





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():
							__lowerCAmelCase			:str	 =     True
							__lowerCAmelCase			:Optional[Any]	 =     'p3dn.24xlarge'
							import smdistributed.dataparallel.tensorflow as sdp
else:
							__lowerCAmelCase			:Union[str, Any]	 =     False







def  A					(				UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase=None					):
					_snake_case							:       Optional[int]												= tqdm(UpperCAmelCase					)
					for i, batch in enumerate(UpperCAmelCase					):
										with tf.GradientTape() as tape:
															_snake_case							,			_snake_case							:       List[Any]												= batch
															_snake_case							:       int												= model(UpperCAmelCase					)
															_snake_case							:       int												= loss(UpperCAmelCase	,	outputs.logits					)

										if SDP_ENABLED:
															_snake_case							:       Dict												= sdp.DistributedGradientTape(UpperCAmelCase	,	sparse_as_dense=UpperCAmelCase					)

										_snake_case							:       Optional[int]												= 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

					_snake_case							:       Tuple												= {"loss": loss_value.numpy()}
					return train_results







def  A					(				UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase					):
					# Load dataset
					_snake_case							,			_snake_case							:       Any												= load_dataset("imdb"	,	split=["train", "test"]					)

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

					_snake_case							:       Optional[int]												= {
					    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[str]												= tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])					)

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

					_snake_case							:       Optional[int]												= {
					    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[Any]												= tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])					)

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

					return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
							__lowerCAmelCase			:Dict	 =     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'])

							__lowerCAmelCase , __lowerCAmelCase			:List[Any]	 =     parser.parse_known_args()

							# Set up logging
							__lowerCAmelCase			:Optional[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()

														__lowerCAmelCase			:str	 =     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
							__lowerCAmelCase			:str	 =     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							__lowerCAmelCase			:Union[str, Any]	 =     AutoTokenizer.from_pretrained(args.model_name_or_path)

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

														__lowerCAmelCase			:Optional[Any]	 =     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):
														__lowerCAmelCase			:Union[str, Any]	 =     model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
														logger.info('*** Evaluate ***')

														__lowerCAmelCase			: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)






def  A					(				UpperCAmelCase					):
					if not isinstance(UpperCAmelCase	,	UpperCAmelCase					):
										_snake_case							:       Optional[Any]												= F"""Input value of [number={number}] must be an integer"""
										raise TypeError(UpperCAmelCase					)
					if number < 1:
										raise ValueError("Input must be a positive integer"					)
					return number * (2 * number - 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()