






"""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					, UpperCAmelCase_					, UpperCAmelCase_)			->   Union[str, Any]:




          '''simple docstring'''




          lowercase__:							Any         =     key
          lowercase__:							List[Any]         =     val
          lowercase__:							int         =     0
          lowercase__:							DoubleLinkedListNode[T, U] | None         =     None
          lowercase__:							DoubleLinkedListNode[T, U] | None         =     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 						_a  (		Generic[T, U]       ):





   '''simple docstring'''






   def __init__(       self)			->   None:




          '''simple docstring'''




          lowercase__:							DoubleLinkedListNode[T, U]         =     DoubleLinkedListNode(UpperCAmelCase_					, UpperCAmelCase_)
          lowercase__:							DoubleLinkedListNode[T, U]         =     DoubleLinkedListNode(UpperCAmelCase_					, UpperCAmelCase_)
          lowercase__    ,      lowercase__:							Union[str, Any]         =     self.rear, self.head





   def __repr__(       self)			->   str:




          '''simple docstring'''




          lowercase__:							Tuple         =     ["DoubleLinkedList"]
          lowercase__:							Union[str, Any]         =     self.head
          while node.next is not None:
                 rep.append(str(UpperCAmelCase_))
                 lowercase__:							List[str]         =     node.next
          rep.append(str(self.rear))
          return ",\n    ".join(UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_)			->   None:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     self.rear.prev

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

          lowercase__:							List[str]         =     node
          lowercase__:							Union[str, Any]         =     previous
          lowercase__:							Tuple         =     node
          lowercase__:							Any         =     self.rear
          node.freq += 1
          self._position_node(UpperCAmelCase_)





   def __lowercase							(       self					, UpperCAmelCase_)			->   None:




          '''simple docstring'''




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

                 lowercase__:							List[str]         =     previous_node.prev
                 lowercase__:							Union[str, Any]         =     node.prev
                 lowercase__:							Dict         =     previous_node
                 lowercase__:							Any         =     node





   def __lowercase							(       self					, UpperCAmelCase_)			->   DoubleLinkedListNode[T, U] | None:




          '''simple docstring'''




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

          lowercase__:							int         =     node.next
          lowercase__:							List[str]         =     node.prev
          lowercase__:							List[Any]         =     None
          lowercase__:							Union[str, Any]         =     None
          return node







class 						_a  (		Generic[T, U]       ):





   '''simple docstring'''






   UpperCamelCase__     	=							{}





   def __init__(       self					, UpperCAmelCase_)			->   List[str]:




          '''simple docstring'''




          lowercase__:							DoubleLinkedList[T, U]         =     DoubleLinkedList()
          lowercase__:							List[Any]         =     capacity
          lowercase__:							int         =     0
          lowercase__:							Any         =     0
          lowercase__:							int         =     0
          lowercase__:							dict[T, DoubleLinkedListNode[T, U]]         =     {}





   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 __lowercase							(       self					, UpperCAmelCase_)			->   U | None:




          '''simple docstring'''




          if key in self.cache:
                 self.hits += 1
                 lowercase__:							DoubleLinkedListNode[T, U]         =     self.cache[key]
                 lowercase__:							Optional[int]         =     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 __lowercase							(       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__:							Any         =     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__:							str         =     DoubleLinkedListNode(UpperCAmelCase_					, UpperCAmelCase_)
                 self.list.add(self.cache[key])
                 self.num_keys += 1

          else:
                 lowercase__:							List[Any]         =     self.list.remove(self.cache[key])
                 assert node is not None  # node guaranteed to be in list
                 lowercase__:							Union[str, Any]         =     value
                 self.list.add(UpperCAmelCase_)





   @classmethod
   def __lowercase							(       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__:							Any         =     LFUCache(UpperCAmelCase_)

                        lowercase__:							str         =     cls.decorator_function_to_instance_map[func].get(args[0])
                        if result is None:
                               lowercase__:							Union[str, Any]         =     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()







'''simple docstring'''




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__":
  __snake_case  : Any		       =    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=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)

  __snake_case       ,       __snake_case  : Dict		       =    parser.parse_known_args()

  # overwrite batch size until we have tf_glue.py
  __snake_case  : Any		       =    16
  __snake_case  : Optional[Any]		       =    16

  # Set up logging
  __snake_case  : Union[str, Any]		       =    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
  __snake_case  : Optional[Any]		       =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  __snake_case  : Optional[Any]		       =    AutoTokenizer.from_pretrained(args.model_name_or_path)

  # Load dataset
  __snake_case       ,       __snake_case  : Optional[int]		       =    load_dataset('imdb', split=['train', 'test'])
  __snake_case  : List[str]		       =    train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  __snake_case  : List[str]		       =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

  # Preprocess train dataset
  __snake_case  : int		       =    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'])

  __snake_case  : Dict		       =    {
      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'])).batch(
      args.per_device_train_batch_size
  )

  # Preprocess test dataset
  __snake_case  : Tuple		       =    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'])

  __snake_case  : Optional[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  : List[str]		       =    tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
      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  : Union[str, Any]		       =    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  __snake_case  : int		       =    [tf.keras.metrics.SparseCategoricalAccuracy()]
  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

  __snake_case  : Any		       =    time.time()
  __snake_case  : int		       =    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  __snake_case  : Union[str, Any]		       =    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}""")






