





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      a    (   _lowercase :  Union[str, Any]			,    _lowercase :  Union[str, Any]			,    _lowercase :  Tuple			,    _lowercase :  Union[str, Any]			,    _lowercase :  int			,    _lowercase :  Dict			,    _lowercase :  Tuple=None							)  ->int:
		UpperCamelCase__								=						tqdm(_SCREAMING_SNAKE_CASE							)
		for i, batch in enumerate(_SCREAMING_SNAKE_CASE							):
				with tf.GradientTape() as tape:
						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      a    (   _lowercase :  Union[str, Any]			,    _lowercase :  Tuple			,    _lowercase :  Optional[Any]							)  ->Optional[int]:
		# Load dataset
		UpperCamelCase__								=						load_dataset("""imdb"""			,    split=["""train""", """test"""]							)

		# Preprocess train dataset
		UpperCamelCase__								=						train_dataset.map(
		    lambda _lowercase							: 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 _lowercase							: 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__":
				__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)






"""simple docstring"""
from __future__ import annotations

import sys







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->int:
  if n < 0:
    raise ValueError('Negative arguments are not supported'  )
  return _fib(_SCREAMING_SNAKE_CASE  )[0]







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->tuple[int, int]:
  if n == 0:  # (F(0), F(1))
    return (0, 1)

  # F(2n) = F(n)[2F(n+1) − F(n)]
  # F(2n+1) = F(n+1)^2+F(n)^2
  a__				,       a__:	Optional[int]     				=      _fib(n // 2  )
  a__:	List[str]     				=      a * (b * 2 - a)
  a__:	Dict     				=      a * a + b * b
  return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 lowercase__   			=      int(sys.argv[1])
 print(f"fibonacci({n}) is {fibonacci(n)}")


