

"""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__":
	__A     :    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)

	__A     :    str    =		parser.parse_known_args()

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

	# Set up logging
	__A     :    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
	__A     :    Any    =		TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
	__A     :    Optional[Any]    =		AutoTokenizer.from_pretrained(args.model_name_or_path)

	# Load dataset
	__A     :    Tuple    =		load_dataset('imdb', split=['train', 'test'])
	__A     :    Optional[Any]    =		train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
	__A     :    Dict    =		test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

	# Preprocess train dataset
	__A     :    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'])

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

	# Preprocess test dataset
	__A     :    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'])

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

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

	__A     :    Any    =		time.time()
	__A     :    Optional[Any]    =		model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
	__A     :    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__)






