



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


def   lowercase_	(   lowerCAmelCase__	:  Optional[int]			,							lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  List[str]			,							lowerCAmelCase__	:  str			,							lowerCAmelCase__	:  Dict			,							lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  List[str]=None ):

			"""simple docstring"""





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

						if SDP_ENABLED:
									__UpperCAmelCase   :      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   :      Optional[Any]										=     {"""loss""": loss_value.numpy()}
			return train_results


def   lowercase_	(   lowerCAmelCase__	:  Dict			,							lowerCAmelCase__	:  Optional[int]			,							lowerCAmelCase__	:  List[str] ):

			"""simple docstring"""





			__UpperCAmelCase				,  __UpperCAmelCase   :      str										=     load_dataset("""imdb"""			,							split=["""train""", """test"""] )

			# Preprocess train dataset
			__UpperCAmelCase   :      Union[str, 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   :      Union[str, Any]										=     tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""]) )

			# Preprocess test dataset
			__UpperCAmelCase   :      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   :      str										=     {
			    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   :      Optional[Any]										=     tf_train_dataset.shard(sdp.size()			,							sdp.rank() )
						__UpperCAmelCase   :      Optional[int]										=     tf_test_dataset.shard(sdp.size()			,							sdp.rank() )
			__UpperCAmelCase   :      Optional[int]										=     tf_train_dataset.batch(lowerCAmelCase__			,							drop_remainder=lowerCAmelCase__ )
			__UpperCAmelCase   :      Optional[int]										=     tf_test_dataset.batch(lowerCAmelCase__			,							drop_remainder=lowerCAmelCase__ )

			return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
					_UpperCamelCase											=			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'''])

					_UpperCamelCase				,  _UpperCamelCase											=			parser.parse_known_args()

					# Set up logging
					_UpperCamelCase											=			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()

										_UpperCamelCase											=			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
					_UpperCamelCase											=			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
					_UpperCamelCase											=			AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

										_UpperCamelCase											=			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'''
import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


_UpperCamelCase											=			logging.get_logger(__name__)



class _A  (    __SCREAMING_SNAKE_CASE    ):





		def __init__(     self ,       *__UpperCAmelCase ,       **__UpperCAmelCase				)       ->     None:

					'''simple docstring'''
					warnings.warn(
					    """The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
					    """ Please use MobileNetV2ImageProcessor instead.""" ,       __UpperCAmelCase ,       )
					super().__init__(*__UpperCAmelCase ,       **__UpperCAmelCase				)




