from_argparse_args(
          snake_case__				,      weights_summary=snake_case__				,      callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]				,      logger=snake_case__				,      **snake_case__				,      )

      if args.do_train:
            trainer.fit(snake_case__				)

      return trainer










'''simple docstring'''







from __future__ import annotations


def     _UpperCamelCase				(  snake_case__				):
      __A		 =   [0] * len(snake_case__				)
      for i in range(1				,      len(snake_case__				)				):
            __A		 =   duration_times[i - 1] + waiting_times[i - 1]
      return waiting_times


def     _UpperCamelCase				(  snake_case__				,      snake_case__				):
      return [
          duration_time + waiting_times[i]
          for i, duration_time in enumerate(snake_case__				)
      ]


def     _UpperCamelCase				(  snake_case__				):
      return sum(snake_case__				) / len(snake_case__				)


def     _UpperCamelCase				(  snake_case__				):
      return sum(snake_case__				) / len(snake_case__				)


if __name__ == "__main__":
       # process id's
       __A   :							List[str]     	=						[1, 2, 3]

       # ensure that we actually have processes
       if len(processes) == 0:
              print('Zero amount of processes')
              raise SystemExit(0)

       # duration time of all processes
       __A   :							List[Any]     	=						[1_9, 8, 9]

       # ensure we can match each id to a duration time
       if len(duration_times) != len(processes):
              print('Unable to match all id\'s with their duration time')
              raise SystemExit(0)

       # get the waiting times and the turnaround times
       __A   :							Dict     	=						calculate_waiting_times(duration_times)
       __A   :							Optional[int]     	=						calculate_turnaround_times(duration_times, waiting_times)

       # get the average times
       __A   :							Dict     	=						calculate_average_waiting_time(waiting_times)
       __A   :							Tuple     	=						calculate_average_turnaround_time(turnaround_times)

       # print all the results
       print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
       for i, process in enumerate(processes):
              print(
                  F"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
                  F"""{turnaround_times[i]}"""
              )
       print(F"""Average waiting time = {average_waiting_time}""")
       print(F"""Average turn around time = {average_turnaround_time}""")


