



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






def 	snake_case__  (						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       List[str]=None      ):
			"""simple docstring"""
			lowerCamelCase__						:						Optional[int]						      =tqdm(__lowerCamelCase      )
			for i, batch in enumerate(__lowerCamelCase      ):
						with tf.GradientTape() as tape:
									lowerCamelCase__      ,  lowerCamelCase__						:						List[Any]						      =batch
									lowerCamelCase__						:						Dict						      =model(__lowerCamelCase      )
									lowerCamelCase__						:						Optional[Any]						      =loss(__lowerCamelCase				,						outputs.logits      )

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

						lowerCamelCase__						:						List[Any]						      =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__						:						List[Any]						      ={'''loss''': loss_value.numpy()}
			return train_results






def 	snake_case__  (						__lowerCamelCase							:       str				,						__lowerCamelCase							:       Union[str, Any]				,						__lowerCamelCase							:       List[str]      ):
			"""simple docstring"""
			# Load dataset
			lowerCamelCase__      ,  lowerCamelCase__						:						Any						      =load_dataset('''imdb'''				,						split=['''train''', '''test''']      )

			# Preprocess train dataset
			lowerCamelCase__						:						Optional[int]						      =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__						:						Any						      ={
			    x: train_dataset[x].to_tensor(default_value=0				,						shape=[None, tokenizer.model_max_length]      )
			    for x in ['''input_ids''', '''attention_mask''']
			}
			lowerCamelCase__						:						Any						      =tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])      )

			# Preprocess test dataset
			lowerCamelCase__						:						List[str]						      =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__						:						List[Any]						      ={
			    x: test_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((test_features, test_dataset['''label'''])      )

			if SDP_ENABLED:
						lowerCamelCase__						:						Union[str, Any]						      =tf_train_dataset.shard(sdp.size()				,						sdp.rank()      )
						lowerCamelCase__						:						str						      =tf_test_dataset.shard(sdp.size()				,						sdp.rank()      )
			lowerCamelCase__						:						int						      =tf_train_dataset.batch(__lowerCamelCase				,						drop_remainder=__lowerCamelCase      )
			lowerCamelCase__						:						Dict						      =tf_test_dataset.batch(__lowerCamelCase				,						drop_remainder=__lowerCamelCase      )

			return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
		_lowercase					:					int 		=		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"])

		_lowercase		,      _lowercase					:					Optional[Any] 		=		parser.parse_known_args()

		# Set up logging
		_lowercase					:					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()

				_lowercase					:					Optional[int] 		=		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
		_lowercase					:					Dict 		=		TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
		_lowercase					:					Any 		=		AutoTokenizer.from_pretrained(args.model_name_or_path)

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

				_lowercase					:					Tuple 		=		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):
				_lowercase					:					Tuple 		=		model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
				logger.info("*** Evaluate ***")

				_lowercase					:					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'''

from __future__ import annotations


def        UpperCAmelCase__   (				_lowerCAmelCase    :			list[int | float])    ->		int | float:



						"""simple docstring"""

						if len(_lowerCAmelCase) == 0:
												raise ValueError("find_max() arg is an empty sequence")
						snake_case :       Any				=    nums[0]
						for x in nums:
												if x > max_num:
																		snake_case :       int				=    x
						return max_num


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





