"""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      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  return int(x / 2**20		)


class 							a					:


    def __enter__(       self						:	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						:	Any ,					*__lowerCAmelCase						:	Union[str, Any]       ):
      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      __UpperCAmelCase       (      lowercase ,lowercase = 16 ,lowercase = "bert-base-cased" ,lowercase = 3_20 ,lowercase = 1_60 ,):







  """simple docstring"""
  _UpperCAmelCase    			=   AutoTokenizer.from_pretrained(lowercase		)
  _UpperCAmelCase    			=   load_dataset(
      """glue""" ,"""mrpc""" ,split={"""train""": f'''train[:{n_train}]''', """validation""": f'''validation[:{n_val}]'''}		)

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

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

  # 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(lowercase		):
    # 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(lowercase ,padding="""max_length""" ,max_length=1_28 ,return_tensors="""pt"""		)
    return tokenizer.pad(lowercase ,padding="""longest""" ,return_tensors="""pt"""		)

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

  return train_dataloader, eval_dataloader






def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  # 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(lowercase		)
  _UpperCAmelCase			,					_UpperCAmelCase    			=   get_dataloaders(lowercase ,lowercase ,lowercase ,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(lowercase ,return_dict=lowercase		)

  # 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=lowercase		)

  if accelerator.state.deepspeed_plugin is not None:
    _UpperCAmelCase    			=   accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    _UpperCAmelCase    			=   1
  _UpperCAmelCase    			=   (len(lowercase		) * 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=lowercase ,num_warmup_steps=0 ,num_training_steps=lowercase ,)
  else:
    _UpperCAmelCase    			=   DummyScheduler(lowercase ,total_num_steps=lowercase ,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(
      lowercase ,lowercase ,lowercase ,lowercase ,lowercase		)

  # 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(lowercase ,lowercase		):
    with TorchTracemalloc() as tracemalloc:
      model.train()
      for step, batch in enumerate(lowercase		):
        _UpperCAmelCase    			=   model(**lowercase		)
        _UpperCAmelCase    			=   outputs.loss
        _UpperCAmelCase    			=   loss / gradient_accumulation_steps
        accelerator.backward(lowercase		)
        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(lowercase ,lowercase		)






def      __UpperCAmelCase       (      ):







  """simple docstring"""
  _UpperCAmelCase    			=   argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""		)
  parser.add_argument(
      """--model_name_or_path""" ,type=lowercase ,default="""bert-base-cased""" ,help="""Path to pretrained model or model identifier from huggingface.co/models.""" ,required=lowercase ,)
  parser.add_argument(
      """--output_dir""" ,type=lowercase ,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=lowercase ,default=lowercase ,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=lowercase ,default=3_20 ,help="""Number of training examples to use.""" ,)
  parser.add_argument(
      """--n_val""" ,type=lowercase ,default=1_60 ,help="""Number of validation examples to use.""" ,)
  parser.add_argument(
      """--num_epochs""" ,type=lowercase ,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(lowercase ,lowercase		)


if __name__ == "__main__":
    main()

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__			=    {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPTBigCodeForSequenceClassification""",
        """GPTBigCodeForTokenClassification""",
        """GPTBigCodeForCausalLM""",
        """GPTBigCodeModel""",
        """GPTBigCodePreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_bigcode import (
            GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTBigCodeForCausalLM,
            GPTBigCodeForSequenceClassification,
            GPTBigCodeForTokenClassification,
            GPTBigCodeModel,
            GPTBigCodePreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase__			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
