





'''simple docstring'''





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        =				1_6
_UpperCAmelCase        =				3_2




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[Any]:
       return int(x / 2**20  )



class 							_UpperCamelCase					:

       def __enter__(		self: Union[str, Any]		)       -> List[Any]:




              """simple docstring"""


              gc.collect()
              torch.cuda.empty_cache()
              torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
              UpperCamelCase_          =  torch.cuda.memory_allocated()
              return self



       def __exit__(		self: int					,   *_SCREAMING_SNAKE_CASE: int		)       -> str:




              """simple docstring"""


              gc.collect()
              torch.cuda.empty_cache()
              UpperCamelCase_          =  torch.cuda.memory_allocated()
              UpperCamelCase_          =  torch.cuda.max_memory_allocated()
              UpperCamelCase_          =  bamb(self.end - self.begin		)
              UpperCamelCase_          =  bamb(self.peak - self.begin		)
              # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




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

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

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

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

       def collate_fn(UpperCamelCase_  ):
              # 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(UpperCamelCase_					,		padding="max_length"					,		max_length=128					,		return_tensors="pt"  )
              return tokenizer.pad(UpperCamelCase_					,		padding="longest"					,		return_tensors="pt"  )

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

       return train_dataloader, eval_dataloader




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Optional[Any]:
       # Initialize accelerator
       UpperCamelCase_          =  Accelerator()

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

       set_seed(UpperCamelCase_  )
       UpperCamelCase_       , UpperCamelCase_          =  get_dataloaders(UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		args.n_train					,		args.n_val  )

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

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

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

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

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

       # Now we train the model
       UpperCamelCase_          =  {}
       for epoch in range(UpperCamelCase_					,		UpperCamelCase_  ):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(UpperCamelCase_  ):
                            UpperCamelCase_          =  model(**UpperCamelCase_  )
                            UpperCamelCase_          =  outputs.loss
                            UpperCamelCase_          =  loss / gradient_accumulation_steps
                            accelerator.backward(UpperCamelCase_  )
                            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  )  )  )
              UpperCamelCase_          =  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(UpperCamelCase_					,		UpperCamelCase_  )




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


if __name__ == "__main__":
     main()








_UpperCAmelCase        =				{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


_UpperCAmelCase        =				{value: key for key, value in encode_dict.items()}




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       UpperCamelCase_          =  ""
       for letter in word.lower():
              if letter.isalpha() or letter == " ":
                     encoded += encode_dict[letter]
              else:
                     raise Exception("encode() accepts only letters of the alphabet and spaces"  )
       return encoded




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       if set(UpperCamelCase_  ) - {"A", "B", " "} != set():
              raise Exception("decode() accepts only 'A', 'B' and spaces"  )
       UpperCamelCase_          =  ""
       for word in coded.split():
              while len(UpperCamelCase_  ) != 0:
                     decoded += decode_dict[word[:5]]
                     UpperCamelCase_          =  word[5:]
              decoded += " "
       return decoded.strip()


if __name__ == "__main__":
     from doctest import testmod

     testmod()

