
'''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__":
						UpperCAmelCase            = 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)

						UpperCAmelCase       ,						UpperCAmelCase            = parser.parse_known_args()

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

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

						# Load dataset
						UpperCAmelCase       ,						UpperCAmelCase            = load_dataset("imdb", split=["train", "test"])
						UpperCAmelCase            = train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
						UpperCAmelCase            = test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

						# Preprocess train dataset
						UpperCAmelCase            = 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"])

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

						# Preprocess test dataset
						UpperCAmelCase            = 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"])

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

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

						UpperCAmelCase            = time.time()
						UpperCAmelCase            = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						UpperCAmelCase            = 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'''

def __UpperCamelCase (      __lowerCamelCase		:			float   ,	__lowerCamelCase		:			float   ,	__lowerCamelCase		:			float					)   ->  float:







     '''simple docstring'''






     if mass < 0:
          raise ValueError("The mass of the body cannot be negative"					)
     if radius <= 0:
          raise ValueError("The radius is always a positive non zero integer"					)
     return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

