

"""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__			:					int							  =					True
						lowerCamelCase__			:					int							  =					'''p3dn.24xlarge'''
						import smdistributed.dataparallel.tensorflow as sdp
else:
						lowerCamelCase__			:					int							  =					False
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  Dict,					_lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  str,					_lowerCAmelCase       :  int,					_lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  Any=None	)			->     Tuple:
	_UpperCAmelCase      :							Tuple       =   tqdm(_lowerCAmelCase	)
	for i, batch in enumerate(_lowerCAmelCase	):
		with tf.GradientTape() as tape:
			_UpperCAmelCase  ,				_UpperCAmelCase      :							Union[str, Any]       =   batch
			_UpperCAmelCase      :							Dict       =   model(_lowerCAmelCase	)
			_UpperCAmelCase      :							Any       =   loss(_lowerCAmelCase,					outputs.logits	)

		if SDP_ENABLED:
			_UpperCAmelCase      :							List[str]       =   sdp.DistributedGradientTape(_lowerCAmelCase,					sparse_as_dense=_lowerCAmelCase	)

		_UpperCAmelCase      :							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

	_UpperCAmelCase      :							str       =   {"""loss""": loss_value.numpy()}
	return train_results
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[Any],					_lowerCAmelCase       :  Union[str, Any],					_lowerCAmelCase       :  Dict	)			->     Union[str, Any]:
	# Load dataset
	_UpperCAmelCase  ,				_UpperCAmelCase      :							int       =   load_dataset("""imdb""",					split=["""train""", """test"""]	)

	# Preprocess train dataset
	_UpperCAmelCase      :							Any       =   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"""]	)

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

	# Preprocess test dataset
	_UpperCAmelCase      :							Union[str, Any]       =   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"""]	)

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

	if SDP_ENABLED:
		_UpperCAmelCase      :							Union[str, Any]       =   tf_train_dataset.shard(sdp.size(),					sdp.rank()	)
		_UpperCAmelCase      :							Dict       =   tf_test_dataset.shard(sdp.size(),					sdp.rank()	)
	_UpperCAmelCase      :							List[str]       =   tf_train_dataset.batch(_lowerCAmelCase,					drop_remainder=_lowerCAmelCase	)
	_UpperCAmelCase      :							Tuple       =   tf_test_dataset.batch(_lowerCAmelCase,					drop_remainder=_lowerCAmelCase	)

	return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
						lowerCamelCase__			:					Any							  =					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__			:					str							  =					parser.parse_known_args()

						# Set up logging
						lowerCamelCase__			:					Tuple							  =					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__			:					List[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__			:					Any							  =					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						lowerCamelCase__			:					int							  =					AutoTokenizer.from_pretrained(args.model_name_or_path)

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

												lowerCamelCase__			:					List[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__			:					Optional[int]							  =					model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
												logger.info('''*** Evaluate ***''')

												lowerCamelCase__			:					Union[str, 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"""






def  UpperCamelCase   (     _lowerCAmelCase       :  float,					_lowerCAmelCase       :  float,					_lowerCAmelCase       :  float	)			->     float:
	if mass < 0:
		raise ValueError("""The mass of the body cannot be negative"""	)
	if radius <= 0:
		raise ValueError("""The radius is always a positive non zero integer"""	)
	return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





