





"""simple docstring"""





from __future__ import annotations

from random import random
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__					=   TypeVar("KT")
SCREAMING_SNAKE_CASE__					=   TypeVar("VT")

class 		lowercase					(					Generic[KT, VT]					):





      def __init__(       self      ,						lowercase = "root"      ,						lowercase = None )		->     Optional[int]:
            lowerCAmelCase  						=       key
            lowerCAmelCase  						=       value
            lowerCAmelCase  						=       []





      def __repr__(       self )		->     str:
            return f'Node({self.key}: {self.value})'





      @property
      def     _snake_case			(       self )		->     int:
            return len(self.forward )







class 		lowercase					(					Generic[KT, VT]					):





      def __init__(       self      ,						lowercase = 0.5      ,						lowercase = 16 )		->     int:
            lowerCAmelCase  						=       Node[KT, VT]()
            lowerCAmelCase  						=       0
            lowerCAmelCase  						=       p
            lowerCAmelCase  						=       max_level





      def __str__(       self )		->     str:
            lowerCAmelCase  						=       list(self )

            if len(lowerCAmelCase_ ) == 0:
                  return f'SkipList(level={self.level})'

            lowerCAmelCase  						=       max((len(str(lowerCAmelCase_ ) ) for item in items)      ,						default=4 )
            lowerCAmelCase  						=       max(lowerCAmelCase_      ,						4 ) + 4

            lowerCAmelCase  						=       self.head
            lowerCAmelCase  						=       []

            lowerCAmelCase  						=       node.forward.copy()
            lines.append(f'[{node.key}]'.ljust(lowerCAmelCase_      ,						"""-""" ) + """* """ * len(lowerCAmelCase_ ) )
            lines.append(""" """ * label_size + """| """ * len(lowerCAmelCase_ ) )

            while len(node.forward ) != 0:
                  lowerCAmelCase  						=       node.forward[0]

                  lines.append(
                      f'[{node.key}]'.ljust(lowerCAmelCase_      ,						"""-""" )
                      + """ """.join(str(n.key ) if n.key == node.key else """|""" for n in forwards ) )
                  lines.append(""" """ * label_size + """| """ * len(lowerCAmelCase_ ) )
                  lowerCAmelCase  						=       node.forward

            lines.append("""None""".ljust(lowerCAmelCase_ ) + """* """ * len(lowerCAmelCase_ ) )
            return f'SkipList(level={self.level})\n' + "\n".join(lowerCAmelCase_ )





      def __iter__(       self )		->     Tuple:
            lowerCAmelCase  						=       self.head

            while len(node.forward ) != 0:
                  yield node.forward[0].key
                  lowerCAmelCase  						=       node.forward[0]





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       1
            while random() < self.p and level < self.max_level:
                  level += 1

            return level





      def     _snake_case			(       self      ,						lowercase )		->     tuple[Node[KT, VT] | None, list[Node[KT, VT]]]:
            lowerCAmelCase  						=       []

            lowerCAmelCase  						=       self.head

            for i in reversed(range(self.level ) ):
                  # i < node.level - When node level is lesser than `i` decrement `i`.
                  # node.forward[i].key < key - Jumping to node with key value higher
                  #                             or equal to searched key would result
                  #                             in skipping searched key.
                  while i < node.level and node.forward[i].key < key:
                        lowerCAmelCase  						=       node.forward[i]
                  # Each leftmost node (relative to searched node) will potentially have to
                  # be updated.
                  update_vector.append(lowerCAmelCase_ )

            update_vector.reverse()  # Note that we were inserting values in reverse order.

            # len(node.forward) != 0 - If current node doesn't contain any further
            #                          references then searched key is not present.
            # node.forward[0].key == key - Next node key should be equal to search key
            #                              if key is present.
            if len(node.forward ) != 0 and node.forward[0].key == key:
                  return node.forward[0], update_vector
            else:
                  return None, update_vector





      def     _snake_case			(       self      ,						lowercase )		->     List[Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=       self._locate_node(lowerCAmelCase_ )

            if node is not None:
                  for i, update_node in enumerate(lowerCAmelCase_ ):
                        # Remove or replace all references to removed node.
                        if update_node.level > i and update_node.forward[i].key == key:
                              if node.level > i:
                                    lowerCAmelCase  						=       node.forward[i]
                              else:
                                    lowerCAmelCase  						=       update_node.forward[:i]





      def     _snake_case			(       self      ,						lowercase      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=       self._locate_node(lowerCAmelCase_ )
            if node is not None:
                  lowerCAmelCase  						=       value
            else:
                  lowerCAmelCase  						=       self.random_level()

                  if level > self.level:
                        # After level increase we have to add additional nodes to head.
                        for _ in range(self.level - 1      ,						lowerCAmelCase_ ):
                              update_vector.append(self.head )
                        lowerCAmelCase  						=       level

                  lowerCAmelCase  						=       Node(lowerCAmelCase_      ,						lowerCAmelCase_ )

                  for i, update_node in enumerate(update_vector[:level] ):
                        # Change references to pass through new node.
                        if update_node.level > i:
                              new_node.forward.append(update_node.forward[i] )

                        if update_node.level < i + 1:
                              update_node.forward.append(lowerCAmelCase_ )
                        else:
                              lowerCAmelCase  						=       new_node





      def     _snake_case			(       self      ,						lowercase )		->     VT | None:
            lowerCAmelCase						,		lowerCAmelCase  						=       self._locate_node(lowerCAmelCase_ )

            if node is not None:
                  return node.value

            return None







def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       SkipList()
      skip_list.insert("""Key1"""							,						3			)
      skip_list.insert("""Key2"""							,						12			)
      skip_list.insert("""Key3"""							,						41			)
      skip_list.insert("""Key4"""							,						-19			)

      lowerCAmelCase  						=       skip_list.head
      lowerCAmelCase  						=       {}
      while node.level != 0:
            lowerCAmelCase  						=       node.forward[0]
            lowerCAmelCase  						=       node.value

      assert len(snake_case__			) == 4
      assert all_values["Key1"] == 3
      assert all_values["Key2"] == 12
      assert all_values["Key3"] == 41
      assert all_values["Key4"] == -19


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       SkipList()
      skip_list.insert("""Key1"""							,						10			)
      skip_list.insert("""Key1"""							,						12			)

      skip_list.insert("""Key5"""							,						7			)
      skip_list.insert("""Key7"""							,						10			)
      skip_list.insert("""Key10"""							,						5			)

      skip_list.insert("""Key7"""							,						7			)
      skip_list.insert("""Key5"""							,						5			)
      skip_list.insert("""Key10"""							,						10			)

      lowerCAmelCase  						=       skip_list.head
      lowerCAmelCase  						=       {}
      while node.level != 0:
            lowerCAmelCase  						=       node.forward[0]
            lowerCAmelCase  						=       node.value

      if len(snake_case__			) != 4:
            print()
      assert len(snake_case__			) == 4
      assert all_values["Key1"] == 12
      assert all_values["Key7"] == 7
      assert all_values["Key5"] == 5
      assert all_values["Key10"] == 10


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       SkipList()
      assert skip_list.find("""Some key"""			) is None


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       SkipList()

      skip_list.insert("""Key2"""							,						20			)
      assert skip_list.find("""Key2"""			) == 20

      skip_list.insert("""Some Key"""							,						10			)
      skip_list.insert("""Key2"""							,						8			)
      skip_list.insert("""V"""							,						13			)

      assert skip_list.find("""Y"""			) is None
      assert skip_list.find("""Key2"""			) == 8
      assert skip_list.find("""Some Key"""			) == 10
      assert skip_list.find("""V"""			) == 13


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       SkipList()
      skip_list.delete("""Some key"""			)

      assert len(skip_list.head.forward			) == 0


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       SkipList()

      skip_list.insert("""Key1"""							,						12			)
      skip_list.insert("""V"""							,						13			)
      skip_list.insert("""X"""							,						14			)
      skip_list.insert("""Key2"""							,						15			)

      skip_list.delete("""V"""			)
      skip_list.delete("""Key2"""			)

      assert skip_list.find("""V"""			) is None
      assert skip_list.find("""Key2"""			) is None


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       SkipList()

      skip_list.insert("""Key1"""							,						12			)
      skip_list.insert("""V"""							,						13			)
      skip_list.insert("""X"""							,						14			)
      skip_list.insert("""Key2"""							,						15			)

      skip_list.delete("""V"""			)
      assert skip_list.find("""V"""			) is None
      assert skip_list.find("""X"""			) == 14
      assert skip_list.find("""Key1"""			) == 12
      assert skip_list.find("""Key2"""			) == 15

      skip_list.delete("""X"""			)
      assert skip_list.find("""V"""			) is None
      assert skip_list.find("""X"""			) is None
      assert skip_list.find("""Key1"""			) == 12
      assert skip_list.find("""Key2"""			) == 15

      skip_list.delete("""Key1"""			)
      assert skip_list.find("""V"""			) is None
      assert skip_list.find("""X"""			) is None
      assert skip_list.find("""Key1"""			) is None
      assert skip_list.find("""Key2"""			) == 15

      skip_list.delete("""Key2"""			)
      assert skip_list.find("""V"""			) is None
      assert skip_list.find("""X"""			) is None
      assert skip_list.find("""Key1"""			) is None
      assert skip_list.find("""Key2"""			) is None


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       SkipList()

      skip_list.insert("""Key1"""							,						12			)
      skip_list.insert("""V"""							,						13			)
      skip_list.insert("""X"""							,						1_42			)
      skip_list.insert("""Key2"""							,						15			)

      skip_list.delete("""X"""			)

      def traverse_keys(SCREAMING_SNAKE_CASE     :						Optional[int]			):
            yield node.key
            for forward_node in node.forward:
                  yield from traverse_keys(snake_case__			)

      assert len(set(traverse_keys(skip_list.head			)			)			) == 4


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      def is_sorted(SCREAMING_SNAKE_CASE     :						str			):
            return all(next_item >= item for item, next_item in zip(snake_case__							,						lst[1:]			)			)

      lowerCAmelCase  						=       SkipList()
      for i in range(10			):
            skip_list.insert(snake_case__							,						snake_case__			)
      assert is_sorted(list(snake_case__			)			)
      skip_list.delete(5			)
      skip_list.delete(8			)
      skip_list.delete(2			)
      assert is_sorted(list(snake_case__			)			)
      skip_list.insert(-12							,						-12			)
      skip_list.insert(77							,						77			)
      assert is_sorted(list(snake_case__			)			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      for _ in range(1_00			):
            # Repeat test 100 times due to the probabilistic nature of skip list
            # random values == random bugs
            test_insert()
            test_insert_overrides_existing_value()

            test_searching_empty_list_returns_none()
            test_search()

            test_deleting_item_from_empty_list_do_nothing()
            test_deleted_items_are_not_founded_by_find_method()
            test_delete_removes_only_given_key()
            test_delete_doesnt_leave_dead_nodes()

            test_iter_always_yields_sorted_values()


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       SkipList()
      skip_list.insert(2							,						"""2"""			)
      skip_list.insert(4							,						"""4"""			)
      skip_list.insert(6							,						"""4"""			)
      skip_list.insert(4							,						"""5"""			)
      skip_list.insert(8							,						"""4"""			)
      skip_list.insert(9							,						"""4"""			)

      skip_list.delete(4			)

      print(snake_case__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()







import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_SCREAMING_SNAKE_CASE										=						16
_SCREAMING_SNAKE_CASE										=						32






def  snake_case  (       snake_case__			:Optional[int])						->   str:
       return int(x / 2**20)



class 				a							:







 """simple docstring"""






 def __enter__(					self							)  ->					List[str]:
        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        _A        =  torch.cuda.memory_allocated()
        return self



 def __exit__(					self		,				*lowerCAmelCase_							)  ->					Optional[int]:
        gc.collect()
        torch.cuda.empty_cache()
        _A        =  torch.cuda.memory_allocated()
        _A        =  torch.cuda.max_memory_allocated()
        _A        =  bamb(self.end - self.begin							)
        _A        =  bamb(self.peak - self.begin							)
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def  snake_case  (       snake_case__			:Accelerator   ,	snake_case__			:int = 16   ,	snake_case__			:str = "bert-base-cased"   ,	snake_case__			:int = 320   ,	snake_case__			:int = 160   ,	)						->   Dict:
       _A        =  AutoTokenizer.from_pretrained(snake_case__)
       _A        =  load_dataset(
           """glue"""   ,	"""mrpc"""   ,	split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''})

       def tokenize_function(snake_case__			:Optional[int]):
              # max_length=None => use the model max length (it's actually the default)
              _A        =  tokenizer(examples["""sentence1"""]   ,	examples["""sentence2"""]   ,	truncation=snake_case__   ,	max_length=snake_case__)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       _A        =  datasets.map(
           snake_case__   ,	batched=snake_case__   ,	remove_columns=["""idx""", """sentence1""", """sentence2"""]   ,	load_from_cache_file=snake_case__)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       _A        =  tokenized_datasets.rename_column("""label"""   ,	"""labels""")

       def collate_fn(snake_case__			:List[str]):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(snake_case__   ,	padding="""max_length"""   ,	max_length=128   ,	return_tensors="""pt""")
              return tokenizer.pad(snake_case__   ,	padding="""longest"""   ,	return_tensors="""pt""")

       # Instantiate dataloaders.
       _A        =  DataLoader(
           tokenized_datasets["""train"""]   ,	shuffle=snake_case__   ,	collate_fn=snake_case__   ,	batch_size=snake_case__)
       _A        =  DataLoader(
           tokenized_datasets["""validation"""]   ,	shuffle=snake_case__   ,	collate_fn=snake_case__   ,	batch_size=snake_case__)

       return train_dataloader, eval_dataloader






def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Optional[int])						->   Optional[int]:
       # Initialize accelerator
       _A        =  Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _A        =  config["""lr"""]
       _A        =  int(config["""num_epochs"""])
       _A        =  int(config["""seed"""])
       _A        =  int(config["""batch_size"""])
       _A        =  args.model_name_or_path

       set_seed(snake_case__)
       _A   ,  _A        =  get_dataloaders(snake_case__   ,	snake_case__   ,	snake_case__   ,	args.n_train   ,	args.n_val)

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _A        =  AutoModelForSequenceClassification.from_pretrained(snake_case__   ,	return_dict=snake_case__)

       # Instantiate optimizer
       _A        =  (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       _A        =  optimizer_cls(params=model.parameters()   ,	lr=snake_case__)

       if accelerator.state.deepspeed_plugin is not None:
              _A        =  accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              _A        =  1
       _A        =  (len(snake_case__) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              _A        =  get_linear_schedule_with_warmup(
                  optimizer=snake_case__   ,	num_warmup_steps=0   ,	num_training_steps=snake_case__   ,	)
       else:
              _A        =  DummyScheduler(snake_case__   ,	total_num_steps=snake_case__   ,	warmup_num_steps=0)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       _A   ,  _A   ,  _A   ,  _A   ,  _A        =  accelerator.prepare(
           snake_case__   ,	snake_case__   ,	snake_case__   ,	snake_case__   ,	snake_case__)

       # We need to keep track of how many total steps we have iterated over
       _A        =  0
       # We also need to keep track of the stating epoch so files are named properly
       _A        =  0

       # Now we train the model
       _A        =  {}
       for epoch in range(snake_case__   ,	snake_case__):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(snake_case__):
                            _A        =  model(**snake_case__)
                            _A        =  outputs.loss
                            _A        =  loss / gradient_accumulation_steps
                            accelerator.backward(snake_case__)
                            if step % gradient_accumulation_steps == 0:
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
              accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin)))
              accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used))
              accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked))
              accelerator.print(
                  """Total Peak Memory consumed during the train (max): {}""".format(
                      tracemalloc.peaked + bamb(tracemalloc.begin)))
              _A        =  tracemalloc.peaked + bamb(tracemalloc.begin)
              if args.peak_memory_upper_bound is not None:
                     assert (
                         train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
                     ), "Peak memory usage exceeded the upper bound"

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir   ,	"""peak_memory_utilization.json""")   ,	"""w""") as f:
                     json.dump(snake_case__   ,	snake_case__)






def  snake_case  (       )						->   Optional[int]:
       _A        =  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""")
       parser.add_argument(
           """--model_name_or_path"""   ,	type=snake_case__   ,	default="""bert-base-cased"""   ,	help="""Path to pretrained model or model identifier from huggingface.co/models."""   ,	required=snake_case__   ,	)
       parser.add_argument(
           """--output_dir"""   ,	type=snake_case__   ,	default="""."""   ,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""   ,	)
       parser.add_argument(
           """--peak_memory_upper_bound"""   ,	type=snake_case__   ,	default=snake_case__   ,	help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""   ,	)
       parser.add_argument(
           """--n_train"""   ,	type=snake_case__   ,	default=320   ,	help="""Number of training examples to use."""   ,	)
       parser.add_argument(
           """--n_val"""   ,	type=snake_case__   ,	default=160   ,	help="""Number of validation examples to use."""   ,	)
       parser.add_argument(
           """--num_epochs"""   ,	type=snake_case__   ,	default=1   ,	help="""Number of train epochs."""   ,	)
       _A        =  parser.parse_args()
       _A        =  {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(snake_case__   ,	snake_case__)


if __name__ == "__main__":
    main()




