'''simple docstring'''




import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
  __snake_case   :      Union[str, Any]						    =  argparse.ArgumentParser()

  # Hyperparameters sent by the client are passed as command-line arguments to the script.
  parser.add_argument('--epochs', type=int, default=1)
  parser.add_argument('--per_device_train_batch_size', type=int, default=16)
  parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
  parser.add_argument('--model_name_or_path', type=str)
  parser.add_argument('--learning_rate', type=str, default=5E-5)
  parser.add_argument('--do_train', type=bool, default=True)
  parser.add_argument('--do_eval', type=bool, default=True)
  parser.add_argument('--output_dir', type=str)

  __snake_case    ,    __snake_case   :      str						    =  parser.parse_known_args()

  # overwrite batch size until we have tf_glue.py
  __snake_case   :      List[Any]						    =  16
  __snake_case   :      List[str]						    =  16

  # Set up logging
  __snake_case   :      Dict						    =  logging.getLogger(__name__)

  logging.basicConfig(
      level=logging.getLevelName('INFO'),
      handlers=[logging.StreamHandler(sys.stdout)],
      format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
  )

  # Load model and tokenizer
  __snake_case   :      Any						    =  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  __snake_case   :      Optional[Any]						    =  AutoTokenizer.from_pretrained(args.model_name_or_path)

  # Load dataset
  __snake_case    ,    __snake_case   :      Tuple						    =  load_dataset('imdb', split=['train', 'test'])
  __snake_case   :      Optional[Any]						    =  train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  __snake_case   :      Dict						    =  test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

  # Preprocess train dataset
  __snake_case   :      List[Any]						    =  train_dataset.map(
      lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
  )
  train_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

  __snake_case   :      Any						    =  {
      x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case   :      int						    =  tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
      args.per_device_train_batch_size
  )

  # Preprocess test dataset
  __snake_case   :      str						    =  test_dataset.map(
      lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
  )
  test_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

  __snake_case   :      int						    =  {
      x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case   :      Tuple						    =  tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
      args.per_device_eval_batch_size
  )

  # fine optimizer and loss
  __snake_case   :      int						    =  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  __snake_case   :      str						    =  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  __snake_case   :      str						    =  [tf.keras.metrics.SparseCategoricalAccuracy()]
  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

  __snake_case   :      Any						    =  time.time()
  __snake_case   :      Optional[Any]						    =  model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  __snake_case   :      Union[str, Any]						    =  time.time() - start_train_time

  logger.info('*** Train ***')
  logger.info(F"""train_runtime = {end_train_time}""")
  for key, value in train_results.history.items():
    logger.info(F"""  {key} = {value}""")







'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__snake_case   :      Optional[int]						    =  {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case   :      Optional[Any]						    =  ['MBart50Tokenizer']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case   :      Dict						    =  ['MBart50TokenizerFast']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa import MBartaaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
  import sys

  __snake_case   :      Tuple						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






