






'''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							:							List[str]  							=		logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

__A							:							str  							=		{
    """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							:							List[str]  							=		{
    """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[int]  							=		sorted(arg_to_scheduler.keys())
__A							:							List[Any]  							=		"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""
class 				lowercase       (							pl.LightningModule      ):


    '''simple docstring'''

    def __init__(					self     :     Dict				,     __lowerCamelCase     :     argparse.Namespace				,     __lowerCamelCase     :     List[Any]=None				,     __lowerCamelCase     :     Any="base"				,     __lowerCamelCase     :     str=None				,     __lowerCamelCase     :     Optional[int]=None				,     __lowerCamelCase     :     Dict=None				,     **__lowerCamelCase     :     Tuple				,     )							->							Dict:

     '''simple docstring'''




     super().__init__()
     # TODO: move to self.save_hyperparameters()
     # self.save_hyperparameters()
     # can also expand arguments into trainer signature for easier reading

     self.save_hyperparameters(__lowerCamelCase     )
     lowerCamelCase__  =     0
     lowerCamelCase__  =     Path(self.hparams.output_dir     )
     lowerCamelCase__  =     self.hparams.cache_dir if self.hparams.cache_dir else None
     if config is None:
      lowerCamelCase__  =     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=__lowerCamelCase				,     **__lowerCamelCase				,     )
     else:
      lowerCamelCase__  =     config

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

     if tokenizer is None:
      lowerCamelCase__  =     AutoTokenizer.from_pretrained(
          self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,     cache_dir=__lowerCamelCase				,     )
     else:
      lowerCamelCase__  =     tokenizer
     lowerCamelCase__  =     MODEL_MODES[mode]
     if model is None:
      lowerCamelCase__  =     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=__lowerCamelCase				,     )
     else:
      lowerCamelCase__  =     model

    def    a__				(					self     :     Union[str, Any]				,     *__lowerCamelCase     :     Optional[Any]				,     **__lowerCamelCase     :     Union[str, Any]     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     self.model_type.from_pretrained(*__lowerCamelCase				,     **__lowerCamelCase     )

    def    a__				(					self     :     int     )							->							List[str]:

     '''simple docstring'''




     lowerCamelCase__  =     arg_to_scheduler[self.hparams.lr_scheduler]
     lowerCamelCase__  =     get_schedule_func(
         self.opt				,     num_warmup_steps=self.hparams.warmup_steps				,     num_training_steps=self.total_steps()     )
     lowerCamelCase__  =     {"scheduler": scheduler, "interval": "step", "frequency": 1}
     return scheduler

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

     '''simple docstring'''




     lowerCamelCase__  =     self.model
     lowerCamelCase__  =     ["bias", "LayerNorm.weight"]
     lowerCamelCase__  =     [
         {
             "params": [
                 p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay     )
             ],  # check this named paramters
             "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:
      lowerCamelCase__  =     Adafactor(
          __lowerCamelCase				,     lr=self.hparams.learning_rate				,     scale_parameter=__lowerCamelCase				,     relative_step=__lowerCamelCase     )

     else:
      lowerCamelCase__  =     AdamW(
          __lowerCamelCase				,     lr=self.hparams.learning_rate				,     eps=self.hparams.adam_epsilon     )
     lowerCamelCase__  =     optimizer

     lowerCamelCase__  =     self.get_lr_scheduler()

     return [optimizer], [scheduler]

    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     int     )							->							int:

     '''simple docstring'''




     return self.validation_step(__lowerCamelCase				,     __lowerCamelCase     )

    def    a__				(					self     :     str				,     __lowerCamelCase     :     Dict     )							->							Optional[Any]:

     '''simple docstring'''




     return self.validation_end(__lowerCamelCase     )

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

     '''simple docstring'''




     lowerCamelCase__  =     max(1				,     self.hparams.gpus     )  # TODO: consider num_tpu_cores
     lowerCamelCase__  =     self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
     return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Dict     )							->							Tuple:

     '''simple docstring'''




     if stage == "test":
      lowerCamelCase__  =     len(self.test_dataloader().dataset     )
     else:
      lowerCamelCase__  =     self.get_dataloader("train"				,     self.hparams.train_batch_size				,     shuffle=__lowerCamelCase     )
      lowerCamelCase__  =     len(self.train_dataloader().dataset     )

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     int				,     __lowerCamelCase     :     bool = False     )							->							Optional[Any]:

     '''simple docstring'''




     raise NotImplementedError("You must implement this for your task"     )

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

     '''simple docstring'''




     return self.train_loader

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

     '''simple docstring'''




     return self.get_dataloader("dev"				,     self.hparams.eval_batch_size				,     shuffle=__lowerCamelCase     )

    def    a__				(					self     :     Optional[int]     )							->							Dict:

     '''simple docstring'''




     return self.get_dataloader("test"				,     self.hparams.eval_batch_size				,     shuffle=__lowerCamelCase     )

    def    a__				(					self     :     int				,     __lowerCamelCase     :     Optional[int]     )							->							Dict:

     '''simple docstring'''




     return os.path.join(
         self.hparams.data_dir				,     "cached_{}_{}_{}".format(
             __lowerCamelCase				,     list(filter(__lowerCamelCase				,     self.hparams.model_name_or_path.split("/"     )     )     ).pop()				,     str(self.hparams.max_seq_length     )				,     )				,     )

    @pl.utilities.rank_zero_only
    def    a__				(					self     :     Any				,     __lowerCamelCase     :     Dict[str, Any]     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     self.output_dir.joinpath("best_tfmr"     )
     lowerCamelCase__  =     self.step_count
     self.model.save_pretrained(__lowerCamelCase     )
     self.tokenizer.save_pretrained(__lowerCamelCase     )




    @staticmethod
    def    a__				(					__lowerCamelCase     :     Tuple				,     __lowerCamelCase     :     int     )							->							Optional[Any]:

     '''simple docstring'''




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


    '''simple docstring'''

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Any     )							->							str:

     '''simple docstring'''




     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 				lowercase       (							pl.Callback      ):


    '''simple docstring'''

    def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     str     )							->							Optional[Any]:

     '''simple docstring'''




     for name, param in pl_module.model.rag.named_parameters():
      if param.grad is None:
       print(__lowerCamelCase     )
class 				lowercase       (							pl.Callback      ):


    '''simple docstring'''

    def    a__				(					self     :     Dict				,     __lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     Optional[Any]     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     trainer.lr_schedulers[0]["scheduler"]
     lowerCamelCase__  =     {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
     pl_module.logger.log_metrics(__lowerCamelCase     )

    def    a__				(					self     :     str				,     __lowerCamelCase     :     pl.Trainer				,     __lowerCamelCase     :     pl.LightningModule     )							->							Any:

     '''simple docstring'''




     rank_zero_info("***** Validation results *****"     )
     lowerCamelCase__  =     trainer.callback_metrics
     # Log results
     for key in sorted(__lowerCamelCase     ):
      if key not in ["log", "progress_bar"]:
       rank_zero_info("{} = {}\n".format(__lowerCamelCase				,     str(metrics[key]     )     )     )




    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     pl.Trainer				,     __lowerCamelCase     :     pl.LightningModule     )							->							List[str]:

     '''simple docstring'''




     rank_zero_info("***** Test results *****"     )
     lowerCamelCase__  =     trainer.callback_metrics
     # Log and save results to file
     lowerCamelCase__  =     os.path.join(pl_module.hparams.output_dir				,     "test_results.txt"     )
     with open(__lowerCamelCase				,     "w"     ) as writer:
      for key in sorted(__lowerCamelCase     ):
       if key not in ["log", "progress_bar"]:
        rank_zero_info("{} = {}\n".format(__lowerCamelCase				,     str(metrics[key]     )     )     )
        writer.write("{} = {}\n".format(__lowerCamelCase				,     str(metrics[key]     )     )     )






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 #  To allow all pl args uncomment the following line
 #  parser = pl.Trainer.add_argparse_args(parser)
 parser.add_argument(
     "--output_dir"				,     default=str(Path(lowercase__).parent / "test_run" / "model_checkpoints")				,     type=lowercase__				,     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=lowercase__				,     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=lowercase__)
 parser.add_argument("--max_grad_norm"				,     dest="gradient_clip_val"				,     default=1.0				,     type=lowercase__				,     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=lowercase__				,     default=1				,     help="Number of updates steps to accumulate before performing a backward/update pass."				,     )
 parser.add_argument("--seed"				,     type=lowercase__				,     default=42				,     help="random seed for initialization")
 parser.add_argument(
     "--data_dir"				,     default=str(Path(lowercase__).parent / "test_run" / "dummy-train-data")				,     type=lowercase__				,     help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."				,     )






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

 # init model
 lowerCamelCase__  =     Path(model.hparams.output_dir)
 odir.mkdir(exist_ok=lowercase__)

 # add custom checkpoints
 if checkpoint_callback is None:
  lowerCamelCase__  =     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(lowercase__)
 if logging_callback is None:
  lowerCamelCase__  =     LoggingCallback()

 lowerCamelCase__  =     {}

 if args.fpaa:
  lowerCamelCase__  =     16

 if args.gpus > 1:
  lowerCamelCase__  =     "auto"
  lowerCamelCase__  =     "ddp"

 lowerCamelCase__  =     args.accumulate_grad_batches
 lowerCamelCase__  =     None
 lowerCamelCase__  =     "auto"

 lowerCamelCase__  =     pl.Trainer.from_argparse_args(
     lowercase__				,     weights_summary=lowercase__				,     callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]				,     logger=lowercase__				,     val_check_interval=1				,     num_sanity_val_steps=2				,     **lowercase__				,     )

 if args.do_train:
  trainer.fit(lowercase__)

 else:
  print("RAG modeling tests with new set functions successfuly executed!")
 return trainer









'''simple docstring'''





from decimal import Decimal, getcontext
from math import ceil, factorial






def    lowerCamelCase_     (    lowercase__):

 if not isinstance(lowercase__				,     lowercase__):
  raise TypeError("Undefined for non-integers")
 elif precision < 1:
  raise ValueError("Undefined for non-natural numbers")

 lowerCamelCase__  =     precision
 lowerCamelCase__  =     ceil(precision / 14)
 lowerCamelCase__  =     426880 * Decimal(10005).sqrt()
 lowerCamelCase__  =     1
 lowerCamelCase__  =     13591409
 lowerCamelCase__  =     Decimal(lowercase__)
 for k in range(1				,     lowercase__):
  lowerCamelCase__  =     factorial(6 * k) // (factorial(3 * k) * factorial(lowercase__) ** 3)
  linear_term += 545140134
  exponential_term *= -262537412640768000
  partial_sum += Decimal(multinomial_term * linear_term) / exponential_term
 return str(constant_term / partial_sum)[:-1]


if __name__ == "__main__":
 __A							:							Dict  							=		50
 print(F"""The first {n} digits of pi is: {pi(n)}""")

