




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						:							Any							=  1_6
__A						:							int							=  3_2
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return int(x / 2**20   )




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __enter__(					self       :	Tuple			)			->					str:
										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       :	str       ,     *a       :	Tuple			)			->					int:
										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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE = 16					,	SCREAMING_SNAKE_CASE = "bert-base-cased"					,	SCREAMING_SNAKE_CASE = 3_20					,	SCREAMING_SNAKE_CASE = 1_60					,	):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					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)
						SCREAMING_SNAKE_CASE           =					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
			SCREAMING_SNAKE_CASE           =					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
			SCREAMING_SNAKE_CASE           =					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=1_28					,	return_tensors="""pt"""   )
						return tokenizer.pad(SCREAMING_SNAKE_CASE					,	padding="""longest"""					,	return_tensors="""pt"""   )

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE           =					DataLoader(
			    tokenized_datasets["""train"""]					,	shuffle=SCREAMING_SNAKE_CASE					,	collate_fn=SCREAMING_SNAKE_CASE					,	batch_size=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					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'''
			# 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(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					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)
			SCREAMING_SNAKE_CASE           =					AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE					,	return_dict=SCREAMING_SNAKE_CASE   )

			# 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=SCREAMING_SNAKE_CASE   )

			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(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
			):
						SCREAMING_SNAKE_CASE           =					get_linear_schedule_with_warmup(
						    optimizer=SCREAMING_SNAKE_CASE					,	num_warmup_steps=0					,	num_training_steps=SCREAMING_SNAKE_CASE					,	)
			else:
						SCREAMING_SNAKE_CASE           =					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.
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					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
			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(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(SCREAMING_SNAKE_CASE   ):
												SCREAMING_SNAKE_CASE           =					model(**SCREAMING_SNAKE_CASE   )
												SCREAMING_SNAKE_CASE           =					outputs.loss
												SCREAMING_SNAKE_CASE           =					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   )   )   )
						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(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					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=3_20					,	help="""Number of training examples to use."""					,	)
			parser.add_argument(
			    """--n_val"""					,	type=SCREAMING_SNAKE_CASE					,	default=1_60					,	help="""Number of validation examples to use."""					,	)
			parser.add_argument(
			    """--num_epochs"""					,	type=SCREAMING_SNAKE_CASE					,	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""": 42, """batch_size""": 16}
			training_function(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
		main()












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""       ,     return_dict=a			).to(a			)
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""google/mt5-small"""			)

										SCREAMING_SNAKE_CASE           =					tokenizer("""Hello there"""       ,     return_tensors="""pt"""			).input_ids
										SCREAMING_SNAKE_CASE           =					tokenizer("""Hi I am"""       ,     return_tensors="""pt"""			).input_ids

										SCREAMING_SNAKE_CASE           =					model(input_ids.to(a			)       ,     labels=labels.to(a			)			).loss
										SCREAMING_SNAKE_CASE           =					-(labels.shape[-1] * loss.item())

										SCREAMING_SNAKE_CASE           =					-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)






