






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_	:	Union[str, Any]				=				logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

A_	:	List[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_	:	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_	:	Tuple				=				sorted(arg_to_scheduler.keys())
A_	:	Any				=				'{' + ', '.join(arg_to_scheduler_choices) + '}'




class        _lowerCAmelCase( pl.LightningModule      ):





       """simple docstring"""







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

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





       def     _a					(      self						,    *_lowerCamelCase						,    **_lowerCamelCase							):
         UpperCamelCase_:      int					   =       self.model_type.from_pretrained(*_lowerCamelCase						,    **_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_:      Union[str, Any]					   =       arg_to_scheduler[self.hparams.lr_scheduler]
         UpperCamelCase_:      Dict					   =       get_schedule_func(
             self.opt						,    num_warmup_steps=self.hparams.warmup_steps						,    num_training_steps=self.total_steps()							)
         UpperCamelCase_:      Tuple					   =       {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
         return scheduler





       def     _a					(      self							):
         UpperCamelCase_:      List[Any]					   =       self.model
         UpperCamelCase_:      Optional[int]					   =       ['bias', 'LayerNorm.weight']
         UpperCamelCase_:      Tuple					   =       [
             {
                 '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_:      List[Any]					   =       Adafactor(
               _lowerCamelCase						,    lr=self.hparams.learning_rate						,    scale_parameter=_lowerCamelCase						,    relative_step=_lowerCamelCase							)

         else:
           UpperCamelCase_:      Any					   =       AdamW(
               _lowerCamelCase						,    lr=self.hparams.learning_rate						,    eps=self.hparams.adam_epsilon							)
         UpperCamelCase_:      Optional[Any]					   =       optimizer

         UpperCamelCase_:      Tuple					   =       self.get_lr_scheduler()

         return [optimizer], [scheduler]





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase							):
         return self.validation_step(_lowerCamelCase						,    _lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase							):
         return self.validation_end(_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_:      str					   =       max(1						,    self.hparams.gpus							)  # TODO: consider num_tpu_cores
         UpperCamelCase_:      List[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     _a					(      self						,    _lowerCamelCase							):
         if mode == "test":
           UpperCamelCase_:      Optional[int]					   =       len(self.test_dataloader().dataset							)
         else:
           UpperCamelCase_:      str					   =       self.get_dataloader('train'						,    self.hparams.train_batch_size						,    shuffle=_lowerCamelCase							)
           UpperCamelCase_:      int					   =       len(self.train_dataloader().dataset							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase = False							):
         raise NotImplementedError('You must implement this for your task'							)





       def     _a					(      self							):
         return self.train_loader





       def     _a					(      self							):
         return self.get_dataloader('dev'						,    self.hparams.eval_batch_size						,    shuffle=_lowerCamelCase							)





       def     _a					(      self							):
         return self.get_dataloader('test'						,    self.hparams.eval_batch_size						,    shuffle=_lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase							):
         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						,    _lowerCamelCase							):
         UpperCamelCase_:      Optional[Any]					   =       self.output_dir.joinpath('best_tfmr'							)
         UpperCamelCase_:      int					   =       self.step_count
         self.model.save_pretrained(_lowerCamelCase							)
         self.tokenizer.save_pretrained(_lowerCamelCase							)





       @staticmethod
       def     _a					(      _lowerCamelCase						,    _lowerCamelCase							):
         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=''						,    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=3_2						,    type=_lowerCamelCase							)
         parser.add_argument('--eval_batch_size'						,    default=3_2						,    type=_lowerCamelCase							)
         parser.add_argument('--adafactor'						,    action='store_true'							)







class        _lowerCAmelCase( pl.Callback      ):





       """simple docstring"""







       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      Dict					   =       trainer.lr_schedulers[0]['scheduler']
         UpperCamelCase_:      Optional[Any]					   =       {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
         pl_module.logger.log_metrics(_lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase							):
         rank_zero_info('***** Validation results *****'							)
         UpperCamelCase_:      str					   =       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						,    _lowerCamelCase						,    _lowerCamelCase							):
         rank_zero_info('***** Test results *****'							)
         UpperCamelCase_:      List[Any]					   =       trainer.callback_metrics
         # Log and save results to file
         UpperCamelCase_:      Tuple					   =       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 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       None:
  #  To allow all pl args uncomment the following line
  #  parser = pl.Trainer.add_argparse_args(parser)
  parser.add_argument(
      '--output_dir'			,					default=UpperCAmelCase__			,					type=UpperCAmelCase__			,					required=UpperCAmelCase__			,					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=UpperCAmelCase__			,					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=UpperCAmelCase__						)
  parser.add_argument('--max_grad_norm'			,					dest='gradient_clip_val'			,					default=1.0			,					type=UpperCAmelCase__			,					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=UpperCAmelCase__			,					default=1			,					help='Number of updates steps to accumulate before performing a backward/update pass.'			,					)
  parser.add_argument('--seed'			,					type=UpperCAmelCase__			,					default=4_2			,					help='random seed for initialization'						)
  parser.add_argument(
      '--data_dir'			,					default=UpperCAmelCase__			,					type=UpperCAmelCase__			,					required=UpperCAmelCase__			,					help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'			,					)

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__			,					UpperCAmelCase__=None			,					UpperCAmelCase__=True			,					UpperCAmelCase__=[]			,					UpperCAmelCase__=None			,					UpperCAmelCase__=None			,					**UpperCAmelCase__			,					) ->       str:
  pl.seed_everything(args.seed						)

  # init model
  UpperCamelCase_:      int					   =       Path(model.hparams.output_dir						)
  odir.mkdir(exist_ok=UpperCAmelCase__						)

  # add custom checkpoints
  if checkpoint_callback is None:
    UpperCamelCase_:      Optional[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(UpperCAmelCase__						)
  if logging_callback is None:
    UpperCamelCase_:      List[Any]					   =       LoggingCallback()

  UpperCamelCase_:      Dict					   =       {}

  # TODO: remove with PyTorch 1.6 since pl uses native amp
  if args.fpaa:
    UpperCamelCase_:      Dict					   =       1_6
    UpperCamelCase_:      List[Any]					   =       args.fpaa_opt_level

  if args.gpus > 1:
    UpperCamelCase_:      List[Any]					   =       'ddp'

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

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

  if args.do_train:
    trainer.fit(UpperCAmelCase__						)

  return trainer







# Lint as: python3

import hashlib
from typing import Union

def 						snake_case      (UpperCAmelCase__						) ->       bytes:
  if isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    # Data already in bytes, returns as it as
    return hash_data
  elif isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    # We keep the data as it as for it ot be later encoded to UTF-8
    # However replace `\\` with `/` for Windows compatibility
    UpperCamelCase_:      List[str]					   =       hash_data.replace('\\'			,					'/'						)
  elif isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    UpperCamelCase_:      Union[str, Any]					   =       str(UpperCAmelCase__						)
  else:
    # If data is not of the required type, raise error
    raise InvalidKeyError(UpperCAmelCase__						)

  return hash_data.encode('utf-8'						)




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       '\nFAILURE TO GENERATE DATASET: Invalid key type detected'
         UpperCamelCase_:      Dict					   =       f'''\nFound Key {hash_data} of type {type(_lowerCamelCase							)}'''
         UpperCamelCase_:      Any					   =       '\nKeys should be either str, int or bytes type'
         super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)







class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase=""							):
         UpperCamelCase_:      int					   =       key
         UpperCamelCase_:      Dict					   =       duplicate_key_indices
         UpperCamelCase_:      int					   =       fix_msg
         UpperCamelCase_:      List[Any]					   =       'Found multiple examples generated with the same key'
         if len(_lowerCamelCase							) <= 2_0:
           UpperCamelCase_:      Tuple					   =       f'''\nThe examples at index {', '.join(_lowerCamelCase							)} have the key {key}'''
         else:
           UpperCamelCase_:      Optional[int]					   =       f'''\nThe examples at index {', '.join(duplicate_key_indices[:2_0]							)}... ({len(_lowerCamelCase							) - 2_0} more) have the key {key}'''
         UpperCamelCase_:      List[Any]					   =       '\n' + fix_msg if fix_msg else ''
         super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)







class        _lowerCAmelCase:





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       hashlib.mda(_as_bytes(_lowerCamelCase							)							)





       def     _a					(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      Optional[int]					   =       self._split_mda.copy()
         UpperCamelCase_:      str					   =       _as_bytes(_lowerCamelCase							)
         mda.update(_lowerCamelCase							)
         # Convert to integer with hexadecimal conversion
         return int(mda.hexdigest()						,    1_6							)