'''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


__a										=					16
__a										=					32






def    __UpperCAmelCase			(  a_:       List[str]							):
   return int(x / 2**20							)





class     A__							:





   """simple docstring"""





   def __enter__( self     :		int			)       ->    Any:







      """simple docstring"""




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




   def __exit__( self     :		str						,     *lowerCAmelCase__     :		Dict			)       ->    str:







      """simple docstring"""




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






def    __UpperCAmelCase			(  a_:       Union[str, Any],					a_:       Optional[int] = 16,					a_:       Any = "bert-base-cased",					a_:       List[Any] = 320,					a_:       Union[str, Any] = 160,					):
   _UpperCAmelCase							:	Dict							=   AutoTokenizer.from_pretrained(a_							)
   _UpperCAmelCase							:	Tuple							=   load_dataset(
       "glue",					"mrpc",					split={"train": f"""train[:{n_train}]""", "validation": f"""validation[:{n_val}]"""}							)

   def tokenize_function(a_:       Tuple							):
      # max_length=None => use the model max length (it's actually the default)
      _UpperCAmelCase							:	Dict							=   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							:	Any							=   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							:	List[Any]							=   tokenized_datasets.rename_column("label",					"labels"							)

   def collate_fn(a_:       Dict							):
      # 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							:	List[Any]							=   DataLoader(
       tokenized_datasets["train"],					shuffle=a_,					collate_fn=a_,					batch_size=a_							)
   _UpperCAmelCase							:	Dict							=   DataLoader(
       tokenized_datasets["validation"],					shuffle=a_,					collate_fn=a_,					batch_size=a_							)

   return train_dataloader, eval_dataloader






def    __UpperCAmelCase			(  a_:       Tuple,					a_:       Dict							):
   _UpperCAmelCase							:	int							=   Accelerator()

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

   set_seed(a_							)
   _UpperCAmelCase							:	List[Any]							=   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							:	Optional[int]							=   AutoModelForSequenceClassification.from_pretrained(a_,					return_dict=a_							)

   # Instantiate optimizer
   _UpperCAmelCase							:	Dict							=   (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   _UpperCAmelCase							:	Union[str, Any]							=   optimizer_cls(params=model.parameters(),					lr=a_							)

   if accelerator.state.deepspeed_plugin is not None:
      _UpperCAmelCase							:	Dict							=   accelerator.state.deepspeed_plugin.deepspeed_config[
          '''gradient_accumulation_steps'''
      ]
   else:
      _UpperCAmelCase							:	int							=   1
   _UpperCAmelCase							:	List[Any]							=   (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							:	List[Any]							=   get_linear_schedule_with_warmup(
          optimizer=a_,					num_warmup_steps=0,					num_training_steps=a_,					)
   else:
      _UpperCAmelCase							:	List[Any]							=   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							:	Tuple							=   accelerator.prepare(
       a_,					a_,					a_,					a_,					a_							)

   # We need to keep track of how many total steps we have iterated over
   _UpperCAmelCase							:	Tuple							=   0
   # We also need to keep track of the stating epoch so files are named properly
   _UpperCAmelCase							:	List[str]							=   0

   # Now we train the model
   _UpperCAmelCase							:	List[str]							=   {}
   for epoch in range(a_,					a_							):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(a_							):
            _UpperCAmelCase							:	List[Any]							=   model(**a_							)
            _UpperCAmelCase							:	Union[str, Any]							=   outputs.loss
            _UpperCAmelCase							:	Tuple							=   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							:	Tuple							=   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			(  ):
   _UpperCAmelCase							:	int							=   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							:	Union[str, Any]							=   parser.parse_args()
   _UpperCAmelCase							:	Optional[int]							=   {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
   training_function(a_,					a_							)


if __name__ == "__main__":
     main()
import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		AlbertConfig.from_json_file(SCREAMING_SNAKE_CASE	)
    print(f"""Building PyTorch model from configuration: {config}"""	)
    __UpperCamelCase     :List[str]			  =		AlbertForPreTraining(SCREAMING_SNAKE_CASE	)

    # Load weights from tf checkpoint
    load_tf_weights_in_albert(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""	)
    torch.save(model.state_dict()  ,			SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--albert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained ALBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __lowercase							 =					parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





