






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






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase=None						):







       '''simple docstring'''
       UpperCAmelCase					=	tqdm(lowerCAmelCase						)
       for i, batch in enumerate(lowerCAmelCase						):
              with tf.GradientTape() as tape:
                     UpperCAmelCase			,				UpperCAmelCase					=	batch
                     UpperCAmelCase					=	model(lowerCAmelCase						)
                     UpperCAmelCase					=	loss(lowerCAmelCase		,					outputs.logits						)

              if SDP_ENABLED:
                     UpperCAmelCase					=	sdp.DistributedGradientTape(lowerCAmelCase		,					sparse_as_dense=lowerCAmelCase						)

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






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       # Load dataset
       UpperCAmelCase			,				UpperCAmelCase					=	load_dataset("""imdb"""		,					split=["""train""", """test"""]						)

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

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

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

       return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
 lowerCAmelCase_		:			Dict				   =					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=1_6)
 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_		:			Optional[int]				   =					parser.parse_known_args()

 # Set up logging
 lowerCAmelCase_		:			List[Any]				   =					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_		:			Dict				   =					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_		:			Any				   =					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 lowerCAmelCase_		:			Optional[Any]				   =					AutoTokenizer.from_pretrained(args.model_name_or_path)

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

  lowerCAmelCase_		:			Tuple				   =					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_		:			Any				   =					model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  logger.info('''*** Evaluate ***''')

  lowerCAmelCase_		:			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"""







import math
from decimal import Decimal, getcontext





def 						lowercase__(A = 4_000_000						)				->int:




						"""simple docstring"""



						try:
												lowercase__				:      int=       int(A						)
						except (TypeError, ValueError):
												raise TypeError("Parameter n must be int or castable to int."						)
						if n <= 0:
												raise ValueError("Parameter n must be greater than or equal to one."						)
						lowercase__				:      Dict=       100
						lowercase__				:      Optional[Any]=       (Decimal(5						) ** Decimal(0.5						) + 1) / Decimal(2						)

						lowercase__				:      str=       (math.floor(math.log(n * (phi + 2)					,		A						) - 1						) // 3) * 3 + 2
						lowercase__				:      Optional[Any]=       Decimal(round(phi ** Decimal(index + 1						)						)						) / (phi + 2)
						lowercase__				:      Tuple=       num // 2
						return int(A						)


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






