


'''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__":
						__magic_name__   :   Union[str, Any]  							=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)

						__magic_name__							,    __magic_name__   :   int  							=parser.parse_known_args()

						# overwrite batch size until we have tf_glue.py
						__magic_name__   :   Any  							=16
						__magic_name__   :   List[Any]  							=16

						# Set up logging
						__magic_name__   :   Optional[Any]  							=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
						__magic_name__   :   int  							=TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						__magic_name__   :   Dict  							=AutoTokenizer.from_pretrained(args.model_name_or_path)

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

						# Preprocess train dataset
						__magic_name__   :   Tuple  							=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'])

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

						# Preprocess test dataset
						__magic_name__   :   List[str]  							=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'])

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

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

						__magic_name__   :   str  							=time.time()
						__magic_name__   :   Optional[int]  							=model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						__magic_name__   :   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'''
import os
import sys
import time

from . import transposition_cipher as trans_cipher




def      __snake_case       (       ):
				'''simple docstring'''




				__magic_name__	  = "Prehistoric Men.txt"
				__magic_name__	  = "Output.txt"
				__magic_name__	  = int(input("Enter key: "						)						)
				__magic_name__	  = input("Encrypt/Decrypt [e/d]: "						)

				if not os.path.exists(lowerCamelCase_						):
								print(F'File {input_file} does not exist. Quitting...'						)
								sys.exit()
				if os.path.exists(lowerCamelCase_						):
								print(F'Overwrite {output_file}? [y/n]'						)
								__magic_name__	  = input("> "						)
								if not response.lower().startswith("y"						):
												sys.exit()

				__magic_name__	  = time.time()
				if mode.lower().startswith("e"						):
								with open(lowerCamelCase_						) as f:
												__magic_name__	  = f.read()
								__magic_name__	  = trans_cipher.encrypt_message(lowerCamelCase_	,  lowerCamelCase_						)
				elif mode.lower().startswith("d"						):
								with open(lowerCamelCase_						) as f:
												__magic_name__	  = f.read()
								__magic_name__	  = trans_cipher.decrypt_message(lowerCamelCase_	,  lowerCamelCase_						)

				with open(lowerCamelCase_	,  "w"						) as output_obj:
								output_obj.write(lowerCamelCase_						)

				__magic_name__	  = round(time.time() - start_time	,  2						)
				print(("Done (", total_time, "seconds )")						)


if __name__ == "__main__":
						main()



