




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


SCREAMING_SNAKE_CASE__ 						=16
SCREAMING_SNAKE_CASE__ 						=32

def 			a   (_lowerCAmelCase     ):
							return int(x / 2**2_0     )






class  __magic_name__						:
				'''simple docstring'''







				def __enter__(							self:   List[Any]		):
											gc.collect()
											torch.cuda.empty_cache()
											torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
											SCREAMING_SNAKE_CASE_   	=    torch.cuda.memory_allocated()
											return self





				def __exit__(							self:   Dict		,					*_lowerCamelCase:   Any		):
											gc.collect()
											torch.cuda.empty_cache()
											SCREAMING_SNAKE_CASE_   	=    torch.cuda.memory_allocated()
											SCREAMING_SNAKE_CASE_   	=    torch.cuda.max_memory_allocated()
											SCREAMING_SNAKE_CASE_   	=    bamb(self.end - self.begin		)
											SCREAMING_SNAKE_CASE_   	=    bamb(self.peak - self.begin		)
											# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase = 1_6  ,    _lowerCAmelCase = "bert-base-cased"  ,    _lowerCAmelCase = 3_2_0  ,    _lowerCAmelCase = 1_6_0  ,    ):
							SCREAMING_SNAKE_CASE_   	=    AutoTokenizer.from_pretrained(__UpperCamelCase     )
							SCREAMING_SNAKE_CASE_   	=    load_dataset(
							    '''glue'''  ,    '''mrpc'''  ,    split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}     )

							def tokenize_function(_lowerCAmelCase     ):
														# max_length=None => use the model max length (it's actually the default)
														SCREAMING_SNAKE_CASE_   	=    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
							SCREAMING_SNAKE_CASE_   	=    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
							SCREAMING_SNAKE_CASE_   	=    tokenized_datasets.rename_column('''label'''  ,    '''labels'''     )

							def collate_fn(_lowerCAmelCase     ):
														# 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=1_2_8  ,    return_tensors='''pt'''     )
														return tokenizer.pad(__UpperCamelCase  ,    padding='''longest'''  ,    return_tensors='''pt'''     )

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE_   	=    DataLoader(
							    tokenized_datasets['''train''']  ,    shuffle=__UpperCamelCase  ,    collate_fn=__UpperCamelCase  ,    batch_size=__UpperCamelCase     )
							SCREAMING_SNAKE_CASE_   	=    DataLoader(
							    tokenized_datasets['''validation''']  ,    shuffle=__UpperCamelCase  ,    collate_fn=__UpperCamelCase  ,    batch_size=__UpperCamelCase     )

							return train_dataloader, eval_dataloader

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							# Initialize accelerator
							SCREAMING_SNAKE_CASE_   	=    Accelerator()

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

							set_seed(__UpperCamelCase     )
							SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_   	=    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)
							SCREAMING_SNAKE_CASE_   	=    AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase  ,    return_dict=__UpperCamelCase     )

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

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

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

							# Now we train the model
							SCREAMING_SNAKE_CASE_   	=    {}
							for epoch in range(__UpperCamelCase  ,    __UpperCamelCase     ):
														with TorchTracemalloc() as tracemalloc:
																					model.train()
																					for step, batch in enumerate(__UpperCamelCase     ):
																												SCREAMING_SNAKE_CASE_   	=    model(**__UpperCamelCase     )
																												SCREAMING_SNAKE_CASE_   	=    outputs.loss
																												SCREAMING_SNAKE_CASE_   	=    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     )     )     )
														SCREAMING_SNAKE_CASE_   	=    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 			a   ():
							SCREAMING_SNAKE_CASE_   	=    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=3_2_0  ,    help='''Number of training examples to use.'''  ,    )
							parser.add_argument(
							    '''--n_val'''  ,    type=__UpperCamelCase  ,    default=1_6_0  ,    help='''Number of validation examples to use.'''  ,    )
							parser.add_argument(
							    '''--num_epochs'''  ,    type=__UpperCamelCase  ,    default=1  ,    help='''Number of train epochs.'''  ,    )
							SCREAMING_SNAKE_CASE_   	=    parser.parse_args()
							SCREAMING_SNAKE_CASE_   	=    {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
							training_function(__UpperCamelCase  ,    __UpperCamelCase     )


if __name__ == "__main__":
			main()








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
			import torch






class  __magic_name__						(  TensorFormatter[Mapping, "torch.Tensor", Mapping]):
				'''simple docstring'''







				def __init__(							self:   int		,					_lowerCamelCase:   int=None		,					**_lowerCamelCase:   List[Any]		):
											super().__init__(features=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch_tensor_kwargs
											import torch  # noqa import torch at initialization


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											import torch

											if isinstance(_lowerCamelCase		,					_lowerCamelCase		) and column:
																		if all(
																		    isinstance(_lowerCamelCase		,					torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
																		    for x in column		):
																									return torch.stack(_lowerCamelCase		)
											return column


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   List[str]		):
											import torch

											if isinstance(_lowerCamelCase		,					(str, bytes, type(_lowerCamelCase		))		):
																		return value
											elif isinstance(_lowerCamelCase		,					(np.character, np.ndarray)		) and np.issubdtype(value.dtype		,					np.character		):
																		return value.tolist()

											SCREAMING_SNAKE_CASE_   	=    {}

											if isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.integer		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.intaa}
											elif isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.floating		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																		import PIL.Image

																		if isinstance(_lowerCamelCase		,					PIL.Image.Image		):
																									SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
											return torch.tensor(_lowerCamelCase		,					**{**default_dtype, **self.torch_tensor_kwargs}		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Dict		):
											import torch

											# support for torch, tf, jax etc.
											if hasattr(_lowerCamelCase		,					'''__array__'''		) and not isinstance(_lowerCamelCase		,					torch.Tensor		):
																		SCREAMING_SNAKE_CASE_   	=    data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(_lowerCamelCase		,					np.ndarray		):
																		if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																									return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											elif isinstance(_lowerCamelCase		,					(list, tuple)		):
																		return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											return self._tensorize(_lowerCamelCase		)


				def     _A							(							self:   str		,					_lowerCamelCase:   dict		):
											return map_nested(self._recursive_tensorize		,					_lowerCamelCase		,					map_list=_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_row(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_row(_lowerCamelCase		)
											return self.recursive_tensorize(_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_column(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_column(_lowerCamelCase		,					pa_table.column_names[0]		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self._consolidate(_lowerCamelCase		)
											return column





				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											for column_name in batch:
																		SCREAMING_SNAKE_CASE_   	=    self._consolidate(batch[column_name]		)
											return batch


