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


__lowercase							 =					16
__lowercase							 =					32



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







    return int(x / 2**20	)


class 	lowerCamelCase_     :






    '''simple docstring'''






    def __enter__(	self)       ->	Any:
        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        __UpperCamelCase     :Dict			  =		torch.cuda.memory_allocated()
        return self







    def __exit__(	self , *__lowercase)       ->	Optional[int]:
        gc.collect()
        torch.cuda.empty_cache()
        __UpperCamelCase     :Dict			  =		torch.cuda.memory_allocated()
        __UpperCamelCase     :str			  =		torch.cuda.max_memory_allocated()
        __UpperCamelCase     :Dict			  =		bamb(self.end - self.begin)
        __UpperCamelCase     :List[str]			  =		bamb(self.peak - self.begin)
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = 16  ,			SCREAMING_SNAKE_CASE = "bert-base-cased"  ,			SCREAMING_SNAKE_CASE = 320  ,			SCREAMING_SNAKE_CASE = 160  ,			):
    '''simple docstring'''







    __UpperCamelCase     :Dict			  =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :int			  =		load_dataset(
        '''glue'''  ,			'''mrpc'''  ,			split={'''train''': f"""train[:{n_train}]""", '''validation''': f"""validation[:{n_val}]"""}	)

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

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

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

    def collate_fn(SCREAMING_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(SCREAMING_SNAKE_CASE  ,			padding='''max_length'''  ,			max_length=128  ,			return_tensors='''pt'''	)
        return tokenizer.pad(SCREAMING_SNAKE_CASE  ,			padding='''longest'''  ,			return_tensors='''pt'''	)

    # Instantiate dataloaders.
    __UpperCamelCase     :str			  =		DataLoader(
        tokenized_datasets['''train''']  ,			shuffle=SCREAMING_SNAKE_CASE  ,			collate_fn=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Optional[Any]			  =		DataLoader(
        tokenized_datasets['''validation''']  ,			shuffle=SCREAMING_SNAKE_CASE  ,			collate_fn=SCREAMING_SNAKE_CASE  ,			batch_size=SCREAMING_SNAKE_CASE	)

    return train_dataloader, eval_dataloader



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







    __UpperCamelCase     :Optional[Any]			  =		Accelerator()

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

    set_seed(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Optional[Any]			  =		get_dataloaders(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			args.n_train  ,			args.n_val	)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCamelCase     :Union[str, Any]			  =		AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE  ,			return_dict=SCREAMING_SNAKE_CASE	)

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

    if accelerator.state.deepspeed_plugin is not None:
        __UpperCamelCase     :Optional[Any]			  =		accelerator.state.deepspeed_plugin.deepspeed_config[
            '''gradient_accumulation_steps'''
        ]
    else:
        __UpperCamelCase     :Optional[Any]			  =		1
    __UpperCamelCase     :Optional[int]			  =		(len(SCREAMING_SNAKE_CASE	) * 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     :str			  =		get_linear_schedule_with_warmup(
            optimizer=SCREAMING_SNAKE_CASE  ,			num_warmup_steps=0  ,			num_training_steps=SCREAMING_SNAKE_CASE  ,			)
    else:
        __UpperCamelCase     :Union[str, Any]			  =		DummyScheduler(SCREAMING_SNAKE_CASE  ,			total_num_steps=SCREAMING_SNAKE_CASE  ,			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     :List[Any]			  =		accelerator.prepare(
        SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

    # Now we train the model
    __UpperCamelCase     :Union[str, Any]			  =		{}
    for epoch in range(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            for step, batch in enumerate(SCREAMING_SNAKE_CASE	):
                __UpperCamelCase     :List[str]			  =		model(**SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :Any			  =		outputs.loss
                __UpperCamelCase     :Optional[Any]			  =		loss / gradient_accumulation_steps
                accelerator.backward(SCREAMING_SNAKE_CASE	)
                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     :Any			  =		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(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)



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







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


if __name__ == "__main__":
    main()









import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def   _SCREAMING_SNAKE_CASE	( *SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = None ,			SCREAMING_SNAKE_CASE=True ,			SCREAMING_SNAKE_CASE=2       ):
  from .. import __version__

  A_      :				Union[str, Any]		     =					take_from
  A_      :				Optional[Any]		     =					()
  if not isinstance(args[0] ,			SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					(args,)

  for attribute, version_name, message in args:
    if version.parse(version.parse(SCREAMING_SNAKE_CASE       ).base_version       ) >= version.parse(SCREAMING_SNAKE_CASE       ):
      raise ValueError(
          f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
          f''' version {__version__} is >= {version_name}'''       )

    A_      :				List[Any]		     =					None
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and attribute in deprecated_kwargs:
      values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE       ),)
      A_      :				Optional[Any]		     =					f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
    elif hasattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      values += (getattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ),)
      A_      :				int		     =					f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
    elif deprecated_kwargs is None:
      A_      :				List[Any]		     =					f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

    if warning is not None:
      A_      :				Union[str, Any]		     =					warning + ''' ''' if standard_warn else ''''''
      warnings.warn(warning + message ,			SCREAMING_SNAKE_CASE ,			stacklevel=SCREAMING_SNAKE_CASE       )

  if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and len(SCREAMING_SNAKE_CASE       ) > 0:
    A_      :				Dict		     =					inspect.getouterframes(inspect.currentframe()       )[1]
    A_      :				Optional[int]		     =					call_frame.filename
    A_      :				Optional[int]		     =					call_frame.lineno
    A_      :				str		     =					call_frame.function
    A_     ,       A_      :				List[str]		     =					next(iter(deprecated_kwargs.items()       )       )
    raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''       )

  if len(SCREAMING_SNAKE_CASE       ) == 0:
    return
  elif len(SCREAMING_SNAKE_CASE       ) == 1:
    return values[0]
  return values




