"""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__   			=      True
 lowercase__   			=      'p3dn.24xlarge'
 import smdistributed.dataparallel.tensorflow as sdp
else:
 lowercase__   			=      False







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=None  )      ->int:
  a__:	List[Any]     				=      tqdm(_SCREAMING_SNAKE_CASE  )
  for i, batch in enumerate(_SCREAMING_SNAKE_CASE  ):
    with tf.GradientTape() as tape:
      a__				,       a__:	Union[str, Any]     				=      batch
      a__:	List[str]     				=      model(_SCREAMING_SNAKE_CASE  )
      a__:	Optional[Any]     				=      loss(_SCREAMING_SNAKE_CASE						,	outputs.logits  )

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

    a__:	List[str]     				=      tape.gradient(_SCREAMING_SNAKE_CASE						,	model.trainable_variables  )
    opt.apply_gradients(zip(_SCREAMING_SNAKE_CASE						,	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

  a__:	Union[str, Any]     				=      {'loss': loss_value.numpy()}
  return train_results







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Optional[int]:
  # Load dataset
  a__				,       a__:	Tuple     				=      load_dataset('imdb'						,	split=['train', 'test']  )

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

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

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

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

  if SDP_ENABLED:
    a__:	int     				=      tf_train_dataset.shard(sdp.size()						,	sdp.rank()  )
    a__:	Optional[int]     				=      tf_test_dataset.shard(sdp.size()						,	sdp.rank()  )
  a__:	Dict     				=      tf_train_dataset.batch(_SCREAMING_SNAKE_CASE						,	drop_remainder=_SCREAMING_SNAKE_CASE  )
  a__:	Tuple     				=      tf_test_dataset.batch(_SCREAMING_SNAKE_CASE						,	drop_remainder=_SCREAMING_SNAKE_CASE  )

  return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
 lowercase__   			=      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__			,   lowercase__   			=      parser.parse_known_args()

 # Set up logging
 lowercase__   			=      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__   			=      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__   			=      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 lowercase__   			=      AutoTokenizer.from_pretrained(args.model_name_or_path)

 # get datasets
 lowercase__			,   lowercase__   			=      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__   			=      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
 lowercase__   			=      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 lowercase__   			=      [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__   			=      time.time()
  lowercase__   			=      fit(
      model,
      loss,
      optimizer,
      tf_train_dataset,
      args.epochs,
      args.per_device_train_batch_size,
      max_steps=args.max_steps,
  )
  lowercase__   			=      time.time() - start_train_time
  logger.info('*** Train ***')
  logger.info(f"train_runtime = {end_train_time}")

  lowercase__   			=      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__   			=      model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  logger.info('*** Evaluate ***')

  lowercase__   			=      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 						_A					(lowerCAmelCase__	:int = 4_00_00_00		) ->	int:




     '''simple docstring'''



     try:
          _a         =				int(lowerCAmelCase__		)
     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.'		)
     _a         =				1_00
     _a         =				(Decimal(5		) ** Decimal(0.5		) + 1) / Decimal(2		)

     _a         =				(math.floor(math.log(n * (phi + 2)					,    lowerCAmelCase__		) - 1		) // 3) * 3 + 2
     _a         =				Decimal(round(phi ** Decimal(index + 1		)		)		) / (phi + 2)
     _a         =				num // 2
     return int(lowerCAmelCase__		)


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

