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            =		16
_UpperCAmelCase            =		32




def     UpperCamelCase						(	__lowercase						:			Optional[Any]     ):
		'''simple docstring'''




		return int(x / 2**20     )





class UpperCAmelCase						:


				'''simple docstring'''






				def __enter__(						self ):



						"""simple docstring"""





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







				def __exit__(						self		,     *lowercase ):



						"""simple docstring"""





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




def     UpperCamelCase						(	__lowercase						:			Accelerator						,__lowercase						:			int = 16						,__lowercase						:			str = "bert-base-cased"						,__lowercase						:			int = 3_20						,__lowercase						:			int = 1_60						,):
		'''simple docstring'''




		A_		:    Any         =				AutoTokenizer.from_pretrained(__lowercase     )
		A_		:    Union[str, Any]         =				load_dataset(
		    'glue'						,'mrpc'						,split={'train': f'''train[:{n_train}]''', 'validation': f'''validation[:{n_val}]'''}     )

		def tokenize_function(__lowercase						:			Dict     ):
				# max_length=None => use the model max length (it's actually the default)
				A_		:    str         =				tokenizer(examples['sentence1']						,examples['sentence2']						,truncation=__lowercase						,max_length=__lowercase     )
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		A_		:    List[str]         =				datasets.map(
		    __lowercase						,batched=__lowercase						,remove_columns=['idx', 'sentence1', 'sentence2']						,load_from_cache_file=__lowercase     )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		A_		:    Dict         =				tokenized_datasets.rename_column('label'						,'labels'     )

		def collate_fn(__lowercase						:			Tuple     ):
				# 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(__lowercase						,padding='max_length'						,max_length=1_28						,return_tensors='pt'     )
				return tokenizer.pad(__lowercase						,padding='longest'						,return_tensors='pt'     )

		# Instantiate dataloaders.
		A_		:    Dict         =				DataLoader(
		    tokenized_datasets['train']						,shuffle=__lowercase						,collate_fn=__lowercase						,batch_size=__lowercase     )
		A_		:    Optional[int]         =				DataLoader(
		    tokenized_datasets['validation']						,shuffle=__lowercase						,collate_fn=__lowercase						,batch_size=__lowercase     )

		return train_dataloader, eval_dataloader




def     UpperCamelCase						(	__lowercase						:			int						,__lowercase						:			List[str]     ):
		'''simple docstring'''




		A_		:    List[Any]         =				Accelerator()

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

		set_seed(__lowercase     )
		A_       ,      A_		:    Optional[int]         =				get_dataloaders(__lowercase						,__lowercase						,__lowercase						,args.n_train						,args.n_val     )

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		A_		:    Any         =				AutoModelForSequenceClassification.from_pretrained(__lowercase						,return_dict=__lowercase     )

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

		if accelerator.state.deepspeed_plugin is not None:
				A_		:    str         =				accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				A_		:    List[Any]         =				1
		A_		:    Optional[Any]         =				(len(__lowercase     ) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				A_		:    List[str]         =				get_linear_schedule_with_warmup(
				    optimizer=__lowercase						,num_warmup_steps=0						,num_training_steps=__lowercase						,)
		else:
				A_		:    Any         =				DummyScheduler(__lowercase						,total_num_steps=__lowercase						,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.
		A_       ,      A_       ,      A_       ,      A_       ,      A_		:    List[Any]         =				accelerator.prepare(
		    __lowercase						,__lowercase						,__lowercase						,__lowercase						,__lowercase     )

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

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




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Tuple         =				argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'     )
		parser.add_argument(
		    '--model_name_or_path'						,type=__lowercase						,default='bert-base-cased'						,help='Path to pretrained model or model identifier from huggingface.co/models.'						,required=__lowercase						,)
		parser.add_argument(
		    '--output_dir'						,type=__lowercase						,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=__lowercase						,default=__lowercase						,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=__lowercase						,default=3_20						,help='Number of training examples to use.'						,)
		parser.add_argument(
		    '--n_val'						,type=__lowercase						,default=1_60						,help='Number of validation examples to use.'						,)
		parser.add_argument(
		    '--num_epochs'						,type=__lowercase						,default=1						,help='Number of train epochs.'						,)
		A_		:    str         =				parser.parse_args()
		A_		:    Tuple         =				{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
		training_function(__lowercase						,__lowercase     )


if __name__ == "__main__":
	main()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase            =		{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase            =		[
	    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """InstructBlipQFormerModel""",
	    """InstructBlipPreTrainedModel""",
	    """InstructBlipForConditionalGeneration""",
	    """InstructBlipVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_instructblip import (
	    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    InstructBlipConfig,
	    InstructBlipQFormerConfig,
	    InstructBlipVisionConfig,
	)
	from .processing_instructblip import InstructBlipProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    InstructBlipForConditionalGeneration,
		    InstructBlipPreTrainedModel,
		    InstructBlipQFormerModel,
		    InstructBlipVisionModel,
		)

else:
	import sys

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






