
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


UpperCAmelCase__	:  Optional[int]      =   logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

UpperCAmelCase__	:  Tuple      =   {
    '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
UpperCAmelCase__	:  Dict      =   {
    '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
}
UpperCAmelCase__	:  Tuple      =   sorted(arg_to_scheduler.keys())
UpperCAmelCase__	:  str      =   '{' + ', '.join(arg_to_scheduler_choices) + '}'






class UpperCAmelCase     (		pl.LightningModule     ):





   '''simple docstring'''


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

      """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_  )
      _A:						Tuple	    = 0
      _A:						str	    = Path(self.hparams.output_dir  )
      _A:						Tuple	    = self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
         _A:						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=lowerCAmelCase_    ,    **lowerCAmelCase_    ,    )
      else:
         _A:						PretrainedConfig	    = config

      _A:						List[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:
         _A:						str	    = AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,    cache_dir=lowerCAmelCase_    ,    )
      else:
         _A:						PreTrainedTokenizer	    = tokenizer
      _A:						int	    = MODEL_MODES[mode]
      if model is None:
         _A:						Dict	    = 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:
         _A:						Union[str, Any]	    = model


   def 				__magic_name__       (      self  :							Optional[Any]    ,    *lowerCAmelCase_  :							Optional[int]    ,    **lowerCAmelCase_  :							str  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = self.model_type.from_pretrained(*lowerCAmelCase_    ,    **lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

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


   def 				__magic_name__       (      self  :							List[str]  ):

      """simple docstring"""
      _A:						Optional[Any]	    = self.model
      _A:						Any	    = ['''bias''', '''LayerNorm.weight''']
      _A:						Optional[int]	    = [
          {
              '''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:
         _A:						Dict	    = Adafactor(
             lowerCAmelCase_    ,    lr=self.hparams.learning_rate    ,    scale_parameter=lowerCAmelCase_    ,    relative_step=lowerCAmelCase_  )

      else:
         _A:						Dict	    = AdamW(
             lowerCAmelCase_    ,    lr=self.hparams.learning_rate    ,    eps=self.hparams.adam_epsilon  )
      _A:						Union[str, Any]	    = optimizer

      _A:						int	    = self.get_lr_scheduler()

      return [optimizer], [scheduler]


   def 				__magic_name__       (      self  :							int    ,    lowerCAmelCase_  :							str    ,    lowerCAmelCase_  :							Any  ):

      """simple docstring"""
      return self.validation_step(lowerCAmelCase_    ,    lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							str    ,    lowerCAmelCase_  :							Dict  ):

      """simple docstring"""
      return self.validation_end(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      _A:						Dict	    = max(1    ,    self.hparams.gpus  )  # TODO: consider num_tpu_cores
      _A:						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 				__magic_name__       (      self  :							Optional[int]    ,    lowerCAmelCase_  :							Dict  ):

      """simple docstring"""
      if mode == "test":
         _A:						Any	    = len(self.test_dataloader().dataset  )
      else:
         _A:						Optional[Any]	    = self.get_dataloader('''train'''    ,    self.hparams.train_batch_size    ,    shuffle=lowerCAmelCase_  )
         _A:						Tuple	    = len(self.train_dataloader().dataset  )


   def 				__magic_name__       (      self  :							List[str]    ,    lowerCAmelCase_  :							str    ,    lowerCAmelCase_  :							int    ,    lowerCAmelCase_  :							bool = False  ):

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


   def 				__magic_name__       (      self  :							Any  ):

      """simple docstring"""
      return self.train_loader


   def 				__magic_name__       (      self  :							Optional[Any]  ):

      """simple docstring"""
      return self.get_dataloader('''dev'''    ,    self.hparams.eval_batch_size    ,    shuffle=lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							List[str]  ):

      """simple docstring"""
      return self.get_dataloader('''test'''    ,    self.hparams.eval_batch_size    ,    shuffle=lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							int    ,    lowerCAmelCase_  :							Optional[int]  ):

      """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 				__magic_name__       (      self  :							Optional[Any]    ,    lowerCAmelCase_  :							Dict[str, Any]  ):

      """simple docstring"""
      _A:						Tuple	    = self.output_dir.joinpath('''best_tfmr'''  )
      _A:						List[str]	    = self.step_count
      self.model.save_pretrained(lowerCAmelCase_  )
      self.tokenizer.save_pretrained(lowerCAmelCase_  )


   @staticmethod
   def 				__magic_name__       (      lowerCAmelCase_  :							List[Any]    ,    lowerCAmelCase_  :							Union[str, 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=''''''    ,    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 UpperCAmelCase     (		pl.Callback     ):





   '''simple docstring'''


   def 				__magic_name__       (      self  :							Any    ,    lowerCAmelCase_  :							Optional[Any]    ,    lowerCAmelCase_  :							Optional[Any]  ):

      """simple docstring"""
      _A:						List[str]	    = trainer.lr_schedulers[0]['''scheduler''']
      _A:						Union[str, Any]	    = {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
      pl_module.logger.log_metrics(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Tuple    ,    lowerCAmelCase_  :							pl.Trainer    ,    lowerCAmelCase_  :							pl.LightningModule  ):

      """simple docstring"""
      rank_zero_info('''***** Validation results *****'''  )
      _A:						Dict	    = 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 				__magic_name__       (      self  :							Any    ,    lowerCAmelCase_  :							pl.Trainer    ,    lowerCAmelCase_  :							pl.LightningModule  ):

      """simple docstring"""
      rank_zero_info('''***** Test results *****'''  )
      _A:						Dict	    = trainer.callback_metrics
      # Log and save results to file
      _A:						Any	    = 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__							(      a	,	a   )   ->     None:
   #  To allow all pl args uncomment the following line
   #  parser = pl.Trainer.add_argparse_args(parser)
   parser.add_argument(
       '''--output_dir'''	,	default=a	,	type=a	,	required=a	,	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=a	,	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=a   )
   parser.add_argument('''--max_grad_norm'''	,	dest='''gradient_clip_val'''	,	default=1.0	,	type=a	,	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=a	,	default=1	,	help='''Number of updates steps to accumulate before performing a backward/update pass.'''	,	)
   parser.add_argument('''--seed'''	,	type=a	,	default=42	,	help='''random seed for initialization'''   )
   parser.add_argument(
       '''--data_dir'''	,	default=a	,	type=a	,	required=a	,	help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''	,	)






def 						lowerCamelCase__							(      a	,	a	,	a=None	,	a=True	,	a=[]	,	a=None	,	a=None	,	**a	,	)   ->     Optional[Any]:
   pl.seed_everything(args.seed   )

   # init model
   _A:						Union[str, Any]	    = Path(model.hparams.output_dir   )
   odir.mkdir(exist_ok=a   )

   # add custom checkpoints
   if checkpoint_callback is None:
      _A:						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(a   )
   if logging_callback is None:
      _A:						int	    = LoggingCallback()

   _A:						List[str]	    = {}

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

   if args.gpus > 1:
      _A:						Any	    = '''ddp'''

   _A:						List[Any]	    = args.accumulate_grad_batches
   _A:						int	    = extra_train_kwargs.get('''accelerator'''	,	a   )
   _A:						int	    = extra_train_kwargs.get('''profiler'''	,	a   )

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

   if args.do_train:
      trainer.fit(a   )

   return trainer





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase__	:  Optional[Any]      =   '\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'

UpperCAmelCase__	:  List[Any]      =   '\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n'


UpperCAmelCase__	:  List[str]      =   '\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric("frugalscore")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results["scores"]])\n    [0.631, 0.645]\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION     )
class UpperCAmelCase     (		datasets.Metric     ):





   '''simple docstring'''


   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,    citation=_CITATION    ,    inputs_description=_KWARGS_DESCRIPTION    ,    features=datasets.Features(
              {
                  '''predictions''': datasets.Value('''string'''  ),
                  '''references''': datasets.Value('''string'''  ),
              }  )    ,    homepage='''https://github.com/moussaKam/FrugalScore'''    ,    )


   def 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							int  ):

      """simple docstring"""
      if self.config_name == "default":
         _A:						Dict	    = '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
      else:
         _A:						str	    = self.config_name
      _A:						Any	    = AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_  )
      _A:						Tuple	    = AutoTokenizer.from_pretrained(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Dict    ,    lowerCAmelCase_  :							Dict    ,    lowerCAmelCase_  :							str    ,    lowerCAmelCase_  :							Any=3_2    ,    lowerCAmelCase_  :							str=1_2_8    ,    lowerCAmelCase_  :							str=None    ,    ):

      """simple docstring"""
      assert len(lowerCAmelCase_  ) == len(
          lowerCAmelCase_  ), "predictions and references should have the same number of sentences."
      if device is not None:
         assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
      else:
         _A:						Union[str, Any]	    = '''gpu''' if torch.cuda.is_available() else '''cpu'''
      _A:						Optional[int]	    = TrainingArguments(
          '''trainer'''    ,    fpaa=(device == '''gpu''')    ,    per_device_eval_batch_size=lowerCAmelCase_    ,    report_to='''all'''    ,    no_cuda=(device == '''cpu''')    ,    log_level='''warning'''    ,    )
      _A:						Optional[int]	    = {'''sentence1''': predictions, '''sentence2''': references}
      _A:						Dict	    = datasets.Dataset.from_dict(lowerCAmelCase_  )

      def tokenize_function(lowerCAmelCase_  :							Any  ):
         return self.tokenizer(
             data['''sentence1''']    ,    data['''sentence2''']    ,    max_length=lowerCAmelCase_    ,    truncation=lowerCAmelCase_    ,    padding=lowerCAmelCase_  )

      _A:						Dict	    = raw_datasets.map(lowerCAmelCase_    ,    batched=lowerCAmelCase_  )
      tokenized_datasets.remove_columns(['''sentence1''', '''sentence2''']  )
      _A:						Optional[int]	    = Trainer(self.model    ,    lowerCAmelCase_    ,    tokenizer=self.tokenizer  )
      _A:						int	    = trainer.predict(lowerCAmelCase_  )
      return {"scores": list(predictions.predictions.squeeze(-1  )  )}



