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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase=None    )							-> Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__ =tqdm(__lowerCAmelCase    )
						for i, batch in enumerate(__lowerCAmelCase    ):
												with tf.GradientTape() as tape:
																		lowerCamelCase__ ,				lowerCamelCase__ =batch
																		lowerCamelCase__ =model(__lowerCAmelCase    )
																		lowerCamelCase__ =loss(__lowerCAmelCase       ,   outputs.logits    )

												if SDP_ENABLED:
																		lowerCamelCase__ =sdp.DistributedGradientTape(__lowerCAmelCase       ,   sparse_as_dense=__lowerCAmelCase    )

												lowerCamelCase__ =tape.gradient(__lowerCAmelCase       ,   model.trainable_variables    )
												opt.apply_gradients(zip(__lowerCAmelCase       ,   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__ ={"loss": loss_value.numpy()}
						return train_results





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[Any]:




						'''simple docstring'''



						lowerCamelCase__ ,				lowerCamelCase__ =load_dataset("imdb"       ,   split=["train", "test"]    )

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

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

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

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

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

						return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
	a							=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							=parser.parse_known_args()

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

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

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

		a							=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       lowerCamelCase_			( __lowerCAmelCase    )							-> bool:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												raise ValueError("Input series is not valid, valid series - [2, 4, 8]"    )
						if len(__lowerCAmelCase    ) == 0:
												raise ValueError("Input list must be a non empty list"    )
						if len(__lowerCAmelCase    ) == 1:
												return True
						try:
												lowerCamelCase__ =series[1] / series[0]
												for index in range(len(__lowerCAmelCase    ) - 1    ):
																		if series[index + 1] / series[index] != common_ratio:
																								return False
						except ZeroDivisionError:
												return False
						return True





def       lowerCamelCase_			( __lowerCAmelCase    )							-> float:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ):
												raise ValueError("Input series is not valid, valid series - [2, 4, 8]"    )
						if len(__lowerCAmelCase    ) == 0:
												raise ValueError("Input list must be a non empty list"    )
						lowerCamelCase__ =1
						for value in series:
												answer *= value
						return pow(__lowerCAmelCase       ,   1 / len(__lowerCAmelCase    )    )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


