


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__       			=				TypeVar("""T""")
SCREAMING_SNAKE_CASE__       			=				TypeVar("""U""")







class     __lowerCamelCase     (  Generic[T, U]     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       str:






								'''simple docstring'''






								lowercase_			 = key
								lowercase_			 = val
								lowercase_			 = 0
								lowercase_			 = None
								lowercase_			 = None




							def __repr__(  self  )     ->       str:






								'''simple docstring'''






								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     __lowerCamelCase     (  Generic[T, U]     ):


							"""simple docstring"""

							def __init__(  self  )     ->       None:






								'''simple docstring'''






								lowercase_			 = DoubleLinkedListNode(UpperCAmelCase						,       UpperCAmelCase  )
								lowercase_			 = DoubleLinkedListNode(UpperCAmelCase						,       UpperCAmelCase  )
								lowercase_ ,	lowercase_			 = self.rear, self.head




							def __repr__(  self  )     ->       str:






								'''simple docstring'''






								lowercase_			 = ["DoubleLinkedList"]
								lowercase_			 = self.head
								while node.next is not None:
									rep.append(str(UpperCAmelCase  )  )
									lowercase_			 = node.next
								rep.append(str(self.rear  )  )
								return ",\n    ".join(UpperCAmelCase  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       None:






								'''simple docstring'''






								lowercase_			 = self.rear.prev

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

								lowercase_			 = node
								lowercase_			 = previous
								lowercase_			 = node
								lowercase_			 = self.rear
								node.freq += 1
								self._position_node(UpperCAmelCase  )




							def 				A__      (  self						,       UpperCAmelCase  )     ->       None:






								'''simple docstring'''






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

									lowercase_			 = previous_node.prev
									lowercase_			 = node.prev
									lowercase_			 = previous_node
									lowercase_			 = node




							def 				A__      (  self						,       UpperCAmelCase  )     ->       DoubleLinkedListNode[T, U] | None:






								'''simple docstring'''






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

								lowercase_			 = node.next
								lowercase_			 = node.prev
								lowercase_			 = None
								lowercase_			 = None
								return node







class     __lowerCamelCase     (  Generic[T, U]     ):


							"""simple docstring"""

							lowerCAmelCase__          =				{}




							def __init__(  self						,       UpperCAmelCase  )     ->       Optional[Any]:






								'''simple docstring'''






								lowercase_			 = DoubleLinkedList()
								lowercase_			 = capacity
								lowercase_			 = 0
								lowercase_			 = 0
								lowercase_			 = 0
								lowercase_			 = {}




							def __repr__(  self  )     ->       str:






								'''simple docstring'''






								return (
								    F'CacheInfo(hits={self.hits}, misses={self.miss}, '
								    F'capacity={self.capacity}, current_size={self.num_keys})'
								)




							def __contains__(  self						,       UpperCAmelCase  )     ->       bool:






								'''simple docstring'''






								return key in self.cache




							def 				A__      (  self						,       UpperCAmelCase  )     ->       U | None:






								'''simple docstring'''






								if key in self.cache:
									self.hits += 1
									lowercase_			 = self.cache[key]
									lowercase_			 = 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(UpperCAmelCase  )
									return node.val
								self.miss += 1
								return None




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       None:






								'''simple docstring'''






								if key not in self.cache:
									if self.num_keys >= self.capacity:
										# delete first node when over capacity
										lowercase_			 = 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(UpperCAmelCase  ) is not None
										# first_node guaranteed to be in list

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

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




							@classmethod
							def 				A__      (  cls						,       UpperCAmelCase = 128  )     ->       Callable[[Callable[[T], U]], Callable[..., U]]:






								'''simple docstring'''






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

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

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

									setattr(UpperCAmelCase						,       "cache_info"						,       UpperCAmelCase  )  # noqa: B010

									return cache_decorator_wrapper

								return cache_decorator_inner


if __name__ == "__main__":
							import doctest

							doctest.testmod()





import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				argparse.ArgumentParser()

							# Hyperparameters sent by the client are passed as command-line arguments to the script.
							parser.add_argument("""--epochs""", type=int, default=1)
							parser.add_argument("""--per_device_train_batch_size""", type=int, default=1_6)
							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)

							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							SCREAMING_SNAKE_CASE__       			=				1_6
							SCREAMING_SNAKE_CASE__       			=				1_6

							# Set up logging
							SCREAMING_SNAKE_CASE__       			=				logging.getLogger(__name__)

							logging.basicConfig(
							    level=logging.getLevelName("""INFO"""),
							    handlers=[logging.StreamHandler(sys.stdout)],
							    format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
							)

							# Load model and tokenizer
							SCREAMING_SNAKE_CASE__       			=				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							SCREAMING_SNAKE_CASE__       			=				AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				load_dataset("""imdb""", split=["""train""", """test"""])
							SCREAMING_SNAKE_CASE__       			=				train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
							SCREAMING_SNAKE_CASE__       			=				test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

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

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

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

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

							# fine optimizer and loss
							SCREAMING_SNAKE_CASE__       			=				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
							SCREAMING_SNAKE_CASE__       			=				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
							SCREAMING_SNAKE_CASE__       			=				[tf.keras.metrics.SparseCategoricalAccuracy()]
							model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

							SCREAMING_SNAKE_CASE__       			=				time.time()
							SCREAMING_SNAKE_CASE__       			=				model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							SCREAMING_SNAKE_CASE__       			=				time.time() - start_train_time

							logger.info("""*** Train ***""")
							logger.info(f"""train_runtime = {end_train_time}""")
							for key, value in train_results.history.items():
														logger.info(f"""  {key} = {value}""")

