


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


UpperCAmelCase__   	=							16
UpperCAmelCase__   	=							32
def 				UpperCAmelCase_					(							__snake_case						)     ->			List[str]:







 """simple docstring"""


 return int(x / 2**20						)







class       lowerCamelCase__   :





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





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



def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case = 16     ,						__snake_case = "bert-base-cased"     ,						__snake_case = 320     ,						__snake_case = 160     ,						)     ->			Any:







 """simple docstring"""


 _lowercase							      =AutoTokenizer.from_pretrained(_A						)
 _lowercase							      =load_dataset(
     '''glue'''     ,						'''mrpc'''     ,						split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}						)

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

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

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

 def collate_fn(__snake_case						):
  # 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(_A     ,						padding='''max_length'''     ,						max_length=128     ,						return_tensors='''pt'''						)
  return tokenizer.pad(_A     ,						padding='''longest'''     ,						return_tensors='''pt'''						)

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

 return train_dataloader, eval_dataloader
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Any:







 """simple docstring"""


 _lowercase							      =Accelerator()

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

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

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

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

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

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

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

 # Now we train the model
 _lowercase							      ={}
 for epoch in range(_A     ,						_A						):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(_A						):
    _lowercase							      =model(**_A						)
    _lowercase							      =outputs.loss
    _lowercase							      =loss / gradient_accumulation_steps
    accelerator.backward(_A						)
    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						)						)						)
  _lowercase							      =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(_A     ,						_A						)
def 				UpperCAmelCase_					(							)     ->			List[Any]:







 """simple docstring"""


 _lowercase							      =argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''						)
 parser.add_argument(
     '''--model_name_or_path'''     ,						type=_A     ,						default='''bert-base-cased'''     ,						help='''Path to pretrained model or model identifier from huggingface.co/models.'''     ,						required=_A     ,						)
 parser.add_argument(
     '''--output_dir'''     ,						type=_A     ,						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=_A     ,						default=_A     ,						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=_A     ,						default=320     ,						help='''Number of training examples to use.'''     ,						)
 parser.add_argument(
     '''--n_val'''     ,						type=_A     ,						default=160     ,						help='''Number of validation examples to use.'''     ,						)
 parser.add_argument(
     '''--num_epochs'''     ,						type=_A     ,						default=1     ,						help='''Number of train epochs.'''     ,						)
 _lowercase							      =parser.parse_args()
 _lowercase							      ={"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
 training_function(_A     ,						_A						)


if __name__ == "__main__":
  main()





from __future__ import annotations

A      :    Union[str, Any]		=					{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Tuple					,      __lowerCamelCase							:			dict[str, list[str]]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  graph
             # mapping node to its parent in resulting breadth first tree
             lowerCamelCase__		:				dict[str, str | None]    =  {}
             lowerCamelCase__		:				Dict    =  source_vertex





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {self.source_vertex}
             lowerCamelCase__		:				Optional[int]    =  None
             lowerCamelCase__		:				Dict    =  [self.source_vertex]  # first in first out queue

             while queue:
                   lowerCamelCase__		:				Optional[int]    =  queue.pop(0       )
                   for adjacent_vertex in self.graph[vertex]:
                         if adjacent_vertex not in visited:
                               visited.add(__lowerCamelCase       )
                               lowerCamelCase__		:				List[str]    =  vertex
                               queue.append(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             if target_vertex == self.source_vertex:
                   return self.source_vertex

             lowerCamelCase__		:				Tuple    =  self.parent.get(__lowerCamelCase       )
             if target_vertex_parent is None:
                   lowerCamelCase__		:				Tuple    =  (
                       f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
                   )
                   raise ValueError(__lowerCamelCase       )

             return self.shortest_path(__lowerCamelCase       ) + f"->{target_vertex}"


if __name__ == "__main__":
    A      :    List[str]		=					Graph(graph, "G")
    g.breath_first_search()
    print(g.shortest_path("D"))
    print(g.shortest_path("G"))
    print(g.shortest_path("Foo"))


