


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


lowerCamelCase		      = logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCamelCase		      = {
    '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
lowerCamelCase		      = {
    '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
}
lowerCamelCase		      = sorted(arg_to_scheduler.keys())
lowerCamelCase		      = '{' + ', '.join(arg_to_scheduler_choices) + '}'




class A						(     pl.LightningModule  ):






      def __init__(      self		:  Any    ,     lowercase_		:  argparse.Namespace    ,     lowercase_		:  int=None    ,     lowercase_		:  List[str]="base"    ,     lowercase_		:  Union[str, Any]=None    ,     lowercase_		:  Dict=None    ,     lowercase_		:  Tuple=None    ,     **lowercase_		:  Tuple    ,     )      ->		Union[str, Any]:
             """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(lowercase_				)
             _lowerCamelCase :	str						=0
             _lowerCamelCase :	List[Any]						=Path(self.hparams.output_dir				)
             _lowerCamelCase :	List[str]						=self.hparams.cache_dir if self.hparams.cache_dir else None
             if config is None:
                    _lowerCamelCase :	Optional[Any]						=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:
                    _lowerCamelCase :	PretrainedConfig						=config

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






      def  lowerCamelCase (      self		:  List[str]    ,     *lowercase_		:  Union[str, Any]    ,     **lowercase_		:  int				)      ->		Dict:
             """simple docstring"""




             _lowerCamelCase :	Dict						=self.model_type.from_pretrained(*lowercase_    ,     **lowercase_				)






      def  lowerCamelCase (      self		:  Dict				)      ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase :	List[str]						=arg_to_scheduler[self.hparams.lr_scheduler]
             _lowerCamelCase :	Optional[Any]						=get_schedule_func(
                 self.opt    ,     num_warmup_steps=self.hparams.warmup_steps    ,     num_training_steps=self.total_steps()				)
             _lowerCamelCase :	int						={'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
             return scheduler






      def  lowerCamelCase (      self		:  List[Any]				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	str						=self.model
             _lowerCamelCase :	Optional[Any]						=['bias', 'LayerNorm.weight']
             _lowerCamelCase :	List[Any]						=[
                 {
                     '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:
                    _lowerCamelCase :	Optional[Any]						=Adafactor(
                        lowercase_    ,     lr=self.hparams.learning_rate    ,     scale_parameter=lowercase_    ,     relative_step=lowercase_				)

             else:
                    _lowerCamelCase :	Tuple						=AdamW(
                        lowercase_    ,     lr=self.hparams.learning_rate    ,     eps=self.hparams.adam_epsilon				)
             _lowerCamelCase :	List[str]						=optimizer

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

             return [optimizer], [scheduler]






      def  lowerCamelCase (      self		:  List[str]    ,     lowercase_		:  List[Any]    ,     lowercase_		:  Optional[int]				)      ->		Any:
             """simple docstring"""




             return self.validation_step(lowercase_    ,     lowercase_				)






      def  lowerCamelCase (      self		:  Dict    ,     lowercase_		:  str				)      ->		Optional[int]:
             """simple docstring"""




             return self.validation_end(lowercase_				)






      def  lowerCamelCase (      self		:  Union[str, Any]				)      ->		int:
             """simple docstring"""




             _lowerCamelCase :	Dict						=max(1    ,     self.hparams.gpus				)  # TODO: consider num_tpu_cores
             _lowerCamelCase :	Union[str, Any]						=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		:  List[Any]    ,     lowercase_		:  str				)      ->		Optional[int]:
             """simple docstring"""




             if mode == "test":
                    _lowerCamelCase :	Dict						=len(self.test_dataloader().dataset				)
             else:
                    _lowerCamelCase :	Optional[int]						=self.get_dataloader('train'    ,     self.hparams.train_batch_size    ,     shuffle=lowercase_				)
                    _lowerCamelCase :	List[str]						=len(self.train_dataloader().dataset				)






      def  lowerCamelCase (      self		:  Any    ,     lowercase_		:  str    ,     lowercase_		:  int    ,     lowercase_		:  bool = False				)      ->		List[Any]:
             """simple docstring"""




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






      def  lowerCamelCase (      self		:  int				)      ->		Union[str, Any]:
             """simple docstring"""




             return self.train_loader






      def  lowerCamelCase (      self		:  Any				)      ->		Optional[int]:
             """simple docstring"""




             return self.get_dataloader('dev'    ,     self.hparams.eval_batch_size    ,     shuffle=lowercase_				)






      def  lowerCamelCase (      self		:  List[Any]				)      ->		Union[str, Any]:
             """simple docstring"""




             return self.get_dataloader('test'    ,     self.hparams.eval_batch_size    ,     shuffle=lowercase_				)






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  Any				)      ->		Dict:
             """simple docstring"""




             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  lowerCamelCase (      self		:  Optional[int]    ,     lowercase_		:  Dict[str, Any]				)      ->		None:
             """simple docstring"""




             _lowerCamelCase :	List[Any]						=self.output_dir.joinpath('best_tfmr'				)
             _lowerCamelCase :	Optional[int]						=self.step_count
             self.model.save_pretrained(lowercase_				)
             self.tokenizer.save_pretrained(lowercase_				)






      @staticmethod
      def  lowerCamelCase (      lowercase_		:  Optional[int]    ,     lowercase_		:  List[str]				)      ->		Optional[int]:
             """simple docstring"""




             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  ):






      def  lowerCamelCase (      self		:  int    ,     lowercase_		:  Optional[int]    ,     lowercase_		:  Optional[Any]				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=trainer.lr_schedulers[0]['scheduler']
             _lowerCamelCase :	str						={F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
             pl_module.logger.log_metrics(lowercase_				)






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  pl.Trainer    ,     lowercase_		:  pl.LightningModule				)      ->		Optional[Any]:
             """simple docstring"""




             rank_zero_info('***** Validation results *****'				)
             _lowerCamelCase :	List[str]						=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  lowerCamelCase (      self		:  List[Any]    ,     lowercase_		:  pl.Trainer    ,     lowercase_		:  pl.LightningModule				)      ->		List[Any]:
             """simple docstring"""




             rank_zero_info('***** Test results *****'				)
             _lowerCamelCase :	int						=trainer.callback_metrics
             # Log and save results to file
             _lowerCamelCase :	List[str]						=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 			a_   (					SCREAMING_SNAKE_CASE__      :   Tuple	,    SCREAMING_SNAKE_CASE__      :   Dict  ):
       '''simple docstring'''





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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   BaseTransformer	,    SCREAMING_SNAKE_CASE__      :   argparse.Namespace	,    SCREAMING_SNAKE_CASE__      :   Optional[Any]=None	,    SCREAMING_SNAKE_CASE__      :   Optional[int]=True	,    SCREAMING_SNAKE_CASE__      :   Optional[int]=[]	,    SCREAMING_SNAKE_CASE__      :   Tuple=None	,    SCREAMING_SNAKE_CASE__      :   Any=None	,    **SCREAMING_SNAKE_CASE__      :   Any	,    ):
       '''simple docstring'''





       pl.seed_everything(args.seed  )

       # init model
       _lowerCamelCase :	Any						=Path(model.hparams.output_dir  )
       odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__  )

       # add custom checkpoints
       if checkpoint_callback is None:
              _lowerCamelCase :	Any						=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(SCREAMING_SNAKE_CASE__  )
       if logging_callback is None:
              _lowerCamelCase :	Optional[Any]						=LoggingCallback()

       _lowerCamelCase :	Optional[int]						={}

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

       if args.gpus > 1:
              _lowerCamelCase :	Union[str, Any]						='ddp'

       _lowerCamelCase :	Dict						=args.accumulate_grad_batches
       _lowerCamelCase :	List[Any]						=extra_train_kwargs.get('accelerator'	,    SCREAMING_SNAKE_CASE__  )
       _lowerCamelCase :	Any						=extra_train_kwargs.get('profiler'	,    SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Any						=pl.Trainer.from_argparse_args(
           SCREAMING_SNAKE_CASE__	,    weights_summary=SCREAMING_SNAKE_CASE__	,    callbacks=[logging_callback] + extra_callbacks	,    logger=SCREAMING_SNAKE_CASE__	,    checkpoint_callback=SCREAMING_SNAKE_CASE__	,    **SCREAMING_SNAKE_CASE__	,    )

       if args.do_train:
              trainer.fit(SCREAMING_SNAKE_CASE__  )

       return trainer






from __future__ import annotations

from collections.abc import Generator




def 			a_   (					SCREAMING_SNAKE_CASE__      :   int  ):
       '''simple docstring'''





       if not isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ) or n < 1:
              raise Exception('Sequence only defined for positive integers'  )

       yield n
       while n != 1:
              if n % 2 == 0:
                     n //= 2
              else:
                     _lowerCamelCase :	Optional[int]						=3 * n + 1
              yield n




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=43
       _lowerCamelCase :	Dict						=tuple(collatz_sequence(SCREAMING_SNAKE_CASE__  )  )
       print(SCREAMING_SNAKE_CASE__  )
       print(F'''Collatz sequence from {n} took {len(SCREAMING_SNAKE_CASE__  )} steps.'''  )


if __name__ == "__main__":
 main()


