






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        _a  (     UpperCamelCase_					:				Tuple   ) ->   int:
						"""simple docstring"""







						return int(x / 2**20   )


class      lowercase__     :

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







							'''simple docstring'''





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


	def __exit__(							self		,    *__UpperCAmelCase       )->					str:







							'''simple docstring'''





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





def        _a  (     UpperCamelCase_					:				Accelerator    ,   UpperCamelCase_					:				int = 16    ,   UpperCamelCase_					:				str = "bert-base-cased"    ,   UpperCamelCase_					:				int = 320    ,   UpperCamelCase_					:				int = 160    ,   ) ->   str:
						"""simple docstring"""







						lowerCAmelCase__       =						AutoTokenizer.from_pretrained(UpperCamelCase_   )
						lowerCAmelCase__       =						load_dataset(
						    "glue"    ,   "mrpc"    ,   split={"train": F"train[:{n_train}]", "validation": F"validation[:{n_val}]"}   )

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

						def collate_fn(UpperCamelCase_					:				int   ):
												# 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=128    ,   return_tensors="pt"   )
												return tokenizer.pad(UpperCamelCase_    ,   padding="longest"    ,   return_tensors="pt"   )

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

						return train_dataloader, eval_dataloader


def        _a  (     UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				Tuple   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						Accelerator()

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

						set_seed(UpperCamelCase_   )
						lowerCAmelCase__      ,							lowerCAmelCase__       =						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)
						lowerCAmelCase__       =						AutoModelForSequenceClassification.from_pretrained(UpperCamelCase_    ,   return_dict=UpperCamelCase_   )

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

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

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

						# Now we train the model
						lowerCAmelCase__       =						{}
						for epoch in range(UpperCamelCase_    ,   UpperCamelCase_   ):
												with TorchTracemalloc() as tracemalloc:
																		model.train()
																		for step, batch in enumerate(UpperCamelCase_   ):
																								lowerCAmelCase__       =						model(**UpperCamelCase_   )
																								lowerCAmelCase__       =						outputs.loss
																								lowerCAmelCase__       =						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   )   )   )
												lowerCAmelCase__       =						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  (     ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						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=320    ,   help="Number of training examples to use."    ,   )
						parser.add_argument(
						    "--n_val"    ,   type=UpperCamelCase_    ,   default=160    ,   help="Number of validation examples to use."    ,   )
						parser.add_argument(
						    "--num_epochs"    ,   type=UpperCamelCase_    ,   default=1    ,   help="Number of train epochs."    ,   )
						lowerCAmelCase__       =						parser.parse_args()
						lowerCAmelCase__       =						{"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
						training_function(UpperCamelCase_    ,   UpperCamelCase_   )


if __name__ == "__main__":
	main()














# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    '''pipelines_utils''',
    '''0.22.0''',
    '''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
    standard_warn=False,
    stacklevel=3,
)






