





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE   						=		{
    """configuration_prophetnet""": ["""PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ProphetNetConfig"""],
    """tokenization_prophetnet""": ["""ProphetNetTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE   						=		[
						    """PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """ProphetNetDecoder""",
						    """ProphetNetEncoder""",
						    """ProphetNetForCausalLM""",
						    """ProphetNetForConditionalGeneration""",
						    """ProphetNetModel""",
						    """ProphetNetPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
						from .tokenization_prophetnet import ProphetNetTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_prophetnet import (
												    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ProphetNetDecoder,
												    ProphetNetEncoder,
												    ProphetNetForCausalLM,
												    ProphetNetForConditionalGeneration,
												    ProphetNetModel,
												    ProphetNetPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE   						=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


'''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  : 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  : Dict		       =    parser.parse_known_args()

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

  # Set up logging
  __snake_case  : Union[str, Any]		       =    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  : Optional[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  : Optional[int]		       =    load_dataset('imdb', split=['train', 'test'])
  __snake_case  : List[str]		       =    train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  __snake_case  : List[str]		       =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

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

  # Preprocess test dataset
  __snake_case  : Tuple		       =    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  : Optional[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  : List[str]		       =    tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
      args.per_device_eval_batch_size
  )

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

  __snake_case  : Any		       =    time.time()
  __snake_case  : int		       =    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}""")






