





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						:   int			  =     16
UpperCAmelCase						:   List[Any]			  =     32







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Optional[int]:
					return int(x / 2**20  )





class 						_A:


				"""simple docstring"""


				def __enter__(			self     ):
									gc.collect()
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
									__A	:						Union[str, Any]       =				torch.cuda.memory_allocated()
									return self





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







def  _SCREAMING_SNAKE_CASE				(   a					,						a = 16					,						a = "bert-base-cased"					,						a = 3_20					,						a = 1_60					,						)      ->      Optional[Any]:
					__A	:						str       =				AutoTokenizer.from_pretrained(a  )
					__A	:						List[Any]       =				load_dataset(
					    'glue'					,						'mrpc'					,						split={'train': F"""train[:{n_train}]""", 'validation': F"""validation[:{n_val}]"""}  )

					def tokenize_function(a  ):
										# max_length=None => use the model max length (it's actually the default)
										__A	:						Optional[int]       =				tokenizer(examples['sentence1']					,						examples['sentence2']					,						truncation=a					,						max_length=a  )
										return outputs

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

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

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

					# Instantiate dataloaders.
					__A	:						Tuple       =				DataLoader(
					    tokenized_datasets['train']					,						shuffle=a					,						collate_fn=a					,						batch_size=a  )
					__A	:						List[Any]       =				DataLoader(
					    tokenized_datasets['validation']					,						shuffle=a					,						collate_fn=a					,						batch_size=a  )

					return train_dataloader, eval_dataloader







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Union[str, Any]:
					# Initialize accelerator
					__A	:						int       =				Accelerator()

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

					set_seed(a  )
					__A    ,				__A	:						Dict       =				get_dataloaders(a					,						a					,						a					,						args.n_train					,						args.n_val  )

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

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

					if accelerator.state.deepspeed_plugin is not None:
										__A	:						str       =				accelerator.state.deepspeed_plugin.deepspeed_config[
										    'gradient_accumulation_steps'
										]
					else:
										__A	:						Optional[int]       =				1
					__A	:						List[Any]       =				(len(a  ) * 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	:						Tuple       =				get_linear_schedule_with_warmup(
										    optimizer=a					,						num_warmup_steps=0					,						num_training_steps=a					,						)
					else:
										__A	:						List[str]       =				DummyScheduler(a					,						total_num_steps=a					,						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	:						Optional[Any]       =				accelerator.prepare(
					    a					,						a					,						a					,						a					,						a  )

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

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







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


if __name__ == "__main__":
				main()











from typing import TYPE_CHECKING

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


UpperCAmelCase						:   Any			  =     {
    '''configuration_falcon''': ['''FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FalconConfig'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Any			  =     [
				    '''FALCON_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''FalconForCausalLM''',
				    '''FalconModel''',
				    '''FalconPreTrainedModel''',
				    '''FalconForSequenceClassification''',
				    '''FalconForTokenClassification''',
				    '''FalconForQuestionAnswering''',
				]


if TYPE_CHECKING:
				from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_falcon import (
								    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FalconForCausalLM,
								    FalconForQuestionAnswering,
								    FalconForSequenceClassification,
								    FalconForTokenClassification,
								    FalconModel,
								    FalconPreTrainedModel,
								)


else:
				import sys

				UpperCAmelCase						:   List[str]			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




