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

              if SDP_ENABLED:
                     snake_case :     List[Any]           =   sdp.DistributedGradientTape(__lowerCamelCase       ,      sparse_as_dense=__lowerCamelCase					)

              snake_case :     str           =   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

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


def     UpperCamelCase      (					__lowerCamelCase  :				Optional[Any]       ,      __lowerCamelCase  :				Any       ,      __lowerCamelCase  :				Union[str, Any]					):
       # Load dataset
       snake_case       ,      snake_case :     int           =   load_dataset("imdb"       ,      split=["train", "test"]					)

       # Preprocess train dataset
       snake_case :     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"]					)

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

       # Preprocess test dataset
       snake_case :     Optional[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"]					)

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

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

       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)







def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				list       ,      __lowerCamelCase  :				int       ,      __lowerCamelCase  :				int					):
       if target == 0:
              answer.append(path.copy()					)
       else:
              for index in range(__lowerCamelCase       ,      len(__lowerCamelCase					)					):
                     if target >= candidates[index]:
                            path.append(candidates[index]					)
                            backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      target - candidates[index]       ,      __lowerCamelCase					)
                            path.pop(len(__lowerCamelCase					) - 1					)


def     UpperCamelCase      (					__lowerCamelCase  :				list       ,      __lowerCamelCase  :				int					):
       snake_case :     Optional[Any]           =   []  # type: list[int]
       snake_case :     Tuple           =   []  # type: list[int]
       backtrack(__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      0					)
       return answer


def     UpperCamelCase      (					):
       print(combination_sum([-8, 2.3, 0]       ,      1					)					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





