






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


__A					=	16
__A					=	32







def       lowercase__	(					A_:						Optional[Any]		)	->				str:







       """simple docstring"""






       return int(x / 2**20		)



class 				_A			:
    """simple docstring"""

    def __enter__(				self      :     Union[str, Any]      )      ->							List[str]:
           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      :     List[Any] ,     *__SCREAMING_SNAKE_CASE      :     str      )      ->							Tuple:
           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       lowercase__	(					A_:						Accelerator  ,      A_:						int = 16  ,      A_:						str = "bert-base-cased"  ,      A_:						int = 320  ,      A_:						int = 160  ,      )	->				Dict:







       """simple docstring"""






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

       def tokenize_function(A_:						List[str]		):
              # max_length=None => use the model max length (it's actually the default)
              __UpperCAmelCase     				=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
       __UpperCAmelCase     				=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
       __UpperCAmelCase     				=tokenized_datasets.rename_column("""label"""  ,      """labels"""		)

       def collate_fn(A_:						Union[str, Any]		):
              # 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.
       __UpperCAmelCase     				=DataLoader(
           tokenized_datasets["""train"""]  ,      shuffle=A_  ,      collate_fn=A_  ,      batch_size=A_		)
       __UpperCAmelCase     				=DataLoader(
           tokenized_datasets["""validation"""]  ,      shuffle=A_  ,      collate_fn=A_  ,      batch_size=A_		)

       return train_dataloader, eval_dataloader







def       lowercase__	(					A_:						Optional[Any]  ,      A_:						List[Any]		)	->				Optional[Any]:







       """simple docstring"""






       __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(A_		)
       __UpperCAmelCase				, __UpperCAmelCase     				=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)
       __UpperCAmelCase     				=AutoModelForSequenceClassification.from_pretrained(A_  ,      return_dict=A_		)

       # 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=A_		)

       if accelerator.state.deepspeed_plugin is not None:
              __UpperCAmelCase     				=accelerator.state.deepspeed_plugin.deepspeed_config[
                  """gradient_accumulation_steps"""
              ]
       else:
              __UpperCAmelCase     				=1
       __UpperCAmelCase     				=(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
       ):
              __UpperCAmelCase     				=get_linear_schedule_with_warmup(
                  optimizer=A_  ,      num_warmup_steps=0  ,      num_training_steps=A_  ,      )
       else:
              __UpperCAmelCase     				=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.
       __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=accelerator.prepare(
           A_  ,      A_  ,      A_  ,      A_  ,      A_		)

       # 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(A_  ,      A_		):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(A_		):
                            __UpperCAmelCase     				=model(**A_		)
                            __UpperCAmelCase     				=outputs.loss
                            __UpperCAmelCase     				=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		)		)		)
              __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(A_  ,      A_		)







def       lowercase__	(					)	->				Any:







       """simple docstring"""






       __UpperCAmelCase     				=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."""  ,      )
       __UpperCAmelCase     				=parser.parse_args()
       __UpperCAmelCase     				={"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
       training_function(A_  ,      A_		)


if __name__ == "__main__":
   main()










"""simple docstring"""






import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer






class lowerCAmelCase__      (	tf.keras.layers.Layer		):






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase    , lowercase = None    , lowercase = None    ):
							super().__init__()
							_lowerCamelCase	:     Optional[Any]       =       pad_token_id
							_lowerCamelCase	:     Dict       =       max_length
							_lowerCamelCase	:     Tuple       =       vocab
							_lowerCamelCase	:     List[str]       =       merges
							_lowerCamelCase	:     int       =       BytePairTokenizer(lowercase    , lowercase    , sequence_length=lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     str       =       [' '.join(lowercase    ) for m in tokenizer.bpe_ranks.keys()]
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.get_vocab()
							return cls(lowercase    , lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    , *lowercase    , **lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       GPTaTokenizer.from_pretrained(lowercase    , *lowercase    , **lowercase    )
							return cls.from_tokenizer(lowercase    , *lowercase    , **lowercase    )
			@classmethod
			def    A_       (    cls    , lowercase    ):
							return cls(**lowercase    )
			def    A_       (    self    ):
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}






			def    A_       (    self    , lowercase    , lowercase = None    ):
							_lowerCamelCase	:     int       =       self.tf_tokenizer(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       tf.ones_like(lowercase    )

							if self.pad_token_id is not None:
											# pad the tokens up to max length
											_lowerCamelCase	:     str       =       max_length if max_length is not None else self.max_length

											if max_length is not None:
															_lowerCamelCase,					_lowerCamelCase	:     Union[str, Any]       =       pad_model_inputs(
															    lowercase    , max_seq_length=lowercase    , pad_value=self.pad_token_id    )

							return {"attention_mask": attention_mask, "input_ids": input_ids}