



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    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A				,    _A = None				,    ):
  a		:      Any       =  {}

  if train_file is not None:
    a		:      Union[str, Any]       =  [train_file]
  if eval_file is not None:
    a		:      Any       =  [eval_file]
  if test_file is not None:
    a		:      Union[str, Any]       =  [test_file]

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

  if len(_SCREAMING_SNAKE_CASE       ) == 1:
    for k in files.keys():
      a		:      List[Any]       =  ds[k].map(
          lambda _A       : 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		:      Any       =  ds[k].map(
          lambda _A       : 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		:      Tuple       =  {k: v for k, v in ex.items() if k in input_names}
      a		:      int       =  labelaid[ex[label_name]]
      yield (d, label)

  def gen_val():
    for ex in transformed_ds[datasets.Split.VALIDATION]:
      a		:      Any       =  {k: v for k, v in ex.items() if k in input_names}
      a		:      List[str]       =  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		:      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[Any]       =  train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]       )       )       )

  a		:      int       =  (
      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		:      Any       =  val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]       )       )       )

  a		:      List[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.TEST in transformed_ds
      else None
  )

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

  return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase:      Any		 =		logging.getLogger(__name__)

@dataclass
class 							a__:
    lowercase__				=					field(metadata={"""help""": """Which column contains the label"""}			)
    lowercase__				=					field(default=lowerCamelCase__	,				metadata={"""help""": """The path of the training file"""}			)
    lowercase__				=					field(default=lowerCamelCase__	,				metadata={"""help""": """The path of the development file"""}			)
    lowercase__				=					field(default=lowerCamelCase__	,				metadata={"""help""": """The path of the test file"""}			)
    lowercase__				=					field(
        default=1_28	,				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=lowerCamelCase__	,				metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)

@dataclass
class 							a__:
    lowercase__				=					field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			)
    lowercase__				=					field(
        default=lowerCamelCase__	,				metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
    lowercase__				=					field(
        default=lowerCamelCase__	,				metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
    lowercase__				=					field(default=lowerCamelCase__	,				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=lowerCamelCase__	,				metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}	,				)
def    lowerCamelCase__ (		):
  a		:      str       =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
  a     ,			a     ,			a		:      Tuple       =  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		:      Any       =  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		:      Dict       =  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		:      Tuple       =  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		:      Optional[Any]       =  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(_A       ) -> Dict:
    a		:      Tuple       =  np.argmax(p.predictions				,    axis=1       )

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

  # Initialize our Trainer
  a		:      Union[str, 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		:      List[str]       =  {}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'       )
    a		:      Dict       =  trainer.evaluate()
    a		:      List[Any]       =  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'''






class 							a__:







    def __init__(   self				:						Dict		,    __snake_case				:						Optional[int]		,    __snake_case				:						Any		,    __snake_case				:						Tuple			):
      a		:      List[str]       =  name
      a		:      Dict       =  value
      a		:      List[str]       =  weight







    def __repr__(   self				:						int			):
      return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""







    def     lowercase_ (   self				:						Optional[int]			):
      return self.value







    def     lowercase_ (   self				:						List[str]			):
      return self.name







    def     lowercase_ (   self				:						int			):
      return self.weight







    def     lowercase_ (   self				:						List[str]			):
      return self.value / self.weight
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[int]       =  []
  for i in range(len(_A       )       ):
    menu.append(Things(name[i]				,    value[i]				,    weight[i]       )       )
  return menu
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  sorted(_A				,    key=_A				,    reverse=_A       )
  a		:      Optional[int]       =  []
  a     ,			a		:      str       =  0.0, 0.0
  for i in range(len(_A       )       ):
    if (total_cost + items_copy[i].get_weight()) <= max_cost:
      result.append(items_copy[i]       )
      total_cost += items_copy[i].get_weight()
      total_value += items_copy[i].get_value()
  return (result, total_value)
def    lowerCamelCase__ (		):
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()