

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__":
    lowerCamelCase_					  =   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)

    lowerCamelCase_  ,    lowerCamelCase_					  =   parser.parse_known_args()

    # overwrite batch size until we have tf_glue.py
    lowerCamelCase_					  =   16
    lowerCamelCase_					  =   16

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

    # Load dataset
    lowerCamelCase_  ,    lowerCamelCase_					  =   load_dataset('''imdb''', split=['''train''', '''test'''])
    lowerCamelCase_					  =   train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
    lowerCamelCase_					  =   test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

    # Preprocess train dataset
    lowerCamelCase_					  =   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'''])

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

    # Preprocess test dataset
    lowerCamelCase_					  =   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'''])

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

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

    lowerCamelCase_					  =   time.time()
    lowerCamelCase_					  =   model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
    lowerCamelCase_					  =   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}""")


from math import pi



def   UpperCamelCase(   lowercase_				)      ->							float:





  '''simple docstring'''





  return degree / (180 / pi)


if __name__ == "__main__":
    from doctest import testmod

    testmod()