


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				:       Optional[int]      =  16
A				:       Any      =  32





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Dict:


						'''simple docstring'''






						return int(x / 2**20			)






class      UpperCamelCase:


					def __enter__(    self						:		Union[str, Any]    )      ->						List[str]:




											'''simple docstring'''

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






					def __exit__(    self						:		Optional[int]  ,     *SCREAMING_SNAKE_CASE						:		str    )      ->						str:




											'''simple docstring'''

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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase = 16   ,						_lowerCAmelCase = "bert-base-cased"   ,						_lowerCAmelCase = 320   ,						_lowerCAmelCase = 160   ,						)    ->						List[str]:


						'''simple docstring'''






						__snake_case				=		AutoTokenizer.from_pretrained(_lowerCAmelCase			)
						__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)
												__snake_case				=		tokenizer(examples["sentence1"]   ,						examples["sentence2"]   ,						truncation=_lowerCAmelCase   ,						max_length=_lowerCAmelCase			)
												return outputs

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

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__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(_lowerCAmelCase   ,						padding="max_length"   ,						max_length=128   ,						return_tensors="pt"			)
												return tokenizer.pad(_lowerCAmelCase   ,						padding="longest"   ,						return_tensors="pt"			)

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

						return train_dataloader, eval_dataloader





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[str]:


						'''simple docstring'''






						__snake_case				=		Accelerator()

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

						set_seed(_lowerCAmelCase			)
						__snake_case		,       __snake_case				=		get_dataloaders(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						args.n_train   ,						args.n_val			)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__snake_case				=		AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase   ,						return_dict=_lowerCAmelCase			)

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

						if accelerator.state.deepspeed_plugin is not None:
												__snake_case				=		accelerator.state.deepspeed_plugin.deepspeed_config[
												    "gradient_accumulation_steps"
												]
						else:
												__snake_case				=		1
						__snake_case				=		(len(_lowerCAmelCase			) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												__snake_case				=		get_linear_schedule_with_warmup(
												    optimizer=_lowerCAmelCase   ,						num_warmup_steps=0   ,						num_training_steps=_lowerCAmelCase   ,						)
						else:
												__snake_case				=		DummyScheduler(_lowerCAmelCase   ,						total_num_steps=_lowerCAmelCase   ,						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.
						__snake_case		,       __snake_case		,       __snake_case		,       __snake_case		,       __snake_case				=		accelerator.prepare(
						    _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)

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

						# Now we train the model
						__snake_case				=		{}
						for epoch in range(_lowerCAmelCase   ,						_lowerCAmelCase			):
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		for step, batch in enumerate(_lowerCAmelCase			):
																								__snake_case				=		model(**_lowerCAmelCase			)
																								__snake_case				=		outputs.loss
																								__snake_case				=		loss / gradient_accumulation_steps
																								accelerator.backward(_lowerCAmelCase			)
																								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			)			)			)
												__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(_lowerCAmelCase   ,						_lowerCAmelCase			)





def 						_lowerCAmelCase							(     )    ->						Dict:


						'''simple docstring'''






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


if __name__ == "__main__":
	main()









# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


A				:       Union[str, Any]      =  logging.get_logger(__name__)

A				:       Dict[Optional[str], Type[Formatter]]      =  {}
A				:       Dict[Optional[str], str]      =  {}
A				:       Dict[Optional[str], Exception]      =  {}





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None   ,						)    ->						str:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						if format_type in _FORMAT_TYPES:
												logger.warning(
												    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''			)
						__snake_case				=		formatter_cls
						for alias in set(aliases + [format_type]			):
												if alias in _FORMAT_TYPES_ALIASES:
																		logger.warning(
																		    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''			)
												__snake_case				=		format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						for alias in set(aliases + [format_type]			):
												__snake_case				=		unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
	A				:       Any      =  ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
	_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
	A				:       Tuple      =  ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
	_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
	A				:       Optional[int]      =  ValueError('JAX needs to be installed to be able to return JAX arrays.')
	_register_unavailable_formatter(_jax_error, 'jax', aliases=[])





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[str]:


						'''simple docstring'''






						if format_type in _FORMAT_TYPES_ALIASES:
												return _FORMAT_TYPES_ALIASES[format_type]
						else:
												return format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						**_lowerCAmelCase			)    ->						Formatter:


						'''simple docstring'''






						__snake_case				=		get_format_type_from_alias(_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES:
												return _FORMAT_TYPES[format_type](**_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
												raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
						else:
												raise ValueError(
												    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got \'{format_type}\''''			)





