

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			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__=None						):
      __lowerCAmelCase :						List[Any] = tqdm(lowercase__						)
      for i, batch in enumerate(lowercase__						):
            with tf.GradientTape() as tape:
                  __lowerCAmelCase,							__lowerCAmelCase :						Tuple = batch
                  __lowerCAmelCase :						Union[str, Any] = model(lowercase__						)
                  __lowerCAmelCase :						Optional[Any] = loss(lowercase__       ,     outputs.logits						)

            if SDP_ENABLED:
                  __lowerCAmelCase :						Union[str, Any] = sdp.DistributedGradientTape(lowercase__       ,     sparse_as_dense=lowercase__						)

            __lowerCAmelCase :						List[str] = tape.gradient(lowercase__       ,     model.trainable_variables						)
            opt.apply_gradients(zip(lowercase__       ,     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 :						Union[str, Any] = {'''loss''': loss_value.numpy()}
      return train_results







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      # Load dataset
      __lowerCAmelCase,							__lowerCAmelCase :						Optional[int] = load_dataset('''imdb'''       ,     split=['''train''', '''test''']						)

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

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

      # Preprocess test dataset
      __lowerCAmelCase :						Union[str, Any] = test_dataset.map(
          lambda lowercase__						: tokenizer(e['''text''']       ,     truncation=lowercase__       ,     padding='''max_length'''						)       ,     batched=lowercase__						)
      test_dataset.set_format(type='''tensorflow'''       ,     columns=['''input_ids''', '''attention_mask''', '''label''']						)

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

      if SDP_ENABLED:
            __lowerCAmelCase :						int = tf_train_dataset.shard(sdp.size()       ,     sdp.rank()						)
            __lowerCAmelCase :						List[str] = tf_test_dataset.shard(sdp.size()       ,     sdp.rank()						)
      __lowerCAmelCase :						List[Any] = tf_train_dataset.batch(lowercase__       ,     drop_remainder=lowercase__						)
      __lowerCAmelCase :						List[Any] = tf_test_dataset.batch(lowercase__       ,     drop_remainder=lowercase__						)

      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)





def 		_lowercase			(					lowercase__						):
      return "".join(chr(ord(lowercase__						) + 3_2						) if '''A''' <= char <= '''Z''' else char for char in word						)


if __name__ == "__main__":
 from doctest import testmod

 testmod()


