




'''simple docstring'''







import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('SDP_ENABLED') or is_sagemaker_dp_enabled():
						_lowerCamelCase							:						int								=  True
						_lowerCamelCase							:						Tuple								=  'p3dn.24xlarge'
						import smdistributed.dataparallel.tensorflow as sdp
else:
						_lowerCamelCase							:						Optional[int]								=  False






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_=None  ):



		'''simple docstring'''







		_snake_case	       =       tqdm(lowercase_  )
		for i, batch in enumerate(lowercase_  ):
				with tf.GradientTape() as tape:
						_snake_case       ,    _snake_case	       =       batch
						_snake_case	       =       model(lowercase_  )
						_snake_case	       =       loss(lowercase_						,							outputs.logits  )

				if SDP_ENABLED:
						_snake_case	       =       sdp.DistributedGradientTape(lowercase_						,							sparse_as_dense=lowercase_  )

				_snake_case	       =       tape.gradient(lowercase_						,							model.trainable_variables  )
				opt.apply_gradients(zip(lowercase_						,							model.trainable_variables  )  )

				pbar.set_description(f'''Loss: {loss_value:.4f}'''  )

				if SDP_ENABLED and i == 0:
						sdp.broadcast_variables(model.variables						,							root_rank=0  )
						sdp.broadcast_variables(opt.variables()						,							root_rank=0  )

				if max_steps and i >= max_steps:
						break

		_snake_case	       =       {"loss": loss_value.numpy()}
		return train_results






def 		_lowercase		(	lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case       ,    _snake_case	       =       load_dataset("imdb"						,							split=["train", "test"]  )

		# Preprocess train dataset
		_snake_case	       =       train_dataset.map(
		    lambda lowercase_  : tokenizer(e["text"]						,							truncation=lowercase_						,							padding="max_length"  )						,							batched=lowercase_  )
		train_dataset.set_format(type="tensorflow"						,							columns=["input_ids", "attention_mask", "label"]  )

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

		# Preprocess test dataset
		_snake_case	       =       test_dataset.map(
		    lambda lowercase_  : tokenizer(e["text"]						,							truncation=lowercase_						,							padding="max_length"  )						,							batched=lowercase_  )
		test_dataset.set_format(type="tensorflow"						,							columns=["input_ids", "attention_mask", "label"]  )

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

		if SDP_ENABLED:
				_snake_case	       =       tf_train_dataset.shard(sdp.size()						,							sdp.rank()  )
				_snake_case	       =       tf_test_dataset.shard(sdp.size()						,							sdp.rank()  )
		_snake_case	       =       tf_train_dataset.batch(lowercase_						,							drop_remainder=lowercase_  )
		_snake_case	       =       tf_test_dataset.batch(lowercase_						,							drop_remainder=lowercase_  )

		return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
						_lowerCamelCase							:						List[Any]								=  argparse.ArgumentParser()

						# Hyperparameters sent by the client are passed as command-line arguments to the script.
						parser.add_argument('--epochs', type=int, default=3)
						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)
						parser.add_argument('--max_steps', type=int, default=None)

						# Data, model, and output directories
						parser.add_argument('--output_data_dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
						parser.add_argument('--model_dir', type=str, default=os.environ['SM_MODEL_DIR'])
						parser.add_argument('--n_gpus', type=str, default=os.environ['SM_NUM_GPUS'])

						_lowerCamelCase	,_lowerCamelCase							:						str								=  parser.parse_known_args()

						# Set up logging
						_lowerCamelCase							:						Optional[int]								=  logging.getLogger(__name__)

						logging.basicConfig(
						    level=logging.getLevelName('INFO'),
						    handlers=[logging.StreamHandler(sys.stdout)],
						    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
						)

						if SDP_ENABLED:
												sdp.init()

												_lowerCamelCase							:						Optional[int]								=  tf.config.experimental.list_physical_devices('GPU')
												for gpu in gpus:
																		tf.config.experimental.set_memory_growth(gpu, True)
												if gpus:
																		tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], 'GPU')

    # Load model and tokenizer
						_lowerCamelCase							:						Tuple								=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_lowerCamelCase							:						Optional[int]								=  AutoTokenizer.from_pretrained(args.model_name_or_path)

						# get datasets
						_lowerCamelCase	,_lowerCamelCase							:						int								=  get_datasets(
						    tokenizer=tokenizer,
						    train_batch_size=args.per_device_train_batch_size,
						    eval_batch_size=args.per_device_eval_batch_size,
						)

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

						# Training
						if args.do_train:
												# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
												_lowerCamelCase							:						Tuple								=  time.time()
												_lowerCamelCase							:						List[Any]								=  fit(
												    model,
												    loss,
												    optimizer,
												    tf_train_dataset,
												    args.epochs,
												    args.per_device_train_batch_size,
												    max_steps=args.max_steps,
												)
												_lowerCamelCase							:						str								=  time.time() - start_train_time
												logger.info('*** Train ***')
												logger.info(F'''train_runtime = {end_train_time}''')

												_lowerCamelCase							:						List[str]								=  os.path.join(args.output_dir, 'train_results.txt')

												if not SDP_ENABLED or sdp.rank() == 0:
																		with open(output_eval_file, 'w') as writer:
																								logger.info('***** Train results *****')
																								logger.info(train_results)
																								for key, value in train_results.items():
																														logger.info(F'''  {key} = {value}''')
																														writer.write(F'''{key} = {value}\n''')

    # Evaluation
						if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
												_lowerCamelCase							:						Tuple								=  model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
												logger.info('*** Evaluate ***')

												_lowerCamelCase							:						List[str]								=  os.path.join(args.output_dir, 'eval_results.txt')

												with open(output_eval_file, 'w') as writer:
																		logger.info('***** Eval results *****')
																		logger.info(result)
																		for key, value in result.items():
																								logger.info(F'''  {key} = {value}''')
																								writer.write(F'''{key} = {value}\n''')

    # Save result
						if SDP_ENABLED:
												if sdp.rank() == 0:
																		model.save_pretrained(args.output_dir)
																		tokenizer.save_pretrained(args.output_dir)
						else:
												model.save_pretrained(args.output_dir)
												tokenizer.save_pretrained(args.output_dir)











'''simple docstring'''







from __future__ import annotations

import math
from itertools import permutations






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5						,							int(math.sqrt(lowercase_  ) + 1  )						,							6  ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 		_lowercase		(	lowercase_ = 7  ):



		'''simple docstring'''







		_snake_case	       =       "".join(str(lowercase_  ) for i in range(1						,							n + 1  )  )
		_snake_case	       =       [int("".join(lowercase_  )  ) for i in permutations(lowercase_						,							lowercase_  )]
		_snake_case	       =       [num for num in perm_list if is_prime(lowercase_  )]
		return max(lowercase_  ) if pandigitals else 0


if __name__ == "__main__":
						print(F'''{solution() = }''')





