






"""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():
						_snake_case       :      str       =					True
						_snake_case       :      Any       =					"p3dn.24xlarge"
						import smdistributed.dataparallel.tensorflow as sdp
else:
						_snake_case       :      Optional[int]       =					False




def 						lowerCamelCase_				(  __A  :		Union[str, Any]			,	__A  :		List[Any]			,	__A  :		Any			,	__A  :		Any			,	__A  :		List[str]			,	__A  :		Union[str, Any]			,	__A  :		Optional[Any]=None			):
					lowerCamelCase__				=tqdm(__A			)
					for i, batch in enumerate(__A			):
										with tf.GradientTape() as tape:
															lowerCamelCase__				,     lowerCamelCase__				=batch
															lowerCamelCase__				=model(__A			)
															lowerCamelCase__				=loss(__A			,	outputs.logits			)

										if SDP_ENABLED:
															lowerCamelCase__				=sdp.DistributedGradientTape(__A			,	sparse_as_dense=__A			)

										lowerCamelCase__				=tape.gradient(__A			,	model.trainable_variables			)
										opt.apply_gradients(zip(__A			,	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

					lowerCamelCase__				={'loss': loss_value.numpy()}
					return train_results




def 						lowerCamelCase_				(  __A  :		Dict			,	__A  :		int			,	__A  :		int			):
					# Load dataset
					lowerCamelCase__				,     lowerCamelCase__				=load_dataset('imdb'			,	split=['train', 'test']			)

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

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

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

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

					if SDP_ENABLED:
										lowerCamelCase__				=tf_train_dataset.shard(sdp.size()			,	sdp.rank()			)
										lowerCamelCase__				=tf_test_dataset.shard(sdp.size()			,	sdp.rank()			)
					lowerCamelCase__				=tf_train_dataset.batch(__A			,	drop_remainder=__A			)
					lowerCamelCase__				=tf_test_dataset.batch(__A			,	drop_remainder=__A			)

					return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
						_snake_case       :      Optional[Any]       =					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"])

						_snake_case       :      Optional[Any]       =					parser.parse_known_args()

						# Set up logging
						_snake_case       :      Dict       =					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()

												_snake_case       :      Optional[int]       =					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
						_snake_case       :      Dict       =					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						_snake_case       :      Optional[Any]       =					AutoTokenizer.from_pretrained(args.model_name_or_path)

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

												_snake_case       :      List[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):
												_snake_case       :      Tuple       =					model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
												logger.info("*** Evaluate ***")

												_snake_case       :      Dict       =					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




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			,	__A  :		int			):
					assert n > 1 and a > 0 and greatest_common_divisor(__A			,	__A			) == 1
					((lowerCamelCase__)				,     (lowerCamelCase__)				,     (lowerCamelCase__))				=extended_gcd(__A			,	__A			)  # Implemented below
					lowerCamelCase__				=(b * s) % n
					return x




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					((lowerCamelCase__)				,     (lowerCamelCase__))				=extended_euclid(__A			,	__A			)  # Implemented below
					if b < 0:
										lowerCamelCase__				=(b % n + n) % n
					return b




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			,	__A  :		int			):
					lowerCamelCase__				=invert_modulo(__A			,	__A			)
					lowerCamelCase__				=(b * s) % n
					return x




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					assert a >= 0 and b >= 0

					if b == 0:
										lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				=a, 1, 0
					else:
										((lowerCamelCase__)				,     (lowerCamelCase__)				,     (lowerCamelCase__))				=extended_gcd(__A			,	a % b			)
										lowerCamelCase__				=q
										lowerCamelCase__				=p - q * (a // b)

					assert a % d == 0 and b % d == 0
					assert d == a * x + b * y

					return (d, x, y)




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					if b == 0:
										return (1, 0)
					((lowerCamelCase__)				,     (lowerCamelCase__))				=extended_euclid(__A			,	a % b			)
					lowerCamelCase__				=a // b
					return (y, x - k * y)




def 						lowerCamelCase_				(  __A  :		int			,	__A  :		int			):
					if a < b:
										lowerCamelCase__				,     lowerCamelCase__				=b, a

					while a % b != 0:
										lowerCamelCase__				,     lowerCamelCase__				=b, a % b

					return b


if __name__ == "__main__":
						from doctest import testmod

						testmod(name="modular_division", verbose=True)
						testmod(name="modular_division2", verbose=True)
						testmod(name="invert_modulo", verbose=True)
						testmod(name="extended_gcd", verbose=True)
						testmod(name="extended_euclid", verbose=True)
						testmod(name="greatest_common_divisor", verbose=True)