

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__":
      _SCREAMING_SNAKE_CASE							 =			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=1_6)
      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)

      _SCREAMING_SNAKE_CASE							 =			parser.parse_known_args()

      # overwrite batch size until we have tf_glue.py
      _SCREAMING_SNAKE_CASE							 =			1_6
      _SCREAMING_SNAKE_CASE							 =			1_6

      # Set up logging
      _SCREAMING_SNAKE_CASE							 =			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
      _SCREAMING_SNAKE_CASE							 =			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      _SCREAMING_SNAKE_CASE							 =			AutoTokenizer.from_pretrained(args.model_name_or_path)

      # Load dataset
      _SCREAMING_SNAKE_CASE							 =			load_dataset('''imdb''', split=['''train''', '''test'''])
      _SCREAMING_SNAKE_CASE							 =			train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
      _SCREAMING_SNAKE_CASE							 =			test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

      # Preprocess train dataset
      _SCREAMING_SNAKE_CASE							 =			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'''])

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

      # Preprocess test dataset
      _SCREAMING_SNAKE_CASE							 =			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'''])

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

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

      _SCREAMING_SNAKE_CASE							 =			time.time()
      _SCREAMING_SNAKE_CASE							 =			model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
      _SCREAMING_SNAKE_CASE							 =			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_tokenizers_available, is_torch_available


a__       :  List[Any]       		=					{
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__       :  Union[str, Any]       		=					["""RealmTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__       :  Dict       		=					[
       """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """RealmEmbedder""",
       """RealmForOpenQA""",
       """RealmKnowledgeAugEncoder""",
       """RealmPreTrainedModel""",
       """RealmReader""",
       """RealmScorer""",
       """load_tf_weights_in_realm""",
   ]
   a__       :  Any       		=					["""RealmRetriever"""]


if TYPE_CHECKING:
   from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
   from .tokenization_realm import RealmTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_realm import RealmTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_realm import (
          REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
          RealmEmbedder,
          RealmForOpenQA,
          RealmKnowledgeAugEncoder,
          RealmPreTrainedModel,
          RealmReader,
          RealmScorer,
          load_tf_weights_in_realm,
      )
      from .retrieval_realm import RealmRetriever


else:
   import sys

   a__       :  Any       		=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

