






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=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)

		__SCREAMING_SNAKE_CASE      =      parser.parse_known_args()

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

		# 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(50_00))  # smaller the size for train dataset to 5k
		__SCREAMING_SNAKE_CASE      =      test_dataset.shuffle().select(range(5_00))  # 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 __future__ import annotations


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Union[str, Any]				  = [0] * len(a__   )
						for i in range(1    ,			len(a__   )   ):
												lowerCAmelCase      :Optional[int]				  = duration_times[i - 1] + waiting_times[i - 1]
						return waiting_times


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						return [
						    duration_time + waiting_times[i]
						    for i, duration_time in enumerate(a__   )
						]


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						return sum(a__   ) / len(a__   )


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						return sum(a__   ) / len(a__   )


if __name__ == "__main__":
		# process id's
		__SCREAMING_SNAKE_CASE      =      [1, 2, 3]

		# ensure that we actually have processes
		if len(processes) == 0:
				print('Zero amount of processes')
				raise SystemExit(0)

		# duration time of all processes
		__SCREAMING_SNAKE_CASE      =      [19, 8, 9]

		# ensure we can match each id to a duration time
		if len(duration_times) != len(processes):
				print('Unable to match all id\'s with their duration time')
				raise SystemExit(0)

		# get the waiting times and the turnaround times
		__SCREAMING_SNAKE_CASE      =      calculate_waiting_times(duration_times)
		__SCREAMING_SNAKE_CASE      =      calculate_turnaround_times(duration_times, waiting_times)

		# get the average times
		__SCREAMING_SNAKE_CASE      =      calculate_average_waiting_time(waiting_times)
		__SCREAMING_SNAKE_CASE      =      calculate_average_turnaround_time(turnaround_times)

		# print all the results
		print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
		for i, process in enumerate(processes):
				print(
				    F"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
				    F"""{turnaround_times[i]}"""
				)
		print(F"""Average waiting time = {average_waiting_time}""")
		print(F"""Average turn around time = {average_turnaround_time}""")