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_	     =     True
					lowerCAmelCase_	     =     '''p3dn.24xlarge'''
					import smdistributed.dataparallel.tensorflow as sdp
else:
					lowerCAmelCase_	     =     False


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=None	):
						snake_case_       =     tqdm(_UpperCamelCase	)
						for i, batch in enumerate(_UpperCamelCase	):
												with tf.GradientTape() as tape:
																		snake_case_       =     batch
																		snake_case_       =     model(_UpperCamelCase	)
																		snake_case_       =     loss(_UpperCamelCase  ,		outputs.logits	)

												if SDP_ENABLED:
																		snake_case_       =     sdp.DistributedGradientTape(_UpperCamelCase  ,		sparse_as_dense=_UpperCamelCase	)

												snake_case_       =     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_       =     {"loss": loss_value.numpy()}
						return train_results


def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
						snake_case_       =     load_dataset('''imdb'''  ,		split=['''train''', '''test''']	)

						# Preprocess train dataset
						snake_case_       =     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''']	)

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

						# Preprocess test dataset
						snake_case_       =     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''']	)

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

						if SDP_ENABLED:
												snake_case_       =     tf_train_dataset.shard(sdp.size()  ,		sdp.rank()	)
												snake_case_       =     tf_test_dataset.shard(sdp.size()  ,		sdp.rank()	)
						snake_case_       =     tf_train_dataset.batch(_UpperCamelCase  ,		drop_remainder=_UpperCamelCase	)
						snake_case_       =     tf_test_dataset.batch(_UpperCamelCase  ,		drop_remainder=_UpperCamelCase	)

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
					lowerCAmelCase_	     =     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_	     =     parser.parse_known_args()

					# Set up logging
					lowerCAmelCase_	     =     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_	     =     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_	     =     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
					lowerCAmelCase_	     =     AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

										lowerCAmelCase_	     =     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   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__	):
						snake_case_       =     5381
						for x in s:
												snake_case_       =     ((hash_value << 5) + hash_value) + ord(SCREAMING_SNAKE_CASE__	)
						return hash_value & 0xFF_FF_FF_FF