

'''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 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    str     ,					lowercase_						:    str     ,					lowercase_						:    PreTrainedTokenizer     ,					lowercase_						:    int     ,					lowercase_						:    Optional[int] = None     ,					)		->	int:




  '''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=lowercase_	)
  lowercase									=list(ds[list(files.keys()	)[0]].features.keys()	)
  lowercase									=features_name.pop(lowercase_	)
  lowercase									=list(set(ds[list(files.keys()	)[0]][label_name]	)	)
  lowercase									={label: i for i, label in enumerate(lowercase_	)}
  lowercase									=tokenizer.model_input_names
  lowercase									={}

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

  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(
          lowercase_     ,					({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(
          lowercase_     ,					({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(
          lowercase_     ,					({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  :		List[Any]  =		logging.getLogger(__name__)

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

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




def 	UpperCamelCase   (       )		->	Optional[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=lowercase_     ,					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(lowercase_	)     ,					labelaid=lowercase_     ,					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=lowercase_     ,					cache_dir=model_args.cache_dir     ,					)

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

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

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

  # 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(lowercase_     ,					'''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(lowercase_	)

  return results


if __name__ == "__main__":
     main()








import numpy as np






def      _snake_case     (__lowercase):
   return 1 / (1 + np.exp(-vector))






def      _snake_case     (__lowercase):
   return vector * sigmoid(__lowercase)


if __name__ == "__main__":
 import doctest

 doctest.testmod()





