
'''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        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  PreTrainedTokenizer			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  Optional[int] = None			,     )				->       List[str]:





  """simple docstring"""

  __A		:     Dict												=		{}

  if train_file is not None:
    __A		:     int												=		[train_file]
  if eval_file is not None:
    __A		:     Any												=		[eval_file]
  if test_file is not None:
    __A		:     List[Any]												=		[test_file]

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

  if len(__SCREAMING_SNAKE_CASE ) == 1:
    for k in files.keys():
      __A		:     List[str]												=		ds[k].map(
          lambda __SCREAMING_SNAKE_CASE : tokenizer.batch_encode_plus(
              example[features_name[0]]			,     truncation=__SCREAMING_SNAKE_CASE			,     max_length=__SCREAMING_SNAKE_CASE			,     padding="""max_length""" )			,     batched=__SCREAMING_SNAKE_CASE			,     )
  elif len(__SCREAMING_SNAKE_CASE ) == 2:
    for k in files.keys():
      __A		:     Union[str, Any]												=		ds[k].map(
          lambda __SCREAMING_SNAKE_CASE : tokenizer.batch_encode_plus(
              (example[features_name[0]], example[features_name[1]])			,     truncation=__SCREAMING_SNAKE_CASE			,     max_length=__SCREAMING_SNAKE_CASE			,     padding="""max_length"""			,     )			,     batched=__SCREAMING_SNAKE_CASE			,     )

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

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

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

  __A		:     Optional[Any]												=		(
      tf.data.Dataset.from_generator(
          __SCREAMING_SNAKE_CASE			,     ({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:
    __A		:     List[str]												=		train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

  __A		:     Union[str, Any]												=		(
      tf.data.Dataset.from_generator(
          __SCREAMING_SNAKE_CASE			,     ({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:
    __A		:     str												=		val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

  __A		:     str												=		(
      tf.data.Dataset.from_generator(
          __SCREAMING_SNAKE_CASE			,     ({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:
    __A		:     Optional[int]												=		test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

  return train_ds, val_ds, test_ds, labelaid


A__   :		Dict 		=logging.getLogger(__name__)







@dataclass
class  __A				:
 lowerCamelCase				       =field(metadata={'''help''': '''Which column contains the label'''}			)
 lowerCamelCase				       =field(default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''The path of the training file'''}			)
 lowerCamelCase				       =field(default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''The path of the development file'''}			)
 lowerCamelCase				       =field(default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''The path of the test file'''}			)
 lowerCamelCase				       =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.'''
         )
     }    ,						)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)







@dataclass
class  __A				:
 lowerCamelCase				       =field(
     metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}			)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}			)
 lowerCamelCase				       =field(default=_SCREAMING_SNAKE_CASE    ,						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.
 lowerCamelCase				       =field(
     default=_SCREAMING_SNAKE_CASE    ,						metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}    ,						)







def        A_     (						)				->       Any:





  """simple docstring"""

  __A		:     Any												=		HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
  __A							,					__A							,					__A		:     List[str]												=		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.

  __A		:     int												=		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			,     )

  __A							,					__A							,					__A							,					__A		:     Optional[Any]												=		get_tfds(
      train_file=data_args.train_file			,     eval_file=data_args.dev_file			,     test_file=data_args.test_file			,     tokenizer=__SCREAMING_SNAKE_CASE			,     label_column_id=data_args.label_column_id			,     max_seq_length=data_args.max_seq_length			,     )

  __A		:     Union[str, Any]												=		AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path			,     num_labels=len(__SCREAMING_SNAKE_CASE )			,     labelaid=__SCREAMING_SNAKE_CASE			,     idalabel={id: label for label, id in labelaid.items()}			,     finetuning_task="""text-classification"""			,     cache_dir=model_args.cache_dir			,     )

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

  def compute_metrics(__SCREAMING_SNAKE_CASE						:  EvalPrediction ) -> Dict:
    __A		:     Optional[int]												=		np.argmax(p.predictions			,     axis=1 )

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

  # Initialize our Trainer
  __A		:     List[Any]												=		TFTrainer(
      model=__SCREAMING_SNAKE_CASE			,     args=__SCREAMING_SNAKE_CASE			,     train_dataset=__SCREAMING_SNAKE_CASE			,     eval_dataset=__SCREAMING_SNAKE_CASE			,     compute_metrics=__SCREAMING_SNAKE_CASE			,     )

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

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

    with open(__SCREAMING_SNAKE_CASE			,     """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(__SCREAMING_SNAKE_CASE )

  return results


if __name__ == "__main__":
     main()





'''simple docstring'''
from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
     import accelerate







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Optional[int]:





  """simple docstring"""

  if not is_accelerate_available():
    return method
  __A		:     int												=		version.parse(accelerate.__version__ ).base_version
  if version.parse(__SCREAMING_SNAKE_CASE ) < version.parse("""0.17.0""" ):
    return method

  def wrapper(self						:  str			,     *__SCREAMING_SNAKE_CASE						:  Tuple			,     **__SCREAMING_SNAKE_CASE						:  Tuple ):
    if hasattr(self			,     """_hf_hook""" ) and hasattr(self._hf_hook			,     """pre_forward""" ):
      self._hf_hook.pre_forward(self )
    return method(self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE )

  return wrapper



