






"""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():
			a :				int      					=		True
			a :				Any      					=		"""p3dn.24xlarge"""
			import smdistributed.dataparallel.tensorflow as sdp
else:
			a :				List[Any]      					=		False





def 						lowercase__(A					,		A					,		A					,		A					,		A					,		A					,		A=None						)				->List[str]:




						"""simple docstring"""



						lowercase__				:      List[str]=       tqdm(A						)
						for i, batch in enumerate(A						):
												with tf.GradientTape() as tape:
																		lowercase__,	lowercase__				:      List[str]=       batch
																		lowercase__				:      List[str]=       model(A						)
																		lowercase__				:      List[Any]=       loss(A					,		outputs.logits						)

												if SDP_ENABLED:
																		lowercase__				:      Any=       sdp.DistributedGradientTape(A					,		sparse_as_dense=A						)

												lowercase__				:      Any=       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

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





def 						lowercase__(A					,		A					,		A						)				->Optional[Any]:




						"""simple docstring"""



						lowercase__,	lowercase__				:      str=       load_dataset("imdb"					,		split=["train", "test"]						)

						# Preprocess train dataset
						lowercase__				:      int=       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"]						)

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

						# Preprocess test dataset
						lowercase__				:      List[str]=       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"]						)

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

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

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
			a :				Tuple      					=		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 :				List[Any]      					=		parser.parse_known_args()

			# Set up logging
			a :				Dict      					=		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 :				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
			a :				List[Any]      					=		TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
			a :				List[Any]      					=		AutoTokenizer.from_pretrained(args.model_name_or_path)

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

						a :				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):
						a :				List[Any]      					=		model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
						logger.info("""*** Evaluate ***""")

						a :				Optional[Any]      					=		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 						lowercase__(A						)				->int:




						"""simple docstring"""



						lowercase__				:      str=       0
						for plain_chr in plain_text:
												lowercase__				:      List[str]=       (
												    ord(A						) + (hash_value << 6) + (hash_value << 16) - hash_value
												)
						return hash_value






