



"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase = None ,  ) ->						Optional[Any]:







  '''simple docstring'''


  lowercase_								= {}

  if train_file is not None:
    lowercase_								= [train_file]
  if eval_file is not None:
    lowercase_								= [eval_file]
  if test_file is not None:
    lowercase_								= [test_file]

  lowercase_								= datasets.load_dataset("""csv""" ,  data_files=__lowerCAmelCase							)
  lowercase_								= list(ds[list(files.keys()							)[0]].features.keys()							)
  lowercase_								= features_name.pop(__lowerCAmelCase							)
  lowercase_								= list(set(ds[list(files.keys()							)[0]][label_name]							)							)
  lowercase_								= {label: i for i, label in enumerate(__lowerCAmelCase							)}
  lowercase_								= tokenizer.model_input_names
  lowercase_								= {}

  if len(__lowerCAmelCase							) == 1:
    for k in files.keys():
      lowercase_								= ds[k].map(
          lambda __lowerCAmelCase							: tokenizer.batch_encode_plus(
              example[features_name[0]] ,  truncation=__lowerCAmelCase ,  max_length=__lowerCAmelCase ,  padding="""max_length"""							) ,  batched=__lowerCAmelCase ,  )
  elif len(__lowerCAmelCase							) == 2:
    for k in files.keys():
      lowercase_								= ds[k].map(
          lambda __lowerCAmelCase							: tokenizer.batch_encode_plus(
              (example[features_name[0]], example[features_name[1]]) ,  truncation=__lowerCAmelCase ,  max_length=__lowerCAmelCase ,  padding="""max_length""" ,  ) ,  batched=__lowerCAmelCase ,  )

  def gen_train():
    for ex in transformed_ds[datasets.Split.TRAIN]:
      lowercase_								= {k: v for k, v in ex.items() if k in input_names}
      lowercase_								= labelaid[ex[label_name]]
      yield (d, label)

  def gen_val():
    for ex in transformed_ds[datasets.Split.VALIDATION]:
      lowercase_								= {k: v for k, v in ex.items() if k in input_names}
      lowercase_								= labelaid[ex[label_name]]
      yield (d, label)

  def gen_test():
    for ex in transformed_ds[datasets.Split.TEST]:
      lowercase_								= {k: v for k, v in ex.items() if k in input_names}
      lowercase_								= labelaid[ex[label_name]]
      yield (d, label)

  lowercase_								= (
      tf.data.Dataset.from_generator(
          __lowerCAmelCase ,  ({k: tf.intaa for k in input_names}, tf.intaa) ,  ({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							)) ,  )
      if datasets.Split.TRAIN in transformed_ds
      else None
  )

  if train_ds is not None:
    lowercase_								= train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]							)							)							)

  lowercase_								= (
      tf.data.Dataset.from_generator(
          __lowerCAmelCase ,  ({k: tf.intaa for k in input_names}, tf.intaa) ,  ({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							)) ,  )
      if datasets.Split.VALIDATION in transformed_ds
      else None
  )

  if val_ds is not None:
    lowercase_								= val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]							)							)							)

  lowercase_								= (
      tf.data.Dataset.from_generator(
          __lowerCAmelCase ,  ({k: tf.intaa for k in input_names}, tf.intaa) ,  ({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							)) ,  )
      if datasets.Split.TEST in transformed_ds
      else None
  )

  if test_ds is not None:
    lowercase_								= test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]							)							)							)

  return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase						:		Tuple			= logging.getLogger(__name__)


@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(metadata={"help": "Which column contains the label"}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "The path of the training file"}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "The path of the development file"}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "The path of the test file"}   )
    lowercase__      =   field(
        default=128      ,  metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        }      ,  )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "Overwrite the cached training and evaluation sets"}   )







@dataclass
class    SCREAMING_SNAKE_CASE__ :
    lowercase__      =   field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
    lowercase__      =   field(default=__UpperCAmelCase      ,  metadata={"help": "Set this flag to use fast tokenization."}   )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    lowercase__      =   field(
        default=__UpperCAmelCase      ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,  )







def 			_SCREAMING_SNAKE_CASE						() ->						int:







  '''simple docstring'''


  lowercase_								= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
  lowercase_					,    lowercase_					,    lowercase_								= parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir							)
      and os.listdir(training_args.output_dir							)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
        """ --overwrite_output_dir to overcome."""							)

  # Setup logging
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,  datefmt="""%m/%d/%Y %H:%M:%S""" ,  level=logging.INFO ,  )
  logger.info(
      F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1							)}, '''
      F'''16-bits training: {training_args.fpaa}'''							)
  logger.info(F'''Training/evaluation parameters {training_args}'''							)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  lowercase_								= AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,  cache_dir=model_args.cache_dir ,  )

  lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= get_tfds(
      train_file=data_args.train_file ,  eval_file=data_args.dev_file ,  test_file=data_args.test_file ,  tokenizer=__lowerCAmelCase ,  label_column_id=data_args.label_column_id ,  max_seq_length=data_args.max_seq_length ,  )

  lowercase_								= AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path ,  num_labels=len(__lowerCAmelCase							) ,  labelaid=__lowerCAmelCase ,  idalabel={id: label for label, id in labelaid.items()} ,  finetuning_task="""text-classification""" ,  cache_dir=model_args.cache_dir ,  )

  with training_args.strategy.scope():
    lowercase_								= TFAutoModelForSequenceClassification.from_pretrained(
        model_args.model_name_or_path ,  from_pt=bool(""".bin""" in model_args.model_name_or_path							) ,  config=__lowerCAmelCase ,  cache_dir=model_args.cache_dir ,  )

  def compute_metrics(__lowerCAmelCase							) -> Dict:
    lowercase_								= np.argmax(p.predictions ,  axis=1							)

    return {"acc": (preds == p.label_ids).mean()}

  # Initialize our Trainer
  lowercase_								= TFTrainer(
      model=__lowerCAmelCase ,  args=__lowerCAmelCase ,  train_dataset=__lowerCAmelCase ,  eval_dataset=__lowerCAmelCase ,  compute_metrics=__lowerCAmelCase ,  )

  # Training
  if training_args.do_train:
    trainer.train()
    trainer.save_model()
    tokenizer.save_pretrained(training_args.output_dir							)

  # Evaluation
  lowercase_								= {}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""							)
    lowercase_								= trainer.evaluate()
    lowercase_								= os.path.join(training_args.output_dir ,  """eval_results.txt"""							)

    with open(__lowerCAmelCase ,  """w"""							) as writer:
      logger.info("""***** Eval results *****"""							)

      for key, value in result.items():
        logger.info(F'''  {key} = {value}'''							)
        writer.write(F'''{key} = {value}\n'''							)

      results.update(__lowerCAmelCase							)

  return results


if __name__ == "__main__":
       main()






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase						:		List[Any]			= {"tokenization_bertweet": ["BertweetTokenizer"]}


if TYPE_CHECKING:
       from .tokenization_bertweet import BertweetTokenizer

else:
       import sys

       UpperCAmelCase						:		Tuple			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

