'''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		:					int													=		True
						_lowercase		:					Optional[int]													=		"p3dn.24xlarge"
						import smdistributed.dataparallel.tensorflow as sdp
else:
						_lowercase		:					List[Any]													=		False




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int] , UpperCAmelCase__			:   List[str] , UpperCAmelCase__			:   Optional[Any] , UpperCAmelCase__			:   Dict , UpperCAmelCase__			:   str , UpperCAmelCase__			:   Tuple , UpperCAmelCase__			:   Any=None					)     -> Tuple:
							lowercase_   :     int									=			tqdm(__snake_case					)
							for i, batch in enumerate(__snake_case					):
														with tf.GradientTape() as tape:
																					lowercase_		,		lowercase_   :     Union[str, Any]									=			batch
																					lowercase_   :     Optional[Any]									=			model(__snake_case					)
																					lowercase_   :     int									=			loss(__snake_case , outputs.logits					)

														if SDP_ENABLED:
																					lowercase_   :     Dict									=			sdp.DistributedGradientTape(__snake_case , sparse_as_dense=__snake_case					)

														lowercase_   :     List[Any]									=			tape.gradient(__snake_case , model.trainable_variables					)
														opt.apply_gradients(zip(__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

							lowercase_   :     Optional[Any]									=			{"""loss""": loss_value.numpy()}
							return train_results




def 				lowerCamelCase      ( UpperCAmelCase__			:   Union[str, Any] , UpperCAmelCase__			:   int , UpperCAmelCase__			:   Tuple					)     -> Tuple:
							lowercase_		,		lowercase_   :     List[str]									=			load_dataset("""imdb""" , split=["""train""", """test"""]					)

							# Preprocess train dataset
							lowercase_   :     Union[str, Any]									=			train_dataset.map(
							    lambda UpperCAmelCase__					: tokenizer(e["""text"""] , truncation=__snake_case , padding="""max_length"""					) , batched=__snake_case					)
							train_dataset.set_format(type="""tensorflow""" , columns=["""input_ids""", """attention_mask""", """label"""]					)

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

							# Preprocess test dataset
							lowercase_   :     Optional[int]									=			test_dataset.map(
							    lambda UpperCAmelCase__					: tokenizer(e["""text"""] , truncation=__snake_case , padding="""max_length"""					) , batched=__snake_case					)
							test_dataset.set_format(type="""tensorflow""" , columns=["""input_ids""", """attention_mask""", """label"""]					)

							lowercase_   :     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"""]
							}
							lowercase_   :     Dict									=			tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])					)

							if SDP_ENABLED:
														lowercase_   :     List[Any]									=			tf_train_dataset.shard(sdp.size() , sdp.rank()					)
														lowercase_   :     int									=			tf_test_dataset.shard(sdp.size() , sdp.rank()					)
							lowercase_   :     List[Any]									=			tf_train_dataset.batch(__snake_case , drop_remainder=__snake_case					)
							lowercase_   :     List[str]									=			tf_test_dataset.batch(__snake_case , drop_remainder=__snake_case					)

							return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
						_lowercase		:					Tuple													=		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		:					int													=		parser.parse_known_args()

						# Set up logging
						_lowercase		:					Union[str, 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()

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

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

												_lowercase		:					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):
												_lowercase		:					Dict													=		model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
												logger.info("*** Evaluate ***")

												_lowercase		:					int													=		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)









from __future__ import annotations
def 							UpperCAmelCase_   (				__snake_case		)	->     list[int]:







 """simple docstring"""


 if not list_of_ints:
  return []

 if min(__snake_case		) < 0:
  raise ValueError('''All numbers must be positive'''		)

 _lowercase				 =max(len(bin(__snake_case		)[2:]		) for x in list_of_ints		)
 return _msd_radix_sort(__snake_case	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     list[int]:







 """simple docstring"""


 if bit_position == 0 or len(__snake_case		) in [0, 1]:
  return list_of_ints

 _lowercase				 =[]
 _lowercase				 =[]
 # Split numbers based on bit at bit_position from the right
 for number in list_of_ints:
  if (number >> (bit_position - 1)) & 1:
   # number has a one at bit bit_position
   ones.append(__snake_case		)
  else:
   # number has a zero at bit bit_position
   zeros.append(__snake_case		)

    # recursively split both lists further
 _lowercase				 =_msd_radix_sort(__snake_case	,		bit_position - 1		)
 _lowercase				 =_msd_radix_sort(__snake_case	,		bit_position - 1		)

 # recombine lists
 _lowercase				 =zeros
 res.extend(__snake_case		)

 return res
def 							UpperCAmelCase_   (				__snake_case		)	->     List[str]:







 """simple docstring"""


 _lowercase				 =len(__snake_case		)
 if not list_of_ints or length == 1:
  return

 if min(__snake_case		) < 0:
  raise ValueError('''All numbers must be positive'''		)

 _lowercase				 =max(len(bin(__snake_case		)[2:]		) for x in list_of_ints		)
 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		0	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)	->     Any:







 """simple docstring"""


 if bit_position == 0 or end_index - begin_index <= 1:
  return

 bit_position -= 1

 _lowercase				 =begin_index
 _lowercase				 =end_index - 1
 while i <= j:
  _lowercase				 =False
  if not (list_of_ints[i] >> bit_position) & 1:
   # found zero at the beginning
   i += 1
   _lowercase				 =True
  if (list_of_ints[j] >> bit_position) & 1:
   # found one at the end
   j -= 1
   _lowercase				 =True

  if changed:
   continue

  _lowercase ,       _lowercase				 =list_of_ints[j], list_of_ints[i]
  j -= 1
  if j != i:
   i += 1

 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)
 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

