'''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       :   Tuple  		=    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       :   int  		=    parser.parse_known_args()

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

      # Set up logging
      __snake_case       :   str  		=    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[int]  		=    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      __snake_case       :   Tuple  		=    AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

      # Preprocess test dataset
      __snake_case       :   Any  		=    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       :   str  		=    {
          x: test_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((test_features, test_dataset['''label'''])).batch(
          args.per_device_eval_batch_size
      )

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

      __snake_case       :   Any  		=    time.time()
      __snake_case       :   List[str]  		=    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
      __snake_case       :   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_torch_available, is_vision_available


__snake_case       :   int  		=    {
    '''configuration_videomae''': ['''VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VideoMAEConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   Optional[Any]  		=    [
          '''VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''VideoMAEForPreTraining''',
          '''VideoMAEModel''',
          '''VideoMAEPreTrainedModel''',
          '''VideoMAEForVideoClassification''',
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __snake_case       :   str  		=    ['''VideoMAEFeatureExtractor''']
      __snake_case       :   Any  		=    ['''VideoMAEImageProcessor''']

if TYPE_CHECKING:
      from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_videomae import (
                VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
                VideoMAEForPreTraining,
                VideoMAEForVideoClassification,
                VideoMAEModel,
                VideoMAEPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_videomae import VideoMAEFeatureExtractor
            from .image_processing_videomae import VideoMAEImageProcessor

else:
      import sys

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