
"""simple docstring"""




import argparse
import os

import evaluate
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


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


snake_case    =		1_6
snake_case    =		3_2

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					# Initialize accelerator
					if args.with_tracking:
										SCREAMING_SNAKE_CASE				=	Accelerator(
										    cpu=args.cpu,     mixed_precision=args.mixed_precision,     log_with='all',     project_dir=args.project_dir			)
					else:
										SCREAMING_SNAKE_CASE				=	Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision			)

					if hasattr(args.checkpointing_steps,     'isdigit'			):
										if args.checkpointing_steps == "epoch":
															SCREAMING_SNAKE_CASE				=	args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															SCREAMING_SNAKE_CASE				=	int(args.checkpointing_steps			)
										else:
															raise ValueError(
															    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''			)
					else:
										SCREAMING_SNAKE_CASE				=	None
					# 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']			)

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										SCREAMING_SNAKE_CASE				=	os.path.split(SCREAMING_SNAKE_CASE_			)[-1].split('.'			)[0]
										accelerator.init_trackers(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

					SCREAMING_SNAKE_CASE				=	AutoTokenizer.from_pretrained('bert-base-cased'			)
					SCREAMING_SNAKE_CASE				=	load_dataset('glue',     'mrpc'			)
					SCREAMING_SNAKE_CASE				=	evaluate.load('glue',     'mrpc'			)

					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
					# starting with the main process first:
					with accelerator.main_process_first():
										SCREAMING_SNAKE_CASE				=	datasets.map(
										    SCREAMING_SNAKE_CASE_,     batched=SCREAMING_SNAKE_CASE_,     remove_columns=['idx', 'sentence1', 'sentence2'],     )

					# 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'			)

					# If the batch size is too big we use gradient accumulation
					SCREAMING_SNAKE_CASE				=	1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										SCREAMING_SNAKE_CASE				=	batch_size // MAX_GPU_BATCH_SIZE
										SCREAMING_SNAKE_CASE				=	MAX_GPU_BATCH_SIZE

					def collate_fn(SCREAMING_SNAKE_CASE_			):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										SCREAMING_SNAKE_CASE				=	1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															SCREAMING_SNAKE_CASE				=	1_6
										elif accelerator.mixed_precision != "no":
															SCREAMING_SNAKE_CASE				=	8
										else:
															SCREAMING_SNAKE_CASE				=	None

										return tokenizer.pad(
										    SCREAMING_SNAKE_CASE_,     padding='longest',     max_length=SCREAMING_SNAKE_CASE_,     pad_to_multiple_of=SCREAMING_SNAKE_CASE_,     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_			)

					set_seed(SCREAMING_SNAKE_CASE_			)

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					SCREAMING_SNAKE_CASE				=	AutoModelForSequenceClassification.from_pretrained('bert-base-cased',     return_dict=SCREAMING_SNAKE_CASE_			)

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					SCREAMING_SNAKE_CASE				=	model.to(accelerator.device			)

					# Instantiate optimizer
					SCREAMING_SNAKE_CASE				=	AdamW(params=model.parameters(),     lr=SCREAMING_SNAKE_CASE_			)

					# Instantiate scheduler
					SCREAMING_SNAKE_CASE				=	get_linear_schedule_with_warmup(
					    optimizer=SCREAMING_SNAKE_CASE_,     num_warmup_steps=1_0_0,     num_training_steps=(len(SCREAMING_SNAKE_CASE_			) * num_epochs) // gradient_accumulation_steps,     )

					# 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

					# Potentially load in the weights and states from a previous save
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''			)
															accelerator.load_state(args.resume_from_checkpoint			)
															SCREAMING_SNAKE_CASE				=	os.path.basename(args.resume_from_checkpoint			)
										else:
															# Get the most recent checkpoint
															SCREAMING_SNAKE_CASE				=	[f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
															dirs.sort(key=os.path.getctime			)
															SCREAMING_SNAKE_CASE				=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										SCREAMING_SNAKE_CASE				=	os.path.splitext(SCREAMING_SNAKE_CASE_			)[0]

										if "epoch" in training_difference:
															SCREAMING_SNAKE_CASE				=	int(training_difference.replace('epoch_',     ''			)			) + 1
															SCREAMING_SNAKE_CASE				=	None
										else:
															SCREAMING_SNAKE_CASE				=	int(training_difference.replace('step_',     ''			)			)
															SCREAMING_SNAKE_CASE				=	resume_step // len(SCREAMING_SNAKE_CASE_			)
															resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE_			)

    # Now we train the model
					for epoch in range(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										model.train()
										if args.with_tracking:
															SCREAMING_SNAKE_CASE				=	0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															SCREAMING_SNAKE_CASE				=	accelerator.skip_first_batches(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															SCREAMING_SNAKE_CASE				=	train_dataloader
										for step, batch in enumerate(SCREAMING_SNAKE_CASE_			):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device			)
															SCREAMING_SNAKE_CASE				=	model(**SCREAMING_SNAKE_CASE_			)
															SCREAMING_SNAKE_CASE				=	outputs.loss
															SCREAMING_SNAKE_CASE				=	loss / gradient_accumulation_steps
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(SCREAMING_SNAKE_CASE_			)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

															if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
																				SCREAMING_SNAKE_CASE				=	f'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														SCREAMING_SNAKE_CASE				=	os.path.join(args.output_dir,     SCREAMING_SNAKE_CASE_			)
																									accelerator.save_state(SCREAMING_SNAKE_CASE_			)

										model.eval()
										for step, batch in enumerate(SCREAMING_SNAKE_CASE_			):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device			)
															with torch.no_grad():
																				SCREAMING_SNAKE_CASE				=	model(**SCREAMING_SNAKE_CASE_			)
															SCREAMING_SNAKE_CASE				=	outputs.logits.argmax(dim=-1			)
															SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	accelerator.gather_for_metrics((predictions, batch['labels'])			)
															metric.add_batch(
															    predictions=SCREAMING_SNAKE_CASE_,     references=SCREAMING_SNAKE_CASE_,     )

										SCREAMING_SNAKE_CASE				=	metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}:''',     SCREAMING_SNAKE_CASE_			)
										if args.with_tracking:
															accelerator.log(
															    {
															        'accuracy': eval_metric['accuracy'],
															        'f1': eval_metric['f1'],
															        'train_loss': total_loss.item() / len(SCREAMING_SNAKE_CASE_			),
															        'epoch': epoch,
															    },     step=SCREAMING_SNAKE_CASE_,     )

										if checkpointing_steps == "epoch":
															SCREAMING_SNAKE_CASE				=	f'''epoch_{epoch}'''
															if args.output_dir is not None:
																				SCREAMING_SNAKE_CASE				=	os.path.join(args.output_dir,     SCREAMING_SNAKE_CASE_			)
															accelerator.save_state(SCREAMING_SNAKE_CASE_			)

					if args.with_tracking:
										accelerator.end_training()

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE				=	argparse.ArgumentParser(description='Simple example of training script.'			)
					parser.add_argument(
					    '--mixed_precision',     type=SCREAMING_SNAKE_CASE_,     default=SCREAMING_SNAKE_CASE_,     choices=['no', 'fp16', 'bf16', 'fp8'],     help='Whether to use mixed precision. Choose'
					    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
					    'and an Nvidia Ampere GPU.',     )
					parser.add_argument('--cpu',     action='store_true',     help='If passed, will train on the CPU.'			)
					parser.add_argument(
					    '--checkpointing_steps',     type=SCREAMING_SNAKE_CASE_,     default=SCREAMING_SNAKE_CASE_,     help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.',     )
					parser.add_argument(
					    '--resume_from_checkpoint',     type=SCREAMING_SNAKE_CASE_,     default=SCREAMING_SNAKE_CASE_,     help='If the training should continue from a checkpoint folder.',     )
					parser.add_argument(
					    '--with_tracking',     action='store_true',     help='Whether to load in all available experiment trackers from the environment and use them for logging.',     )
					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(
					    '--project_dir',     type=SCREAMING_SNAKE_CASE_,     default='logs',     help='Location on where to store experiment tracking logs` and relevent project information',     )
					SCREAMING_SNAKE_CASE				=	parser.parse_args()
					SCREAMING_SNAKE_CASE				=	{'lr': 2E-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
					training_function(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	main()


"""simple docstring"""




# Lint as: python3

import hashlib
from typing import Union

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										# Data already in bytes, returns as it as
										return hash_data
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										# We keep the data as it as for it ot be later encoded to UTF-8
										# However replace `\\` with `/` for Windows compatibility
										SCREAMING_SNAKE_CASE				=	hash_data.replace('\\',     '/'			)
					elif isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										SCREAMING_SNAKE_CASE				=	str(SCREAMING_SNAKE_CASE_			)
					else:
										# If data is not of the required type, raise error
										raise InvalidKeyError(SCREAMING_SNAKE_CASE_			)

					return hash_data.encode('utf-8'			)




class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__			)    ->    Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	'\nFAILURE TO GENERATE DATASET: Invalid key type detected'
									SCREAMING_SNAKE_CASE				=	f'''\nFound Key {hash_data} of type {type(lowercase__			)}'''
									SCREAMING_SNAKE_CASE				=	'\nKeys should be either str, int or bytes type'
									super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''			)






class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__		,	lowercase__=""			)    ->    Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	key
									SCREAMING_SNAKE_CASE				=	duplicate_key_indices
									SCREAMING_SNAKE_CASE				=	fix_msg
									SCREAMING_SNAKE_CASE				=	'Found multiple examples generated with the same key'
									if len(lowercase__			) <= 20:
														SCREAMING_SNAKE_CASE				=	f'''\nThe examples at index {', '.join(lowercase__			)} have the key {key}'''
									else:
														SCREAMING_SNAKE_CASE				=	f'''\nThe examples at index {', '.join(duplicate_key_indices[:20]			)}... ({len(lowercase__			) - 20} more) have the key {key}'''
									SCREAMING_SNAKE_CASE				=	'\n' + fix_msg if fix_msg else ''
									super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''			)






class      UpperCamelCase	:

				"""simple docstring"""



				def __init__(				self		,	lowercase__			)    ->    Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	hashlib.mda(_as_bytes(lowercase__			)			)




				def 							A					(				self		,	lowercase__			)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self._split_mda.copy()
									SCREAMING_SNAKE_CASE				=	_as_bytes(lowercase__			)
									mda.update(lowercase__			)
									# Convert to integer with hexadecimal conversion
									return int(mda.hexdigest()		,	16			)
