






"""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():
 a  :						Dict					       =							True
 a  :						Tuple					       =							'''p3dn.24xlarge'''
 import smdistributed.dataparallel.tensorflow as sdp
else:
 a  :						Union[str, Any]					       =							False




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=None			)    ->int:
 UpperCAmelCase__ 						=				tqdm(_SCREAMING_SNAKE_CASE			)
 for i, batch in enumerate(_SCREAMING_SNAKE_CASE			):
  with tf.GradientTape() as tape:
   UpperCAmelCase__						,	UpperCAmelCase__ 						=				batch
   UpperCAmelCase__ 						=				model(_SCREAMING_SNAKE_CASE			)
   UpperCAmelCase__ 						=				loss(_SCREAMING_SNAKE_CASE       ,	outputs.logits			)

  if SDP_ENABLED:
   UpperCAmelCase__ 						=				sdp.DistributedGradientTape(_SCREAMING_SNAKE_CASE       ,	sparse_as_dense=_SCREAMING_SNAKE_CASE			)

  UpperCAmelCase__ 						=				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

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




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->Any:
 # Load dataset
 UpperCAmelCase__						,	UpperCAmelCase__ 						=				load_dataset("""imdb"""       ,	split=["""train""", """test"""]			)

 # Preprocess train dataset
 UpperCAmelCase__ 						=				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"""]			)

 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 _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"""]			)

 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(_SCREAMING_SNAKE_CASE       ,	drop_remainder=_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				tf_test_dataset.batch(_SCREAMING_SNAKE_CASE       ,	drop_remainder=_SCREAMING_SNAKE_CASE			)

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
 a  :						str					       =							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'''])

 a       ,				a  :						str					       =							parser.parse_known_args()

 # Set up logging
 a  :						List[str]					       =							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()

  a  :						List[Any]					       =							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
 a  :						str					       =							TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 a  :						Union[str, Any]					       =							AutoTokenizer.from_pretrained(args.model_name_or_path)

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

  a  :						Union[str, Any]					       =							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):
  a  :						Dict					       =							model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  logger.info('''*** Evaluate ***''')

  a  :						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"""



from math import exp




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->float:
 return 8 * x - 2 * exp(-x			)




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE			)    ->float:
 UpperCAmelCase__ 						=				lower_bound
 UpperCAmelCase__ 						=				upper_bound
 for _ in range(0       ,	_SCREAMING_SNAKE_CASE			):
  UpperCAmelCase__						,	UpperCAmelCase__ 						=				xa, xa - (f(_SCREAMING_SNAKE_CASE			) * (xa - xa)) / (f(_SCREAMING_SNAKE_CASE			) - f(_SCREAMING_SNAKE_CASE			))
 return xa


if __name__ == "__main__":
 print(F'''Example: {secant_method(1, 3, 2)}''')



