





'''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():
	__UpperCamelCase										=  True
	__UpperCamelCase										=  "p3dn.24xlarge"
	import smdistributed.dataparallel.tensorflow as sdp
else:
	__UpperCamelCase										=  False







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase=None   )   ->							List[str]:



			"""simple docstring"""


			__snake_case				:			Union[str, Any]       			=    tqdm(_lowerCamelCase   )
			for i, batch in enumerate(_lowerCamelCase   ):
						with tf.GradientTape() as tape:
									__snake_case			,    __snake_case				:			List[Any]       			=    batch
									__snake_case				:			List[Any]       			=    model(_lowerCamelCase   )
									__snake_case				:			Optional[Any]       			=    loss(_lowerCamelCase						,  outputs.logits   )

						if SDP_ENABLED:
									__snake_case				:			Optional[int]       			=    sdp.DistributedGradientTape(_lowerCamelCase						,  sparse_as_dense=_lowerCamelCase   )

						__snake_case				:			int       			=    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

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







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase   )   ->							str:



			"""simple docstring"""


			__snake_case			,    __snake_case				:			int       			=    load_dataset("""imdb"""						,  split=["""train""", """test"""]   )

			# Preprocess train dataset
			__snake_case				:			Optional[Any]       			=    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"""]   )

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

			# Preprocess test dataset
			__snake_case				:			Tuple       			=    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"""]   )

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

			if SDP_ENABLED:
						__snake_case				:			Tuple       			=    tf_train_dataset.shard(sdp.size()						,  sdp.rank()   )
						__snake_case				:			Union[str, Any]       			=    tf_test_dataset.shard(sdp.size()						,  sdp.rank()   )
			__snake_case				:			Tuple       			=    tf_train_dataset.batch(_lowerCamelCase						,  drop_remainder=_lowerCamelCase   )
			__snake_case				:			Union[str, Any]       			=    tf_test_dataset.batch(_lowerCamelCase						,  drop_remainder=_lowerCamelCase   )

			return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
	__UpperCamelCase										=  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"])

	__UpperCamelCase						,		__UpperCamelCase										=  parser.parse_known_args()

	# Set up logging
	__UpperCamelCase										=  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()

		__UpperCamelCase										=  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
	__UpperCamelCase										=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
	__UpperCamelCase										=  AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

		__UpperCamelCase										=  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

from typing import Any




class    _A   :

		def __init__(      self				:      Optional[int]     ,			__magic_name__				:      Any     ,			__magic_name__				:      Any )				->						None:  # noqa: A002


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    item
					__snake_case				:			List[Any]       			=    next





class    _A   :

		def __init__(      self				:      str )				->						None:


					"""simple docstring"""

					__snake_case				:			Node | None       			=    None
					__snake_case				:			Optional[Any]       			=    0

		def   lowercase__    (      self				:      Optional[Any]     ,			__magic_name__				:      Any )				->						None:


					"""simple docstring"""

					__snake_case				:			int       			=    Node(__magic_name__     ,			self.head )
					self.size += 1

		def   lowercase__    (      self				:      Tuple )				->						Any:


					"""simple docstring"""

					if self.head is None:
								return None
					else:
								__snake_case				:			Union[str, Any]       			=    self.head.item
								__snake_case				:			Union[str, Any]       			=    self.head.next
								self.size -= 1
								return item

		def   lowercase__    (      self				:      str )				->						bool:


					"""simple docstring"""

					return self.head is None

		def __str__(      self				:      str )				->						str:


					"""simple docstring"""

					if self.is_empty():
								return ""
					else:
								__snake_case				:			Dict       			=    self.head
								__snake_case				:			Optional[int]       			=    """"""
								__snake_case				:			list[str]       			=    []
								while iterate:
											item_list.append(str(iterate.item ) )
											__snake_case				:			int       			=    iterate.next

								__snake_case				:			Union[str, Any]       			=    """ --> """.join(__magic_name__ )

								return item_str

		def __len__(      self				:      Optional[int] )				->						int:


					"""simple docstring"""

					return self.size


