code: 
  
  
  
  '''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__						:			List[str]										=			logging.getLogger(__name__)
  
  require_version('pytorch_lightning>=1.0.4')
  
  a__						:			Any										=			{
      '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__						:			List[str]										=			{
      '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__						:			List[Any]										=			sorted(arg_to_scheduler.keys())
  a__						:			Optional[Any]										=			'{' + ', '.join(arg_to_scheduler_choices) + '}'
  class        UpperCAmelCase__  (    pl.LightningModule):
  
  
  
   def __init__(  self		,	lowercase		,	lowercase=None		,	lowercase="base"		,	lowercase=None		,	lowercase=None		,	lowercase=None		,	**lowercase		,	)  ->							Tuple:
    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    __lowerCamelCase				(  self		,	*lowercase		,	**lowercase			)  ->							Union[str, Any]:
    __UpperCamelCase				      =  self.model_type.from_pretrained(*lowercase		,	**lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							int:
    __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    __lowerCamelCase				(  self			)  ->							Union[str, 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    __lowerCamelCase				(  self		,	lowercase		,	lowercase			)  ->							Optional[Any]:
    return self.validation_step(lowercase		,	lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							List[Any]:
    return self.validation_end(lowercase			)
  
  
  
   def    __lowerCamelCase				(  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    __lowerCamelCase				(  self		,	lowercase			)  ->							Union[str, Any]:
    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    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase = False			)  ->							Optional[Any]:
    raise NotImplementedError("""You must implement this for your task"""			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Dict:
    return self.train_loader
  
  
  
   def    __lowerCamelCase				(  self			)  ->							Optional[int]:
    return self.get_dataloader("""dev"""		,	self.hparams.eval_batch_size		,	shuffle=lowercase			)
  
  
  
   def    __lowerCamelCase				(  self			)  ->							List[str]:
    return self.get_dataloader("""test"""		,	self.hparams.eval_batch_size		,	shuffle=lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							Optional[int]:
    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		,	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    __lowerCamelCase				(  lowercase		,	lowercase			)  ->							int:
    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    __lowerCamelCase				(  self		,	lowercase		,	lowercase			)  ->							Any:
    __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    __lowerCamelCase				(  self		,	lowercase		,	lowercase			)  ->							str:
    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    __lowerCamelCase				(  self		,	lowercase		,	lowercase			)  ->							Optional[Any]:
    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   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   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 _lowercase   (  __A							,__A							,__A=None							,__A=True							,__A=[]							,__A=None							,__A=None							,**__A							,):
  
  
  
   '''simple docstring'''
  
  
  
   pl.seed_everything(args.seed       )
  
   # init model
   __UpperCamelCase				      =  Path(model.hparams.output_dir       )
   odir.mkdir(exist_ok=__A       )
  
   # 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(__A       )
   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"""							,__A       )
   __UpperCamelCase				      =  extra_train_kwargs.get("""profiler"""							,__A       )
  
   __UpperCamelCase				      =  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
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text.txt"""
   __UpperCamelCase				      =  textwrap.dedent(
       """\
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
  
          Second paragraph:
          Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
          Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
          Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
          Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
          """       )
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.fixture
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  tmp_path / """text_with_image.txt"""
   with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
    f.write(__A       )
   return str(__A       )
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""keep_linebreaks"""							,[True, False]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines(keepends=__A       )
   __UpperCamelCase				      =  Text(keep_linebreaks=__A							,encoding="""utf-8"""       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  
  
  @require_pil
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read().splitlines()[0]
   __UpperCamelCase				      =  Text(encoding="""utf-8"""							,features=Features({"""image""": Image()}       )       )
   __UpperCamelCase				      =  text._generate_tables([[text_file_with_image]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       )
   assert pa_table.schema.field("""image"""       ).type == Image()()
   __UpperCamelCase				      =  pa_table.to_pydict()["""image"""]
   assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  
  
  
  
  @pytest.mark.parametrize("""sample_by"""							,["""line""", """paragraph""", """document"""]       )
  def _lowercase   (  __A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   with open(__A							,encoding="""utf-8"""       ) as f:
    __UpperCamelCase				      =  f.read()
   if sample_by == "line":
    __UpperCamelCase				      =  expected_content.splitlines()
   elif sample_by == "paragraph":
    __UpperCamelCase				      =  expected_content.split("""\n\n"""       )
   elif sample_by == "document":
    __UpperCamelCase				      =  [expected_content]
   __UpperCamelCase				      =  Text(sample_by=__A							,encoding="""utf-8"""							,chunksize=100       )
   __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
   __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
   assert generated_content == expected_content
  
  
  
  
  
  