


"""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():
			SCREAMING_SNAKE_CASE					:       Dict							   =							True
			SCREAMING_SNAKE_CASE					:       int							   =							'''p3dn.24xlarge'''
			import smdistributed.dataparallel.tensorflow as sdp
else:
			SCREAMING_SNAKE_CASE					:       Any							   =							False






def 						lowercase				(	_snake_case		: List[str]	,  _snake_case		: List[Any]	,  _snake_case		: Dict	,  _snake_case		: Any	,  _snake_case		: Any	,  _snake_case		: List[Any]	,  _snake_case		: Dict=None	)	->List[str]:
	"""simple docstring"""





	__snake_case      :		int         =							tqdm(_lowercase	)
	for i, batch in enumerate(_lowercase	):
		with tf.GradientTape() as tape:
			__snake_case      :		List[Any]         =							batch
			__snake_case      :		Optional[int]         =							model(_lowercase	)
			__snake_case      :		Dict         =							loss(_lowercase	,  outputs.logits	)

		if SDP_ENABLED:
			__snake_case      :		Tuple         =							sdp.DistributedGradientTape(_lowercase	,  sparse_as_dense=_lowercase	)

		__snake_case      :		Tuple         =							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      :		str         =							{'loss': loss_value.numpy()}
	return train_results






def 						lowercase				(	_snake_case		: List[str]	,  _snake_case		: List[str]	,  _snake_case		: Optional[int]	)	->List[Any]:
	"""simple docstring"""





	__snake_case      :		Optional[Any]         =							load_dataset('''imdb'''	,  split=['''train''', '''test''']	)

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

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

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

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

	if SDP_ENABLED:
		__snake_case      :		Dict         =							tf_train_dataset.shard(sdp.size()	,  sdp.rank()	)
		__snake_case      :		str         =							tf_test_dataset.shard(sdp.size()	,  sdp.rank()	)
	__snake_case      :		Union[str, Any]         =							tf_train_dataset.batch(_lowercase	,  drop_remainder=_lowercase	)
	__snake_case      :		List[Any]         =							tf_test_dataset.batch(_lowercase	,  drop_remainder=_lowercase	)

	return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE					:       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"""])

			SCREAMING_SNAKE_CASE					:       int							   =							parser.parse_known_args()

			# Set up logging
			SCREAMING_SNAKE_CASE					:       Tuple							   =							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()

						SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							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
			SCREAMING_SNAKE_CASE					:       Dict							   =							TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
			SCREAMING_SNAKE_CASE					:       Any							   =							AutoTokenizer.from_pretrained(args.model_name_or_path)

			# get datasets
			SCREAMING_SNAKE_CASE					:       Tuple							   =							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
			SCREAMING_SNAKE_CASE					:       Optional[int]							   =							tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
			SCREAMING_SNAKE_CASE					:       int							   =							tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
			SCREAMING_SNAKE_CASE					:       Tuple							   =							[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)
						SCREAMING_SNAKE_CASE					:       List[Any]							   =							time.time()
						SCREAMING_SNAKE_CASE					:       Union[str, Any]							   =							fit(
						    model,
						    loss,
						    optimizer,
						    tf_train_dataset,
						    args.epochs,
						    args.per_device_train_batch_size,
						    max_steps=args.max_steps,
						)
						SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							time.time() - start_train_time
						logger.info("""*** Train ***""")
						logger.info(F'train_runtime = {end_train_time}')

						SCREAMING_SNAKE_CASE					:       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):
						SCREAMING_SNAKE_CASE					:       Optional[int]							   =							model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
						logger.info("""*** Evaluate ***""")

						SCREAMING_SNAKE_CASE					:       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 sys







def     __UpperCamelCase					(				_lowercase       )			->      int:
	if n < 0:
		raise ValueError('Negative arguments are not supported'       )
	return _fib(_lowercase       )[0]







def     __UpperCamelCase					(				_lowercase       )			->      tuple[int, int]:
	if n == 0:  # (F(0), F(1))
		return (0, 1)

	# F(2n) = F(n)[2F(n+1) − F(n)]
	# F(2n+1) = F(n+1)^2+F(n)^2
	_lowercase	,     _lowercase       :							List[str]  	=		_fib(n // 2       )
	_lowercase       :							Dict  	=		a * (b * 2 - a)
	_lowercase       :							Union[str, Any]  	=		a * a + b * b
	return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		_A					:     Optional[Any]      =int(sys.argv[1])
		print(F'''fibonacci({n}) is {fibonacci(n)}''')
