


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 	_lowerCamelCase  (		A_  :	int							,  A_  :	Optional[int]							,  A_  :	Union[str, Any]							,  A_  :	Union[str, Any]							,  A_  :	Union[str, Any]							,  A_  :	int							,  A_  :	Union[str, Any]=None      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=tqdm(a__      )
   for i, batch in enumerate(a__      ):
      with tf.GradientTape() as tape:
         UpperCamelCase__						,	UpperCamelCase__ :			List[str] 						=batch
         UpperCamelCase__ :			Union[str, Any] 						=model(a__      )
         UpperCamelCase__ :			Tuple 						=loss(a__							,  outputs.logits      )

      if SDP_ENABLED:
         UpperCamelCase__ :			Dict 						=sdp.DistributedGradientTape(a__							,  sparse_as_dense=a__      )

      UpperCamelCase__ :			Optional[Any] 						=tape.gradient(a__							,  model.trainable_variables      )
      opt.apply_gradients(zip(a__							,  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__ :			int 						={"loss": loss_value.numpy()}
   return train_results
def 	_lowerCamelCase  (		A_  :	Tuple							,  A_  :	Optional[int]							,  A_  :	Optional[Any]      )			->			List[Any]:

   '''simple docstring'''






   UpperCamelCase__						,	UpperCamelCase__ :			List[Any] 						=load_dataset("imdb"							,  split=["train", "test"]      )

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

   UpperCamelCase__ :			List[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__ :			Union[str, Any] 						=test_dataset.map(
       lambda A_      : tokenizer(e["text"]							,  truncation=a__							,  padding="max_length"      )							,  batched=a__      )
   test_dataset.set_format(type="tensorflow"							,  columns=["input_ids", "attention_mask", "label"]      )

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

   if SDP_ENABLED:
      UpperCamelCase__ :			Dict 						=tf_train_dataset.shard(sdp.size()							,  sdp.rank()      )
      UpperCamelCase__ :			str 						=tf_test_dataset.shard(sdp.size()							,  sdp.rank()      )
   UpperCamelCase__ :			Any 						=tf_train_dataset.batch(a__							,  drop_remainder=a__      )
   UpperCamelCase__ :			Dict 						=tf_test_dataset.batch(a__							,  drop_remainder=a__      )

   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'''

def 				UpperCamelCase__      (      a__ = 1_0_0_0   ):


						'''simple docstring'''






						_lowerCAmelCase					    =[]
						_lowerCAmelCase					    =[]
						_lowerCAmelCase					    =3
						_lowerCAmelCase					    =5
						_lowerCAmelCase					    =1
						while True:
												_lowerCAmelCase					    =z * temp
												if result < n:
																		zmulti.append(a__   )
																		temp += 1
												else:
																		_lowerCAmelCase					    =1
																		break
						while True:
												_lowerCAmelCase					    =x * temp
												if result < n:
																		xmulti.append(a__   )
																		temp += 1
												else:
																		break
						_lowerCAmelCase					    =list(set(xmulti + zmulti   )   )
						return sum(a__   )


if __name__ == "__main__":
							print(F'{solution() = }')





