

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

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_		,  A_		,  A_		,  A_		,  A_=None				):
	lowerCAmelCase__			:		List[Any]						       =			tqdm(A_				)
	for i, batch in enumerate(A_				):
		with tf.GradientTape() as tape:
			lowerCAmelCase__       ,lowerCAmelCase__			:		Any						       =			batch
			lowerCAmelCase__			:		Tuple						       =			model(A_				)
			lowerCAmelCase__			:		Any						       =			loss(A_		,  outputs.logits				)

		if SDP_ENABLED:
			lowerCAmelCase__			:		Optional[Any]						       =			sdp.DistributedGradientTape(A_		,  sparse_as_dense=A_				)

		lowerCAmelCase__			:		List[str]						       =			tape.gradient(A_		,  model.trainable_variables				)
		opt.apply_gradients(zip(A_		,  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

	lowerCAmelCase__			:		Optional[int]						       =			{'''loss''': loss_value.numpy()}
	return train_results

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_		,  A_				):
	# Load dataset
	lowerCAmelCase__       ,lowerCAmelCase__			:		Dict						       =			load_dataset('''imdb'''		,  split=['''train''', '''test''']				)

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

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

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

	lowerCAmelCase__			:		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''']
	}
	lowerCAmelCase__			:		str						       =			tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])				)

	if SDP_ENABLED:
		lowerCAmelCase__			:		str						       =			tf_train_dataset.shard(sdp.size()		,  sdp.rank()				)
		lowerCAmelCase__			:		Optional[int]						       =			tf_test_dataset.shard(sdp.size()		,  sdp.rank()				)
	lowerCAmelCase__			:		Optional[int]						       =			tf_train_dataset.batch(A_		,  drop_remainder=A_				)
	lowerCAmelCase__			:		str						       =			tf_test_dataset.batch(A_		,  drop_remainder=A_				)

	return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
							__UpperCamelCase						:   str							   =	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=1_6)
							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'''])

							__UpperCamelCase   ,					__UpperCamelCase						:   str							   =	parser.parse_known_args()

							# Set up logging
							__UpperCamelCase						:   Any							   =	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()

														__UpperCamelCase						:   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
							__UpperCamelCase						:   Optional[int]							   =	TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							__UpperCamelCase						:   Any							   =	AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

														__UpperCamelCase						:   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"""





def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		List[Any]						       =			53_81
	for x in s:
		lowerCAmelCase__			:		Tuple						       =			((hash_value << 5) + hash_value) + ord(A_				)
	return hash_value & 0Xf_f_f_f_f_f_f_f




