
"""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


SCREAMING_SNAKE_CASE_         =  logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

SCREAMING_SNAKE_CASE_         =  {
    '''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
SCREAMING_SNAKE_CASE_         =  {
    '''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
}
SCREAMING_SNAKE_CASE_         =  sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE_         =  '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class        _UpperCAmelCase							(						pl.LightningModule							):




      def __init__(							self				,    lowercase_				,    lowercase_=None				,    lowercase_="base"				,    lowercase_=None				,    lowercase_=None				,    lowercase_=None				,    **lowercase_				,    )  ->   str:
          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_							)
          UpperCAmelCase       =   0
          UpperCAmelCase       =   Path(self.hparams.output_dir							)
          UpperCAmelCase       =   self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
              UpperCAmelCase       =   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:
              UpperCAmelCase       =   config

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




      def       a_ (							self				,    *lowercase_				,    **lowercase_							)  ->   Tuple:
          UpperCAmelCase       =   self.model_type.from_pretrained(*lowercase_				,    **lowercase_							)




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




      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase       =   self.model
          UpperCAmelCase       =   ['bias', 'LayerNorm.weight']
          UpperCAmelCase       =   [
              {
                  '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       =   Adafactor(
                  lowercase_				,    lr=self.hparams.learning_rate				,    scale_parameter=lowercase_				,    relative_step=lowercase_							)

          else:
              UpperCAmelCase       =   AdamW(
                  lowercase_				,    lr=self.hparams.learning_rate				,    eps=self.hparams.adam_epsilon							)
          UpperCAmelCase       =   optimizer

          UpperCAmelCase       =   self.get_lr_scheduler()

          return [optimizer], [scheduler]




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   List[Any]:
          return self.validation_step(lowercase_				,    lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   Optional[Any]:
          return self.validation_end(lowercase_							)




      def       a_ (							self							)  ->   int:
          UpperCAmelCase       =   max(1				,    self.hparams.gpus							)  # TODO: consider num_tpu_cores
          UpperCAmelCase       =   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				,    lowercase_							)  ->   Optional[int]:
          if mode == "test":
              UpperCAmelCase       =   len(self.test_dataloader().dataset							)
          else:
              UpperCAmelCase       =   self.get_dataloader('train'				,    self.hparams.train_batch_size				,    shuffle=lowercase_							)
              UpperCAmelCase       =   len(self.train_dataloader().dataset							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_ = False							)  ->   Any:
          raise NotImplementedError('You must implement this for your task'							)




      def       a_ (							self							)  ->   Dict:
          return self.train_loader




      def       a_ (							self							)  ->   List[str]:
          return self.get_dataloader('dev'				,    self.hparams.eval_batch_size				,    shuffle=lowercase_							)




      def       a_ (							self							)  ->   Tuple:
          return self.get_dataloader('test'				,    self.hparams.eval_batch_size				,    shuffle=lowercase_							)




      def       a_ (							self				,    lowercase_							)  ->   List[str]:
          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       a_ (							self				,    lowercase_							)  ->   None:
          UpperCAmelCase       =   self.output_dir.joinpath('best_tfmr'							)
          UpperCAmelCase       =   self.step_count
          self.model.save_pretrained(lowercase_							)
          self.tokenizer.save_pretrained(lowercase_							)




      @staticmethod
      def       a_ (							lowercase_				,    lowercase_							)  ->   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=3_2				,    type=lowercase_							)
          parser.add_argument('--eval_batch_size'				,    default=3_2				,    type=lowercase_							)
          parser.add_argument('--adafactor'				,    action='store_true'							)

class        _UpperCAmelCase							(						pl.Callback							):




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   int:
          UpperCAmelCase       =   trainer.lr_schedulers[0]['scheduler']
          UpperCAmelCase       =   {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
          pl_module.logger.log_metrics(lowercase_							)




      def       a_ (							self				,    lowercase_				,    lowercase_							)  ->   Any:
          rank_zero_info('***** Validation results *****'							)
          UpperCAmelCase       =   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       a_ (							self				,    lowercase_				,    lowercase_							)  ->   Tuple:
          rank_zero_info('***** Test results *****'							)
          UpperCAmelCase       =   trainer.callback_metrics
          # Log and save results to file
          UpperCAmelCase       =   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       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   Any	)	-> None:






    """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       lowercase__  (   lowerCAmelCase			:   BaseTransformer	,			lowerCAmelCase			:   argparse.Namespace	,			lowerCAmelCase			:   Optional[int]=None	,			lowerCAmelCase			:   Optional[int]=True	,			lowerCAmelCase			:   Union[str, Any]=[]	,			lowerCAmelCase			:   Any=None	,			lowerCAmelCase			:   int=None	,			**lowerCAmelCase			:   Union[str, Any]	,			)	-> Optional[int]:






    """simple docstring"""




    pl.seed_everything(args.seed	)

    # init model
    UpperCAmelCase       =   Path(model.hparams.output_dir	)
    odir.mkdir(exist_ok=lowerCAmelCase	)

    # add custom checkpoints
    if checkpoint_callback is None:
        UpperCAmelCase       =   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       =   LoggingCallback()

    UpperCAmelCase       =   {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        UpperCAmelCase       =   16
        UpperCAmelCase       =   args.fpaa_opt_level

    if args.gpus > 1:
        UpperCAmelCase       =   'ddp'

    UpperCAmelCase       =   args.accumulate_grad_batches
    UpperCAmelCase       =   extra_train_kwargs.get('accelerator'	,			lowerCAmelCase	)
    UpperCAmelCase       =   extra_train_kwargs.get('profiler'	,			lowerCAmelCase	)

    UpperCAmelCase       =   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





"""simple docstring"""

SCREAMING_SNAKE_CASE_         =  {
    0: '''underwear''',
    1: '''pants''',
    2: '''belt''',
    3: '''suit''',
    4: '''shoe''',
    5: '''socks''',
    6: '''shirt''',
    7: '''tie''',
    8: '''watch''',
}

SCREAMING_SNAKE_CASE_         =  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

SCREAMING_SNAKE_CASE_         =  [0 for x in range(len(graph))]
SCREAMING_SNAKE_CASE_         =  []




def       lowercase__  (   lowerCAmelCase			:   Optional[int]	,			lowerCAmelCase			:   Any	)	-> List[str]:






    """simple docstring"""




    UpperCAmelCase       =   1
    while stack:
        UpperCAmelCase       =   stack.pop()
        print(lowerCAmelCase	,			clothes[current_clothing]	)
        order += 1




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   1
    for v in graph[u]:
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    stack.append(lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	)	-> Optional[Any]:






    """simple docstring"""




    for v in range(len(lowerCAmelCase	)	):
        if not visited[v]:
            depth_first_search(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)



