


'''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 					_snake_case   (     A ,    A ,    A ,    A ,    A ,    A ,    A=None       )						->    str:
							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 					_snake_case   (     A ,    A ,    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__":
		__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 collections.abc import Callable
from typing import Generic, TypeVar

__UpperCAmelCase    				=							TypeVar('''T''')
__UpperCAmelCase    				=							TypeVar('''U''')




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	key
														lowerCAmelCase__          =	val
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None







							def __repr__(		self   )       ->							str:
														return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
														    self.key					,    self.val					,    self.freq					,    self.next is not None					,    self.prev is not None   )




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self   )       ->							None:
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.rear, self.head







							def __repr__(		self   )       ->							str:
														lowerCAmelCase__          =	['''DoubleLinkedList''']
														lowerCAmelCase__          =	self.head
														while node.next is not None:
																					rep.append(str(lowerCamelCase_   )   )
																					lowerCAmelCase__          =	node.next
														rep.append(str(self.rear   )   )
														return ",\n    ".join(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:
														lowerCAmelCase__          =	self.rear.prev

														# All nodes other than self.head are guaranteed to have non-None previous
														assert previous is not None

														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	previous
														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	self.rear
														node.freq += 1
														self._position_node(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:

														while node.prev is not None and node.prev.freq > node.freq:
																					# swap node with previous node
																					lowerCAmelCase__          =	node.prev

																					lowerCAmelCase__          =	previous_node.prev
																					lowerCAmelCase__          =	node.prev
																					lowerCAmelCase__          =	previous_node
																					lowerCAmelCase__          =	node







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							DoubleLinkedListNode[T, U] | None:

														if node.prev is None or node.next is None:
																					return None

														lowerCAmelCase__          =	node.next
														lowerCAmelCase__          =	node.prev
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														return node




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							lowercase__			:   dict[Callable[[T], U], LFUCache[T, U]]							=      {}







							def __init__(		self					,    lowerCamelCase_   )       ->							Any:
														lowerCAmelCase__          =	DoubleLinkedList()
														lowerCAmelCase__          =	capacity
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	{}







							def __repr__(		self   )       ->							str:
														return (
														    F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
														    F"""capacity={self.capacity}, current_size={self.num_keys})"""
														)







							def __contains__(		self					,    lowerCamelCase_   )       ->							bool:
														return key in self.cache







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							U | None:

														if key in self.cache:
																					self.hits += 1
																					lowerCAmelCase__          =	self.cache[key]
																					lowerCAmelCase__          =	self.list.remove(self.cache[key]   )
																					assert node == value_node

																					# node is guaranteed not None because it is in self.cache
																					assert node is not None
																					self.list.add(lowerCamelCase_   )
																					return node.val
														self.miss += 1
														return None







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							None:

														if key not in self.cache:
																					if self.num_keys >= self.capacity:
																												# delete first node when over capacity
																												lowerCAmelCase__          =	self.list.head.next

																												# guaranteed to have a non-None first node when num_keys > 0
																												# explain to type checker via assertions
																												assert first_node is not None
																												assert first_node.key is not None
																												assert self.list.remove(lowerCamelCase_   ) is not None
																												# first_node guaranteed to be in list

																												del self.cache[first_node.key]
																												self.num_keys -= 1
																					lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
																					self.list.add(self.cache[key]   )
																					self.num_keys += 1

														else:
																					lowerCAmelCase__          =	self.list.remove(self.cache[key]   )
																					assert node is not None  # node guaranteed to be in list
																					lowerCAmelCase__          =	value
																					self.list.add(lowerCamelCase_   )







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls					,    lowerCamelCase_ = 1_28   )       ->							Callable[[Callable[[T], U]], Callable[..., U]]:

														def cache_decorator_inner(lowerCamelCase_   ) -> Callable[..., U]:
																					def cache_decorator_wrapper(*lowerCamelCase_   ) -> U:
																												if func not in cls.decorator_function_to_instance_map:
																																			lowerCAmelCase__          =	LFUCache(lowerCamelCase_   )

																												lowerCAmelCase__          =	cls.decorator_function_to_instance_map[func].get(args[0]   )
																												if result is None:
																																			lowerCAmelCase__          =	func(*lowerCamelCase_   )
																																			cls.decorator_function_to_instance_map[func].put(args[0]					,    lowerCamelCase_   )
																												return result

																					def cache_info() -> LFUCache[T, U]:
																												return cls.decorator_function_to_instance_map[func]

																					setattr(lowerCamelCase_					,    '''cache_info'''					,    lowerCamelCase_   )  # noqa: B010

																					return cache_decorator_wrapper

														return cache_decorator_inner


if __name__ == "__main__":
		import doctest

		doctest.testmod()