




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():
	a__		    =    True
	a__		    =    '''p3dn.24xlarge'''
	import smdistributed.dataparallel.tensorflow as sdp
else:
	a__		    =    False





def 							A__   (snake_case      :		Tuple		,  snake_case      :		Dict		,  snake_case      :		List[Any]		,  snake_case      :		Any		,  snake_case      :		Tuple		,  snake_case      :		List[Any]		,  snake_case      :		int=None							)    ->	Tuple:
					__UpperCamelCase				:  Dict								=			tqdm(snake_case							)
					for i, batch in enumerate(snake_case							):
										with tf.GradientTape() as tape:
															__UpperCamelCase			, __UpperCamelCase				:  List[str]								=			batch
															__UpperCamelCase				:  int								=			model(snake_case							)
															__UpperCamelCase				:  List[str]								=			loss(snake_case		,  outputs.logits							)

										if SDP_ENABLED:
															__UpperCamelCase				:  str								=			sdp.DistributedGradientTape(snake_case		,  sparse_as_dense=snake_case							)

										__UpperCamelCase				:  int								=			tape.gradient(snake_case		,  model.trainable_variables							)
										opt.apply_gradients(zip(snake_case		,  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

					__UpperCamelCase				:  List[Any]								=			{"""loss""": loss_value.numpy()}
					return train_results





def 							A__   (snake_case      :		Optional[Any]		,  snake_case      :		Any		,  snake_case      :		Optional[Any]							)    ->	Dict:
					# Load dataset
					__UpperCamelCase			, __UpperCamelCase				:  Dict								=			load_dataset("""imdb"""		,  split=["""train""", """test"""]							)

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

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

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

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

					if SDP_ENABLED:
										__UpperCamelCase				:  str								=			tf_train_dataset.shard(sdp.size()		,  sdp.rank()							)
										__UpperCamelCase				:  List[str]								=			tf_test_dataset.shard(sdp.size()		,  sdp.rank()							)
					__UpperCamelCase				:  List[str]								=			tf_train_dataset.batch(snake_case		,  drop_remainder=snake_case							)
					__UpperCamelCase				:  Dict								=			tf_test_dataset.batch(snake_case		,  drop_remainder=snake_case							)

					return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
	a__		    =    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'''])

	a__    ,       a__		    =    parser.parse_known_args()

	# Set up logging
	a__		    =    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()

		a__		    =    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
	a__		    =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
	a__		    =    AutoTokenizer.from_pretrained(args.model_name_or_path)

	# get datasets
	a__    ,       a__		    =    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
	a__		    =    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
	a__		    =    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
	a__		    =    [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)
		a__		    =    time.time()
		a__		    =    fit(
		    model,
		    loss,
		    optimizer,
		    tf_train_dataset,
		    args.epochs,
		    args.per_device_train_batch_size,
		    max_steps=args.max_steps,
		)
		a__		    =    time.time() - start_train_time
		logger.info('''*** Train ***''')
		logger.info(f"train_runtime = {end_train_time}")

		a__		    =    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):
		a__		    =    model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
		logger.info('''*** Evaluate ***''')

		a__		    =    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)












import os





def 							A__   ()    ->	int:
					__UpperCamelCase				:  int								=			os.path.dirname(os.path.realpath(snake_case							)							)
					__UpperCamelCase				:  List[str]								=			os.path.join(snake_case		,  """triangle.txt"""							)

					with open(snake_case							) as in_file:
										__UpperCamelCase				:  Optional[Any]								=			[[int(snake_case							) for i in line.split()] for line in in_file]

					while len(snake_case							) != 1:
										__UpperCamelCase				:  Union[str, Any]								=			triangle.pop()
										__UpperCamelCase				:  List[str]								=			triangle[-1]
										for j in range(len(snake_case							) - 1							):
															curr_row[j] += max(last_row[j]		,  last_row[j + 1]							)
					return triangle[0][0]


if __name__ == "__main__":
	print(solution())






