
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_  :       Dict					      =     True
							lowerCamelCase_  :       Optional[int]					      =     """p3dn.24xlarge"""
							import smdistributed.dataparallel.tensorflow as sdp
else:
							lowerCamelCase_  :       List[str]					      =     False







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase=None  )     ->		str:
				UpperCamelCase_:							List[str]		=  tqdm(lowerCamelCase  )
				for i, batch in enumerate(lowerCamelCase  ):
								with tf.GradientTape() as tape:
												UpperCamelCase_,      UpperCamelCase_:							List[str]		=  batch
												UpperCamelCase_:							int		=  model(lowerCamelCase  )
												UpperCamelCase_:							Any		=  loss(lowerCamelCase   ,			outputs.logits  )

								if SDP_ENABLED:
												UpperCamelCase_:							Union[str, Any]		=  sdp.DistributedGradientTape(lowerCamelCase   ,			sparse_as_dense=lowerCamelCase  )

								UpperCamelCase_:							Any		=  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

				UpperCamelCase_:							int		=  {"""loss""": loss_value.numpy()}
				return train_results







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		int:
				# Load dataset
				UpperCamelCase_,      UpperCamelCase_:							Dict		=  load_dataset("""imdb"""   ,			split=["""train""", """test"""]  )

				# Preprocess train dataset
				UpperCamelCase_:							Tuple		=  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"""]  )

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

				# Preprocess test dataset
				UpperCamelCase_:							Dict		=  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"""]  )

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

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

				return tf_train_dataset, tf_test_dataset


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

							lowerCamelCase_      ,				lowerCamelCase_  :       List[Any]					      =     parser.parse_known_args()

							# Set up logging
							lowerCamelCase_  :       Tuple					      =     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_  :       Dict					      =     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_  :       List[Any]					      =     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							lowerCamelCase_  :       Dict					      =     AutoTokenizer.from_pretrained(args.model_name_or_path)

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

														lowerCamelCase_  :       int					      =     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_  :       Tuple					      =     model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
														logger.info("""*** Evaluate ***""")

														lowerCamelCase_  :       Union[str, Any]					      =     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()