



'''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


lowercase__  :	List[Any]            =      logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowercase__  :	Optional[int]            =      {
    '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
lowercase__  :	Optional[int]            =      {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase__  :	str            =      sorted(arg_to_scheduler.keys())
lowercase__  :	Tuple            =      '{' + ', '.join(arg_to_scheduler_choices) + '}'

class    lowerCamelCase_				(						pl.LightningModule	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    __UpperCamelCase="base"  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    **__UpperCamelCase  ,    ):
       super().__init__()
       # TODO: move to self.save_hyperparameters()
       # self.save_hyperparameters()
       # can also expand arguments into trainer signature for easier reading

       self.save_hyperparameters(__UpperCamelCase			)
       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=__UpperCamelCase  ,    **__UpperCamelCase  ,    )
       else:
           UpperCamelCase__	     =      config

       UpperCamelCase__	     =      ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
       for p in extra_model_params:
           if getattr(self.hparams  ,    __UpperCamelCase  ,    __UpperCamelCase			):
               assert hasattr(self.config  ,    __UpperCamelCase			), f'model config doesn\'t have a `{p}` attribute'
               setattr(self.config  ,    __UpperCamelCase  ,    getattr(self.hparams  ,    __UpperCamelCase			)			)

       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=__UpperCamelCase  ,    )
       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=__UpperCamelCase  ,    )
       else:
           UpperCamelCase__	     =      model


   def 						_UpperCamelCase							(	self  ,    *__UpperCamelCase  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      self.model_type.from_pretrained(*__UpperCamelCase  ,    **__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       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 						_UpperCamelCase							(	self			):
       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(
               __UpperCamelCase  ,    lr=self.hparams.learning_rate  ,    scale_parameter=__UpperCamelCase  ,    relative_step=__UpperCamelCase			)

       else:
           UpperCamelCase__	     =      AdamW(
               __UpperCamelCase  ,    lr=self.hparams.learning_rate  ,    eps=self.hparams.adam_epsilon			)
       UpperCamelCase__	     =      optimizer

       UpperCamelCase__	     =      self.get_lr_scheduler()

       return [optimizer], [scheduler]


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       return self.validation_step(__UpperCamelCase  ,    __UpperCamelCase			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       return self.validation_end(__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       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 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       if mode == "test":
           UpperCamelCase__	     =      len(self.test_dataloader().dataset			)
       else:
           UpperCamelCase__	     =      self.get_dataloader("train"  ,    self.hparams.train_batch_size  ,    shuffle=__UpperCamelCase			)
           UpperCamelCase__	     =      len(self.train_dataloader().dataset			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase = False			):
       raise NotImplementedError("You must implement this for your task"			)


   def 						_UpperCamelCase							(	self			):
       return self.train_loader


   def 						_UpperCamelCase							(	self			):
       return self.get_dataloader("dev"  ,    self.hparams.eval_batch_size  ,    shuffle=__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       return self.get_dataloader("test"  ,    self.hparams.eval_batch_size  ,    shuffle=__UpperCamelCase			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       return os.path.join(
           self.hparams.data_dir  ,    "cached_{}_{}_{}".format(
               __UpperCamelCase  ,    list(filter(__UpperCamelCase  ,    self.hparams.model_name_or_path.split("/"			)			)			).pop()  ,    str(self.hparams.max_seq_length			)  ,    )  ,    )


   @pl.utilities.rank_zero_only
   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.output_dir.joinpath("best_tfmr"			)
       UpperCamelCase__	     =      self.step_count
       self.model.save_pretrained(__UpperCamelCase			)
       self.tokenizer.save_pretrained(__UpperCamelCase			)







   @staticmethod
   def 						_UpperCamelCase							(	__UpperCamelCase  ,    __UpperCamelCase			):
       parser.add_argument(
           "--model_name_or_path"  ,    default=__UpperCamelCase  ,    type=__UpperCamelCase  ,    required=__UpperCamelCase  ,    help="Path to pretrained model or model identifier from huggingface.co/models"  ,    )
       parser.add_argument(
           "--config_name"  ,    default=""  ,    type=__UpperCamelCase  ,    help="Pretrained config name or path if not the same as model_name"			)
       parser.add_argument(
           "--tokenizer_name"  ,    default=__UpperCamelCase  ,    type=__UpperCamelCase  ,    help="Pretrained tokenizer name or path if not the same as model_name"  ,    )
       parser.add_argument(
           "--cache_dir"  ,    default=""  ,    type=__UpperCamelCase  ,    help="Where do you want to store the pre-trained models downloaded from huggingface.co"  ,    )
       parser.add_argument(
           "--encoder_layerdrop"  ,    type=__UpperCamelCase  ,    help="Encoder layer dropout probability (Optional). Goes into model.config"  ,    )
       parser.add_argument(
           "--decoder_layerdrop"  ,    type=__UpperCamelCase  ,    help="Decoder layer dropout probability (Optional). Goes into model.config"  ,    )
       parser.add_argument(
           "--dropout"  ,    type=__UpperCamelCase  ,    help="Dropout probability (Optional). Goes into model.config"  ,    )
       parser.add_argument(
           "--attention_dropout"  ,    type=__UpperCamelCase  ,    help="Attention dropout probability (Optional). Goes into model.config"  ,    )
       parser.add_argument("--learning_rate"  ,    default=5e-5  ,    type=__UpperCamelCase  ,    help="The initial learning rate for Adam."			)
       parser.add_argument(
           "--lr_scheduler"  ,    default="linear"  ,    choices=__UpperCamelCase  ,    metavar=__UpperCamelCase  ,    type=__UpperCamelCase  ,    help="Learning rate scheduler"  ,    )
       parser.add_argument("--weight_decay"  ,    default=0.0  ,    type=__UpperCamelCase  ,    help="Weight decay if we apply some."			)
       parser.add_argument("--adam_epsilon"  ,    default=1e-8  ,    type=__UpperCamelCase  ,    help="Epsilon for Adam optimizer."			)
       parser.add_argument("--warmup_steps"  ,    default=0  ,    type=__UpperCamelCase  ,    help="Linear warmup over warmup_steps."			)
       parser.add_argument("--num_workers"  ,    default=4  ,    type=__UpperCamelCase  ,    help="kwarg passed to DataLoader"			)
       parser.add_argument("--num_train_epochs"  ,    dest="max_epochs"  ,    default=3  ,    type=__UpperCamelCase			)
       parser.add_argument("--train_batch_size"  ,    default=32  ,    type=__UpperCamelCase			)
       parser.add_argument("--eval_batch_size"  ,    default=32  ,    type=__UpperCamelCase			)
       parser.add_argument("--adafactor"  ,    action="store_true"			)

class    lowerCamelCase_				(						pl.Callback	):






   '''simple docstring'''

   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       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(__UpperCamelCase			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       rank_zero_info("***** Validation results *****"			)
       UpperCamelCase__	     =      trainer.callback_metrics
       # Log results
       for key in sorted(__UpperCamelCase			):
           if key not in ["log", "progress_bar"]:
               rank_zero_info("{} = {}\n".format(__UpperCamelCase  ,    str(metrics[key]			)			)			)







   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       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(__UpperCamelCase  ,    "w"			) as writer:
           for key in sorted(__UpperCamelCase			):
               if key not in ["log", "progress_bar"]:
                   rank_zero_info("{} = {}\n".format(__UpperCamelCase  ,    str(metrics[key]			)			)			)
                   writer.write("{} = {}\n".format(__UpperCamelCase  ,    str(metrics[key]			)			)			)





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    #  To allow all pl args uncomment the following line
    #  parser = pl.Trainer.add_argparse_args(parser)
    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     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_=None       ,	lowerCAmelCase_=True       ,	lowerCAmelCase_=[]       ,	lowerCAmelCase_=None       ,	lowerCAmelCase_=None       ,	**lowerCAmelCase_       ,	):
    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'''







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


lowercase__  :	str            =      logging.get_logger(__name__)

@dataclass
class    lowerCamelCase_				:






   '''simple docstring'''

   A			:  str
   A			:  str
   A			:  Optional[str]					=						None
   A			:  Optional[str]					=						None


   def 						_UpperCamelCase							(	self			):
       return json.dumps(dataclasses.asdict(self			)  ,    indent=2			) + "\n"

@dataclass(frozen=lowerCAmelCase_	)
class    lowerCamelCase_				:






   '''simple docstring'''

   A			:  List[int]
   A			:  Optional[List[int]]					=						None
   A			:  Optional[List[int]]					=						None
   A			:  Optional[Union[int, float]]					=						None


   def 						_UpperCamelCase							(	self			):
       return json.dumps(dataclasses.asdict(self			)			) + "\n"

class    lowerCamelCase_				:






   '''simple docstring'''

   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self			):
       raise NotImplementedError()


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       if len(self.get_labels()			) > 1:
           UpperCamelCase__	     =      self.get_labels()[int(example.label			)]
       return example







   @classmethod
   def 						_UpperCamelCase							(	cls  ,    __UpperCamelCase  ,    __UpperCamelCase=None			):
       with open(__UpperCamelCase  ,    "r"  ,    encoding="utf-8-sig"			) as f:
           return list(csv.reader(__UpperCamelCase  ,    delimiter="\t"  ,    quotechar=__UpperCamelCase			)			)

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase="classification"  ,    __UpperCamelCase=False			):
       UpperCamelCase__	     =      [] if labels is None else labels
       UpperCamelCase__	     =      [] if examples is None else examples
       UpperCamelCase__	     =      mode
       UpperCamelCase__	     =      verbose


   def __len__(	self			):
       return len(self.examples			)


   def __getitem__(	self  ,    __UpperCamelCase			):
       if isinstance(__UpperCamelCase  ,    __UpperCamelCase			):
           return SingleSentenceClassificationProcessor(labels=self.labels  ,    examples=self.examples[idx]			)
       return self.examples[idx]


   @classmethod
   def 						_UpperCamelCase							(	cls  ,    __UpperCamelCase  ,    __UpperCamelCase=""  ,    __UpperCamelCase=0  ,    __UpperCamelCase=1  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      cls(**__UpperCamelCase			)
       processor.add_examples_from_csv(
           __UpperCamelCase  ,    split_name=__UpperCamelCase  ,    column_label=__UpperCamelCase  ,    column_text=__UpperCamelCase  ,    column_id=__UpperCamelCase  ,    skip_first_row=__UpperCamelCase  ,    overwrite_labels=__UpperCamelCase  ,    overwrite_examples=__UpperCamelCase  ,    )
       return processor


   @classmethod
   def 						_UpperCamelCase							(	cls  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    **__UpperCamelCase			):
       UpperCamelCase__	     =      cls(**__UpperCamelCase			)
       processor.add_examples(__UpperCamelCase  ,    labels=__UpperCamelCase			)
       return processor


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=""  ,    __UpperCamelCase=0  ,    __UpperCamelCase=1  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False  ,    ):
       UpperCamelCase__	     =      self._read_tsv(__UpperCamelCase			)
       if skip_first_row:
           UpperCamelCase__	     =      lines[1:]
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      []
       for i, line in enumerate(__UpperCamelCase			):
           texts.append(line[column_text]			)
           labels.append(line[column_label]			)
           if column_id is not None:
               ids.append(line[column_id]			)
           else:
               UpperCamelCase__	     =      f'{split_name}-{i}' if split_name else str(__UpperCamelCase			)
               ids.append(__UpperCamelCase			)

       return self.add_examples(
           __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    overwrite_labels=__UpperCamelCase  ,    overwrite_examples=__UpperCamelCase			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    __UpperCamelCase=False			):
       if labels is not None and len(__UpperCamelCase			) != len(__UpperCamelCase			):
           raise ValueError(
               f'Text and labels have mismatched lengths {len(__UpperCamelCase			)} and {len(__UpperCamelCase			)}'			)
       if ids is not None and len(__UpperCamelCase			) != len(__UpperCamelCase			):
           raise ValueError(f'Text and ids have mismatched lengths {len(__UpperCamelCase			)} and {len(__UpperCamelCase			)}'			)
       if ids is None:
           UpperCamelCase__	     =      [None] * len(__UpperCamelCase			)
       if labels is None:
           UpperCamelCase__	     =      [None] * len(__UpperCamelCase			)
       UpperCamelCase__	     =      []
       UpperCamelCase__	     =      set()
       for text_or_text_and_label, label, guid in zip(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
           if isinstance(__UpperCamelCase  ,    (tuple, list)			) and label is None:
               UpperCamelCase__     ,	UpperCamelCase__	     =      text_or_text_and_label
           else:
               UpperCamelCase__	     =      text_or_text_and_label
           added_labels.add(__UpperCamelCase			)
           examples.append(InputExample(guid=__UpperCamelCase  ,    text_a=__UpperCamelCase  ,    text_b=__UpperCamelCase  ,    label=__UpperCamelCase			)			)

       # Update examples
       if overwrite_examples:
           UpperCamelCase__	     =      examples
       else:
           self.examples.extend(__UpperCamelCase			)

       # Update labels
       if overwrite_labels:
           UpperCamelCase__	     =      list(__UpperCamelCase			)
       else:
           UpperCamelCase__	     =      list(set(self.labels			).union(__UpperCamelCase			)			)

       return self.examples







   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=None  ,    __UpperCamelCase=False  ,    __UpperCamelCase=0  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    ):
       if max_length is None:
           UpperCamelCase__	     =      tokenizer.max_len

       UpperCamelCase__	     =      {label: i for i, label in enumerate(self.labels			)}

       UpperCamelCase__	     =      []
       for ex_index, example in enumerate(self.examples			):
           if ex_index % 1_00_00 == 0:
               logger.info(f'Tokenizing example {ex_index}'			)

           UpperCamelCase__	     =      tokenizer.encode(
               example.text_a  ,    add_special_tokens=__UpperCamelCase  ,    max_length=min(__UpperCamelCase  ,    tokenizer.max_len			)  ,    )
           all_input_ids.append(__UpperCamelCase			)

       UpperCamelCase__	     =      max(len(__UpperCamelCase			) for input_ids in all_input_ids			)

       UpperCamelCase__	     =      []
       for ex_index, (input_ids, example) in enumerate(zip(__UpperCamelCase  ,    self.examples			)			):
           if ex_index % 1_00_00 == 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.
           UpperCamelCase__	     =      [1 if mask_padding_with_zero else 0] * len(__UpperCamelCase			)

           # Zero-pad up to the sequence length.
           UpperCamelCase__	     =      batch_length - len(__UpperCamelCase			)
           if pad_on_left:
               UpperCamelCase__	     =      ([pad_token] * padding_length) + input_ids
               UpperCamelCase__	     =      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
           else:
               UpperCamelCase__	     =      input_ids + ([pad_token] * padding_length)
               UpperCamelCase__	     =      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

           if len(__UpperCamelCase			) != batch_length:
               raise ValueError(f'Error with input length {len(__UpperCamelCase			)} vs {batch_length}'			)
           if len(__UpperCamelCase			) != batch_length:
               raise ValueError(f'Error with input length {len(__UpperCamelCase			)} vs {batch_length}'			)

           if self.mode == "classification":
               UpperCamelCase__	     =      label_map[example.label]
           elif self.mode == "regression":
               UpperCamelCase__	     =      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(__UpperCamelCase			) for x in input_ids]			)}'			)
               logger.info(f'attention_mask: {" ".join([str(__UpperCamelCase			) for x in attention_mask]			)}'			)
               logger.info(f'label: {example.label} (id = {label})'			)

           features.append(InputFeatures(input_ids=__UpperCamelCase  ,    attention_mask=__UpperCamelCase  ,    label=__UpperCamelCase			)			)

       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)

           UpperCamelCase__	     =      tf.data.Dataset.from_generator(
               __UpperCamelCase  ,    ({"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

           UpperCamelCase__	     =      torch.tensor([f.input_ids for f in features]  ,    dtype=torch.long			)
           UpperCamelCase__	     =      torch.tensor([f.attention_mask for f in features]  ,    dtype=torch.long			)
           if self.mode == "classification":
               UpperCamelCase__	     =      torch.tensor([f.label for f in features]  ,    dtype=torch.long			)
           elif self.mode == "regression":
               UpperCamelCase__	     =      torch.tensor([f.label for f in features]  ,    dtype=torch.float			)

           UpperCamelCase__	     =      TensorDataset(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
           return dataset
       else:
           raise ValueError("return_tensors should be one of 'tf' or 'pt'"			)




