
"""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__   :						int 							=     logging.getLogger(__name__)

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

A__   :						Dict 							=     {
    """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__   :						int 							=     {
    """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__   :						str 							=     sorted(arg_to_scheduler.keys())
A__   :						List[str] 							=     """{""" + """, """.join(arg_to_scheduler_choices) + """}"""







class 	_lowercase    (							pl.LightningModule					):


     '''simple docstring'''





     def __init__(   self			,      __UpperCamelCase			,      __UpperCamelCase=None			,      __UpperCamelCase="base"			,      __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=None			,      **__UpperCamelCase			,      )->       Dict:
          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(UpperCamelCase__	)
          UpperCAmelCase__				:			List[str]							=    0
          UpperCAmelCase__				:			Optional[Any]							=    Path(self.hparams.output_dir	)
          UpperCAmelCase__				:			Any							=    self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
               UpperCAmelCase__				:			Optional[int]							=    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=UpperCamelCase__			,      **UpperCamelCase__			,      )
          else:
               UpperCAmelCase__				:			PretrainedConfig							=    config

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

          if tokenizer is None:
               UpperCAmelCase__				:			int							=    AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,      cache_dir=UpperCamelCase__			,      )
          else:
               UpperCAmelCase__				:			PreTrainedTokenizer							=    tokenizer
          UpperCAmelCase__				:			Tuple							=    MODEL_MODES[mode]
          if model is None:
               UpperCAmelCase__				:			int							=    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=UpperCamelCase__			,      )
          else:
               UpperCAmelCase__				:			int							=    model



     def 			lowerCAmelCase__							(   self			,      *__UpperCamelCase			,      **__UpperCamelCase	)->       List[str]:
          UpperCAmelCase__				:			Union[str, Any]							=    self.model_type.from_pretrained(*UpperCamelCase__			,      **UpperCamelCase__	)



     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          UpperCAmelCase__				:			Optional[int]							=    arg_to_scheduler[self.hparams.lr_scheduler]
          UpperCAmelCase__				:			Tuple							=    get_schedule_func(
              self.opt			,      num_warmup_steps=self.hparams.warmup_steps			,      num_training_steps=self.total_steps()	)
          UpperCAmelCase__				:			int							=    {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
          return scheduler



     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          UpperCAmelCase__				:			List[Any]							=    self.model
          UpperCAmelCase__				:			List[Any]							=    ['bias', 'LayerNorm.weight']
          UpperCAmelCase__				:			str							=    [
              {
                  '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:
               UpperCAmelCase__				:			str							=    Adafactor(
                   UpperCamelCase__			,      lr=self.hparams.learning_rate			,      scale_parameter=UpperCamelCase__			,      relative_step=UpperCamelCase__	)

          else:
               UpperCAmelCase__				:			Optional[int]							=    AdamW(
                   UpperCamelCase__			,      lr=self.hparams.learning_rate			,      eps=self.hparams.adam_epsilon	)
          UpperCAmelCase__				:			List[str]							=    optimizer

          UpperCAmelCase__				:			Optional[Any]							=    self.get_lr_scheduler()

          return [optimizer], [scheduler]



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase	)->       Any:
          return self.validation_step(UpperCamelCase__			,      UpperCamelCase__	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       Any:
          return self.validation_end(UpperCamelCase__	)



     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          UpperCAmelCase__				:			Optional[Any]							=    max(1			,      self.hparams.gpus	)  # TODO: consider num_tpu_cores
          UpperCAmelCase__				:			str							=    self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
          return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       Optional[int]:
          if mode == "test":
               UpperCAmelCase__				:			str							=    len(self.test_dataloader().dataset	)
          else:
               UpperCAmelCase__				:			Any							=    self.get_dataloader("train"			,      self.hparams.train_batch_size			,      shuffle=UpperCamelCase__	)
               UpperCAmelCase__				:			int							=    len(self.train_dataloader().dataset	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase = False	)->       Any:
          raise NotImplementedError("You must implement this for your task"	)



     def 			lowerCAmelCase__							(   self	)->       List[str]:
          return self.train_loader



     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          return self.get_dataloader("dev"			,      self.hparams.eval_batch_size			,      shuffle=UpperCamelCase__	)



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



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



     @pl.utilities.rank_zero_only
     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       List[str]:
          UpperCAmelCase__				:			List[str]							=    self.output_dir.joinpath("best_tfmr"	)
          UpperCAmelCase__				:			Dict							=    self.step_count
          self.model.save_pretrained(UpperCamelCase__	)
          self.tokenizer.save_pretrained(UpperCamelCase__	)



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







class 	_lowercase    (							pl.Callback					):


     '''simple docstring'''





     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase	)->       List[Any]:
          UpperCAmelCase__				:			Tuple							=    trainer.lr_schedulers[0]['scheduler']
          UpperCAmelCase__				:			Union[str, Any]							=    {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
          pl_module.logger.log_metrics(UpperCamelCase__	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase	)->       List[str]:
          rank_zero_info("***** Validation results *****"	)
          UpperCAmelCase__				:			str							=    trainer.callback_metrics
          # Log results
          for key in sorted(UpperCamelCase__	):
               if key not in ["log", "progress_bar"]:
                    rank_zero_info("{} = {}\n".format(UpperCamelCase__			,      str(metrics[key]	)	)	)



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





def 				a__			(					lowerCAmelCase						:							Union[str, Any]							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     parser.add_argument(
         "--output_dir"							,     default=lowerCAmelCase__							,     type=lowerCAmelCase__							,     required=lowerCAmelCase__							,     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=lowerCAmelCase__							,     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=lowerCAmelCase__		)
     parser.add_argument("--max_grad_norm"							,     dest="gradient_clip_val"							,     default=1.0							,     type=lowerCAmelCase__							,     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=lowerCAmelCase__							,     default=1							,     help="Number of updates steps to accumulate before performing a backward/update pass."							,     )
     parser.add_argument("--seed"							,     type=lowerCAmelCase__							,     default=42							,     help="random seed for initialization"		)
     parser.add_argument(
         "--data_dir"							,     default=lowerCAmelCase__							,     type=lowerCAmelCase__							,     required=lowerCAmelCase__							,     help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."							,     )



def 				a__			(					lowerCAmelCase						:							BaseTransformer							,     lowerCAmelCase						:							argparse.Namespace							,     lowerCAmelCase						:							str=None							,     lowerCAmelCase						:							Optional[int]=True							,     lowerCAmelCase						:							Tuple=[]							,     lowerCAmelCase						:							Tuple=None							,     lowerCAmelCase						:							int=None							,     **lowerCAmelCase						:							Optional[int]							,     ):






     '''simple docstring'''
     pl.seed_everything(args.seed		)

     # init model
     UpperCAmelCase__				:			List[Any]							=    Path(model.hparams.output_dir		)
     odir.mkdir(exist_ok=lowerCAmelCase__		)

     # add custom checkpoints
     if checkpoint_callback is None:
          UpperCAmelCase__				:			int							=    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(lowerCAmelCase__		)
     if logging_callback is None:
          UpperCAmelCase__				:			Any							=    LoggingCallback()

     UpperCAmelCase__				:			List[str]							=    {}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          UpperCAmelCase__				:			List[Any]							=    16
          UpperCAmelCase__				:			str							=    args.fpaa_opt_level

     if args.gpus > 1:
          UpperCAmelCase__				:			Dict							=    'ddp'

     UpperCAmelCase__				:			Optional[int]							=    args.accumulate_grad_batches
     UpperCAmelCase__				:			Optional[Any]							=    extra_train_kwargs.get("accelerator"							,     lowerCAmelCase__		)
     UpperCAmelCase__				:			List[Any]							=    extra_train_kwargs.get("profiler"							,     lowerCAmelCase__		)

     UpperCAmelCase__				:			str							=    pl.Trainer.from_argparse_args(
         lowerCAmelCase__							,     weights_summary=lowerCAmelCase__							,     callbacks=[logging_callback] + extra_callbacks							,     logger=lowerCAmelCase__							,     checkpoint_callback=lowerCAmelCase__							,     **lowerCAmelCase__							,     )

     if args.do_train:
          trainer.fit(lowerCAmelCase__		)

     return trainer











def        _lowerCamelCase(      lowerCAmelCase__					:		float     ,     lowerCAmelCase__					:		float     ,     lowerCAmelCase__					:		float   ):





				'''simple docstring'''







				if mass < 0:
								raise ValueError('The mass of the body cannot be negative'   )
				if radius <= 0:
								raise ValueError('The radius is always a positive non zero integer'   )
				return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)