code: 
  
  
  
  
  
  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			:			Union[str, Any]	  = logging.getLogger(__name__)
  
  require_version("""pytorch_lightning>=1.0.4""")
  
  UpperCAmelCase			:			Union[str, 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
  UpperCAmelCase			:			List[Any]	  = {
      """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			:			Union[str, Any]	  = sorted(arg_to_scheduler.keys())
  UpperCAmelCase			:			Optional[Any]	  = """{""" + """, """.join(arg_to_scheduler_choices) + """}"""
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					pl.LightningModule):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__="base"	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   **lowerCAmelCase__	,   )     ->		List[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(lowerCAmelCase__							)
          a__			:      List[str]            =0
          a__			:      Dict            =Path(self.hparams.output_dir							)
          a__			:      Dict            =self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
            a__			:      List[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:
            a__			:      PretrainedConfig            =config
  
          a__			:      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:
            a__			:      Optional[Any]            =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__			:      List[Any]            =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__			:      Optional[Any]            =model
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   *lowerCAmelCase__	,   **lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      int            =self.model_type.from_pretrained(*lowerCAmelCase__	,   **lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      str            =arg_to_scheduler[self.hparams.lr_scheduler]
          a__			:      Optional[Any]            =get_schedule_func(
              self.opt	,   num_warmup_steps=self.hparams.warmup_steps	,   num_training_steps=self.total_steps()							)
          a__			:      Optional[int]            ={"scheduler": scheduler, "interval": "step", "frequency": 1}
          return scheduler
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =self.model
          a__			:      Any            =["bias", "LayerNorm.weight"]
          a__			:      Dict            =[
              {
                  "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__			:      List[Any]            =Adafactor(
                lowerCAmelCase__	,   lr=self.hparams.learning_rate	,   scale_parameter=lowerCAmelCase__	,   relative_step=lowerCAmelCase__							)
  
          else:
            a__			:      List[str]            =AdamW(
                lowerCAmelCase__	,   lr=self.hparams.learning_rate	,   eps=self.hparams.adam_epsilon							)
          a__			:      int            =optimizer
  
          a__			:      Any            =self.get_lr_scheduler()
  
          return [optimizer], [scheduler]
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.validation_step(lowerCAmelCase__	,   lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.validation_end(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =max(1	,   self.hparams.gpus							)  # TODO: consider num_tpu_cores
          a__			:      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      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if mode == "test":
            a__			:      str            =len(self.test_dataloader().dataset							)
          else:
            a__			:      Tuple            =self.get_dataloader("train"	,   self.hparams.train_batch_size	,   shuffle=lowerCAmelCase__							)
            a__			:      List[Any]            =len(self.train_dataloader().dataset							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__ = False							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError("You must implement this for your task"							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.train_loader
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.get_dataloader("dev"	,   self.hparams.eval_batch_size	,   shuffle=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          return self.get_dataloader("test"	,   self.hparams.eval_batch_size	,   shuffle=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[Any]:
  
  
  
          '''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      _lowercase						(					self	,   lowerCAmelCase__							)     ->		None:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =self.output_dir.joinpath("best_tfmr"							)
          a__			:      Optional[Any]            =self.step_count
          self.model.save_pretrained(lowerCAmelCase__							)
          self.tokenizer.save_pretrained(lowerCAmelCase__							)
  
  
  
  
  
  
  
        @staticmethod
        def      _lowercase						(					lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Tuple:
  
  
  
          '''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        __lowerCAmelCase (					pl.Callback):
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[Any]            =trainer.lr_schedulers[0]["scheduler"]
          a__			:      Dict            ={F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
          pl_module.logger.log_metrics(lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          rank_zero_info("***** Validation results *****"							)
          a__			:      int            =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      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          rank_zero_info("***** Test results *****"							)
          a__			:      Dict            =trainer.callback_metrics
          # Log and save results to file
          a__			:      int            =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   _A (	SCREAMING_SNAKE_CASE  :     Union[str, Any] , SCREAMING_SNAKE_CASE  :     str		):
  
  
  
  
  
    """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  :     Dict=None , SCREAMING_SNAKE_CASE  :     Optional[Any]=True , SCREAMING_SNAKE_CASE  :     Any=[] , SCREAMING_SNAKE_CASE  :     str=None , SCREAMING_SNAKE_CASE  :     str=None , **SCREAMING_SNAKE_CASE  :     Any , ):
  
  
  
  
  
    """simple docstring"""
  
  
  
    pl.seed_everything(args.seed		)
  
    # init model
    a__			:      Dict            =Path(model.hparams.output_dir		)
    odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE		)
  
    # add custom checkpoints
    if checkpoint_callback is None:
      a__			:      Tuple            =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:
      a__			:      str            =LoggingCallback()
  
    a__			:      Dict            ={}
  
    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
      a__			:      Union[str, Any]            =16
      a__			:      Tuple            =args.fpaa_opt_level
  
    if args.gpus > 1:
      a__			:      Union[str, Any]            ="ddp"
  
    a__			:      Dict            =args.accumulate_grad_batches
    a__			:      Any            =extra_train_kwargs.get("accelerator" , SCREAMING_SNAKE_CASE		)
    a__			:      Optional[int]            =extra_train_kwargs.get("profiler" , SCREAMING_SNAKE_CASE		)
  
    a__			:      int            =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
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  UpperCAmelCase			:			int	  = logging.get_logger(__name__)
  
  
  
  
  
  
  
  @dataclass
  class        __lowerCAmelCase :
        _lowercase       :      str
        _lowercase       :      str
        _lowercase       :      Optional[str]        =    None
        _lowercase       :      Optional[str]        =    None
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return json.dumps(dataclasses.asdict(self							)	,   indent=2							) + "\n"
  
  
  
  
  
  
  
  @dataclass(frozen=UpperCamelCase__)
  class        __lowerCAmelCase :
        _lowercase       :      List[int]
        _lowercase       :      Optional[List[int]]        =    None
        _lowercase       :      Optional[List[int]]        =    None
        _lowercase       :      Optional[Union[int, float]]        =    None
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return json.dumps(dataclasses.asdict(self							)							) + "\n"
  
  
  
  
  
  
  
  class        __lowerCAmelCase :
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		str:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          raise NotImplementedError()
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__							)     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          if len(self.get_labels()							) > 1:
            a__			:      Dict            =self.get_labels()[int(example.label							)]
          return example
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None							)     ->		Union[str, Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          with open(lowerCAmelCase__	,   "r"	,   encoding="utf-8-sig"							) as f:
            return list(csv.reader(lowerCAmelCase__	,   delimiter="\t"	,   quotechar=lowerCAmelCase__							)							)
  
  
  
  
  
  
  
  class        __lowerCAmelCase (					UpperCamelCase__):
  
  
  
  
  
  
  
        def __init__(					self	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__="classification"	,   lowerCAmelCase__=False							)     ->		Any:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Any            =[] if labels is None else labels
          a__			:      List[Any]            =[] if examples is None else examples
          a__			:      List[str]            =mode
          a__			:      Tuple            =verbose
  
  
  
  
  
  
  
        def __len__(					self							)     ->		Optional[int]:
  
  
  
          '''simple docstring'''
  
  
  
  
          return len(self.examples							)
  
  
  
  
  
  
  
        def __getitem__(					self	,   lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          if isinstance(lowerCAmelCase__	,   lowerCAmelCase__							):
            return SingleSentenceClassificationProcessor(labels=self.labels	,   examples=self.examples[idx]							)
          return self.examples[idx]
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   **lowerCAmelCase__							)     ->		List[str]:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Tuple            =cls(**lowerCAmelCase__							)
          processor.add_examples_from_csv(
              lowerCAmelCase__	,   split_name=lowerCAmelCase__	,   column_label=lowerCAmelCase__	,   column_text=lowerCAmelCase__	,   column_id=lowerCAmelCase__	,   skip_first_row=lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__	,   )
          return processor
  
  
  
  
  
  
  
        @classmethod
        def      _lowercase						(					cls	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   **lowerCAmelCase__							)     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      List[str]            =cls(**lowerCAmelCase__							)
          processor.add_examples(lowerCAmelCase__	,   labels=lowerCAmelCase__							)
          return processor
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=""	,   lowerCAmelCase__=0	,   lowerCAmelCase__=1	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False	,   )     ->		Dict:
  
  
  
          '''simple docstring'''
  
  
  
  
          a__			:      Dict            =self._read_tsv(lowerCAmelCase__							)
          if skip_first_row:
            a__			:      Tuple            =lines[1:]
          a__			:      List[Any]            =[]
          a__			:      int            =[]
          a__			:      str            =[]
          for i, line in enumerate(lowerCAmelCase__							):
            texts.append(line[column_text]							)
            labels.append(line[column_label]							)
            if column_id is not None:
              ids.append(line[column_id]							)
            else:
              a__			:      List[str]            =F'''{split_name}-{i}''' if split_name else str(lowerCAmelCase__							)
              ids.append(lowerCAmelCase__							)
  
          return self.add_examples(
              lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__	,   overwrite_labels=lowerCAmelCase__	,   overwrite_examples=lowerCAmelCase__							)
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=False							)     ->		List[Any]:
  
  
  
          '''simple docstring'''
  
  
  
  
          if labels is not None and len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
            raise ValueError(
                F'''Text and labels have mismatched lengths {len(lowerCAmelCase__							)} and {len(lowerCAmelCase__							)}'''							)
          if ids is not None and len(lowerCAmelCase__							) != len(lowerCAmelCase__							):
            raise ValueError(F'''Text and ids have mismatched lengths {len(lowerCAmelCase__							)} and {len(lowerCAmelCase__							)}'''							)
          if ids is None:
            a__			:      Dict            =[None] * len(lowerCAmelCase__							)
          if labels is None:
            a__			:      str            =[None] * len(lowerCAmelCase__							)
          a__			:      Union[str, Any]            =[]
          a__			:      Optional[Any]            =set()
          for text_or_text_and_label, label, guid in zip(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							):
            if isinstance(lowerCAmelCase__	,   (tuple, list)							) and label is None:
              a__      ,							a__			:      int            =text_or_text_and_label
            else:
              a__			:      List[str]            =text_or_text_and_label
            added_labels.add(lowerCAmelCase__							)
            examples.append(InputExample(guid=lowerCAmelCase__	,   text_a=lowerCAmelCase__	,   text_b=lowerCAmelCase__	,   label=lowerCAmelCase__							)							)
  
          # Update examples
          if overwrite_examples:
            a__			:      Dict            =examples
          else:
            self.examples.extend(lowerCAmelCase__							)
  
          # Update labels
          if overwrite_labels:
            a__			:      Any            =list(lowerCAmelCase__							)
          else:
            a__			:      List[Any]            =list(set(self.labels							).union(lowerCAmelCase__							)							)
  
          return self.examples
  
  
  
  
  
  
  
        def      _lowercase						(					self	,   lowerCAmelCase__	,   lowerCAmelCase__=None	,   lowerCAmelCase__=False	,   lowerCAmelCase__=0	,   lowerCAmelCase__=True	,   lowerCAmelCase__=None	,   )     ->		int:
  
  
  
          '''simple docstring'''
  
  
  
  
          if max_length is None:
            a__			:      int            =tokenizer.max_len
  
          a__			:      int            ={label: i for i, label in enumerate(self.labels							)}
  
          a__			:      Union[str, Any]            =[]
          for ex_index, example in enumerate(self.examples							):
            if ex_index % 1_0_0_0_0 == 0:
              logger.info(F'''Tokenizing example {ex_index}'''							)
  
            a__			:      Dict            =tokenizer.encode(
                example.text_a	,   add_special_tokens=lowerCAmelCase__	,   max_length=min(lowerCAmelCase__	,   tokenizer.max_len							)	,   )
            all_input_ids.append(lowerCAmelCase__							)
  
          a__			:      List[Any]            =max(len(lowerCAmelCase__							) for input_ids in all_input_ids							)
  
          a__			:      Dict            =[]
          for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase__	,   self.examples							)							):
            if ex_index % 1_0_0_0_0 == 0:
              logger.info(F'''Writing example {ex_index}/{len(self.examples							)}'''							)
            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            a__			:      Optional[Any]            =[1 if mask_padding_with_zero else 0] * len(lowerCAmelCase__							)
  
            # Zero-pad up to the sequence length.
            a__			:      List[str]            =batch_length - len(lowerCAmelCase__							)
            if pad_on_left:
              a__			:      Tuple            =([pad_token] * padding_length) + input_ids
              a__			:      Dict            =([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
            else:
              a__			:      List[str]            =input_ids + ([pad_token] * padding_length)
              a__			:      Any            =attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
            if len(lowerCAmelCase__							) != batch_length:
              raise ValueError(F'''Error with input length {len(lowerCAmelCase__							)} vs {batch_length}'''							)
            if len(lowerCAmelCase__							) != batch_length:
              raise ValueError(F'''Error with input length {len(lowerCAmelCase__							)} vs {batch_length}'''							)
  
            if self.mode == "classification":
              a__			:      Dict            =label_map[example.label]
            elif self.mode == "regression":
              a__			:      Any            =float(example.label							)
            else:
              raise ValueError(self.mode							)
  
            if ex_index < 5 and self.verbose:
              logger.info("*** Example ***"							)
              logger.info(F'''guid: {example.guid}'''							)
              logger.info(F'''input_ids: {" ".join([str(lowerCAmelCase__							) for x in input_ids]							)}'''							)
              logger.info(F'''attention_mask: {" ".join([str(lowerCAmelCase__							) for x in attention_mask]							)}'''							)
              logger.info(F'''label: {example.label} (id = {label})'''							)
  
            features.append(InputFeatures(input_ids=lowerCAmelCase__	,   attention_mask=lowerCAmelCase__	,   label=lowerCAmelCase__							)							)
  
          if return_tensors is None:
            return features
          elif return_tensors == "tf":
            if not is_tf_available():
              raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"							)
            import tensorflow as tf
  
            def gen():
              for ex in features:
                yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
            a__			:      Any            =tf.data.Dataset.from_generator(
                lowerCAmelCase__	,   ({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)	,   ({"input_ids": tf.TensorShape([None]							), "attention_mask": tf.TensorShape([None]							)}, tf.TensorShape([]							))	,   )
            return dataset
          elif return_tensors == "pt":
            if not is_torch_available():
              raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"							)
            import torch
            from torch.utils.data import TensorDataset
  
            a__			:      str            =torch.tensor([f.input_ids for f in features]	,   dtype=torch.long							)
            a__			:      str            =torch.tensor([f.attention_mask for f in features]	,   dtype=torch.long							)
            if self.mode == "classification":
              a__			:      Dict            =torch.tensor([f.label for f in features]	,   dtype=torch.long							)
            elif self.mode == "regression":
              a__			:      Tuple            =torch.tensor([f.label for f in features]	,   dtype=torch.float							)
  
            a__			:      List[str]            =TensorDataset(lowerCAmelCase__	,   lowerCAmelCase__	,   lowerCAmelCase__							)
            return dataset
          else:
            raise ValueError("return_tensors should be one of 'tf' or 'pt'"							)
  
  
  
  
  
  
  