





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


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_=None		) ->  Optional[Any]:
		lowerCamelCase :			Union[str, Any]    		=tqdm(_UpperCamelCase		)
		for i, batch in enumerate(_UpperCamelCase		):
				with tf.GradientTape() as tape:
						lowerCamelCase :			Optional[int]    		=batch
						lowerCamelCase :			Union[str, Any]    		=model(_UpperCamelCase		)
						lowerCamelCase :			Any    		=loss(_UpperCamelCase				,							outputs.logits		)

				if SDP_ENABLED:
						lowerCamelCase :			Optional[Any]    		=sdp.DistributedGradientTape(_UpperCamelCase				,							sparse_as_dense=_UpperCamelCase		)

				lowerCamelCase :			List[Any]    		=tape.gradient(_UpperCamelCase				,							model.trainable_variables		)
				opt.apply_gradients(zip(_UpperCamelCase				,							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 :			Dict    		={'''loss''': loss_value.numpy()}
		return train_results


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  str:
		# Load dataset
		lowerCamelCase :			Union[str, Any]    		=load_dataset('''imdb'''				,							split=['''train''', '''test''']		)

		# Preprocess train dataset
		lowerCamelCase :			List[Any]    		=train_dataset.map(
		    lambda SCREAMING_SNAKE_CASE_		: tokenizer(e['''text''']				,							truncation=_UpperCamelCase				,							padding='''max_length'''		)				,							batched=_UpperCamelCase		)
		train_dataset.set_format(type='''tensorflow'''				,							columns=['''input_ids''', '''attention_mask''', '''label''']		)

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

		# Preprocess test dataset
		lowerCamelCase :			Optional[int]    		=test_dataset.map(
		    lambda SCREAMING_SNAKE_CASE_		: tokenizer(e['''text''']				,							truncation=_UpperCamelCase				,							padding='''max_length'''		)				,							batched=_UpperCamelCase		)
		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 :			Optional[int]    		=tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])		)

		if SDP_ENABLED:
				lowerCamelCase :			Dict    		=tf_train_dataset.shard(sdp.size()				,							sdp.rank()		)
				lowerCamelCase :			Optional[Any]    		=tf_test_dataset.shard(sdp.size()				,							sdp.rank()		)
		lowerCamelCase :			Optional[int]    		=tf_train_dataset.batch(_UpperCamelCase				,							drop_remainder=_UpperCamelCase		)
		lowerCamelCase :			int    		=tf_test_dataset.batch(_UpperCamelCase				,							drop_remainder=_UpperCamelCase		)

		return tf_train_dataset, tf_test_dataset


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

				snake_case_				=      parser.parse_known_args()

				# Set up logging
				snake_case_				=      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()

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

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

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

								snake_case_				=      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)






UpperCAmelCase__			:   Optional[int]      			=    [0.4_85, 0.4_56, 0.4_06]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.2_29, 0.2_24, 0.2_25]
UpperCAmelCase__			:   Union[str, Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   Optional[Any]      			=    [0.5, 0.5, 0.5]
UpperCAmelCase__			:   str      			=    [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
UpperCAmelCase__			:   Dict      			=    [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
