




"""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():
						__lowerCamelCase								=						True
						__lowerCamelCase								=						"p3dn.24xlarge"
						import smdistributed.dataparallel.tensorflow as sdp
else:
						__lowerCamelCase								=						False



def 				a (						__snake_case     :				List[str],    __snake_case     :				Optional[Any],    __snake_case     :				Optional[int],    __snake_case     :				str,    __snake_case     :				int,    __snake_case     :				Any,    __snake_case     :				Any=None  ):



				'''simple docstring'''





				UpperCAmelCase_					:Tuple      			=      tqdm(__snake_case  )
				for i, batch in enumerate(__snake_case  ):
								with tf.GradientTape() as tape:
												UpperCAmelCase_			,UpperCAmelCase_					:List[str]      			=      batch
												UpperCAmelCase_					:Optional[int]      			=      model(__snake_case  )
												UpperCAmelCase_					:Union[str, Any]      			=      loss(__snake_case,    outputs.logits  )

								if SDP_ENABLED:
												UpperCAmelCase_					:str      			=      sdp.DistributedGradientTape(__snake_case,    sparse_as_dense=__snake_case  )

								UpperCAmelCase_					:Optional[int]      			=      tape.gradient(__snake_case,    model.trainable_variables  )
								opt.apply_gradients(zip(__snake_case,    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

				UpperCAmelCase_					:str      			=      {'''loss''': loss_value.numpy()}
				return train_results



def 				a (						__snake_case     :				List[Any],    __snake_case     :				Optional[Any],    __snake_case     :				int  ):



				'''simple docstring'''





				UpperCAmelCase_			,UpperCAmelCase_					:Optional[int]      			=      load_dataset('''imdb''',    split=['''train''', '''test''']  )

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

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

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

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

				if SDP_ENABLED:
								UpperCAmelCase_					:str      			=      tf_train_dataset.shard(sdp.size(),    sdp.rank()  )
								UpperCAmelCase_					:Any      			=      tf_test_dataset.shard(sdp.size(),    sdp.rank()  )
				UpperCAmelCase_					:List[Any]      			=      tf_train_dataset.batch(__snake_case,    drop_remainder=__snake_case  )
				UpperCAmelCase_					:Dict      			=      tf_test_dataset.batch(__snake_case,    drop_remainder=__snake_case  )

				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)








"""simple docstring"""

def 				a (						__snake_case     :				int  ):



				'''simple docstring'''





				if not isinstance(__snake_case,    __snake_case  ) or number < 0:
								raise ValueError('''Input must be a non-negative integer'''  )

				UpperCAmelCase_					:Optional[Any]      			=      number & ~(number - 1)
				UpperCAmelCase_					:List[str]      			=      0
				while intermediate:
								intermediate >>= 1
								index += 1
				return index - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)


