
"""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     :   Any							 =							True
					_lowercase     :   List[str]							 =							'p3dn.24xlarge'
					import smdistributed.dataparallel.tensorflow as sdp
else:
					_lowercase     :   Any							 =							False



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__ ,      __magic_name__=None			)	->Any:
			__lowercase						      =					tqdm(__magic_name__			)
			for i, batch in enumerate(__magic_name__			):
						with tf.GradientTape() as tape:
									__lowercase      ,				__lowercase						      =					batch
									__lowercase						      =					model(__magic_name__			)
									__lowercase						      =					loss(__magic_name__ ,      outputs.logits			)

						if SDP_ENABLED:
									__lowercase						      =					sdp.DistributedGradientTape(__magic_name__ ,      sparse_as_dense=__magic_name__			)

						__lowercase						      =					tape.gradient(__magic_name__ ,      model.trainable_variables			)
						opt.apply_gradients(zip(__magic_name__ ,      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

			__lowercase						      =					{"loss": loss_value.numpy()}
			return train_results



def     lowerCAmelCase__ (		__magic_name__ ,      __magic_name__ ,      __magic_name__			)	->Optional[int]:
			__lowercase      ,				__lowercase						      =					load_dataset("imdb" ,      split=["train", "test"]			)

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

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

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

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

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

			return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
					_lowercase     :   Optional[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=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'''])

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

					# Set up logging
					_lowercase     :   List[str]							 =							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[Any]							 =							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     :   Union[str, Any]							 =							TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
					_lowercase     :   Optional[int]							 =							AutoTokenizer.from_pretrained(args.model_name_or_path)

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

										_lowercase     :   List[str]							 =							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     :   Any							 =							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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase							 =							{'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							[
					    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SEWDForCTC''',
					    '''SEWDForSequenceClassification''',
					    '''SEWDModel''',
					    '''SEWDPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

					_lowercase							 =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





