


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


_UpperCAmelCase			:  List[str]     =16
_UpperCAmelCase			:  Union[str, Any]     =32



def 			lowerCAmelCase (		lowerCAmelCase_ )-> List[Any]:
				return int(x / 2**20 )







class 				snake_case__:







						'''simple docstring'''




						def __enter__(				self							)					->    List[str]:
										gc.collect()
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
										lowerCAmelCase_ :      Optional[int]    				=				torch.cuda.memory_allocated()
										return self




						def __exit__(				self   ,						*__lowercase							)					->    Dict:
										gc.collect()
										torch.cuda.empty_cache()
										lowerCAmelCase_ :      Any    				=				torch.cuda.memory_allocated()
										lowerCAmelCase_ :      Union[str, Any]    				=				torch.cuda.max_memory_allocated()
										lowerCAmelCase_ :      Any    				=				bamb(self.end - self.begin							)
										lowerCAmelCase_ :      Tuple    				=				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 ,   )-> Dict:
				lowerCAmelCase_ :      List[str]    				=				AutoTokenizer.from_pretrained(lowerCAmelCase_ )
				lowerCAmelCase_ :      List[str]    				=				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)
								lowerCAmelCase_ :      int    				=				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
				lowerCAmelCase_ :      Tuple    				=				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
				lowerCAmelCase_ :      Dict    				=				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.
				lowerCAmelCase_ :      List[str]    				=				DataLoader(
				    tokenized_datasets['''train'''] ,   shuffle=lowerCAmelCase_ ,   collate_fn=lowerCAmelCase_ ,   batch_size=lowerCAmelCase_ )
				lowerCAmelCase_ :      Any    				=				DataLoader(
				    tokenized_datasets['''validation'''] ,   shuffle=lowerCAmelCase_ ,   collate_fn=lowerCAmelCase_ ,   batch_size=lowerCAmelCase_ )

				return train_dataloader, eval_dataloader



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> Any:
				# Initialize accelerator
				lowerCAmelCase_ :      Optional[int]    				=				Accelerator()

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

				set_seed(lowerCAmelCase_ )
				lowerCAmelCase_				,  lowerCAmelCase_ :      List[str]    				=				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)
				lowerCAmelCase_ :      Optional[int]    				=				AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_ ,   return_dict=lowerCAmelCase_ )

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

				if accelerator.state.deepspeed_plugin is not None:
								lowerCAmelCase_ :      Dict    				=				accelerator.state.deepspeed_plugin.deepspeed_config[
								    '''gradient_accumulation_steps'''
								]
				else:
								lowerCAmelCase_ :      Tuple    				=				1
				lowerCAmelCase_ :      List[Any]    				=				(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
				):
								lowerCAmelCase_ :      str    				=				get_linear_schedule_with_warmup(
								    optimizer=lowerCAmelCase_ ,   num_warmup_steps=0 ,   num_training_steps=lowerCAmelCase_ ,   )
				else:
								lowerCAmelCase_ :      Tuple    				=				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.
				lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      List[str]    				=				accelerator.prepare(
				    lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )

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

				# Now we train the model
				lowerCAmelCase_ :      Any    				=				{}
				for epoch in range(lowerCAmelCase_ ,   lowerCAmelCase_ ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(lowerCAmelCase_ ):
																lowerCAmelCase_ :      List[Any]    				=				model(**lowerCAmelCase_ )
																lowerCAmelCase_ :      Dict    				=				outputs.loss
																lowerCAmelCase_ :      Optional[Any]    				=				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 ) ) )
								lowerCAmelCase_ :      List[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(lowerCAmelCase_ ,   lowerCAmelCase_ )



def 			lowerCAmelCase (		)-> Dict:
				lowerCAmelCase_ :      List[Any]    				=				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.''' ,   )
				lowerCAmelCase_ :      Dict    				=				parser.parse_args()
				lowerCAmelCase_ :      Dict    				=				{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCAmelCase_ ,   lowerCAmelCase_ )


if __name__ == "__main__":
						main()



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase			:  Union[str, Any]     ={
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Optional[Any]     =["""LlamaTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Optional[int]     =["""LlamaTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Tuple     =[
						    """LlamaForCausalLM""",
						    """LlamaModel""",
						    """LlamaPreTrainedModel""",
						    """LlamaForSequenceClassification""",
						]


if TYPE_CHECKING:
						from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama import LlamaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama_fast import LlamaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
						import sys

						_UpperCAmelCase			:  List[Any]     =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)