





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
#
########################################################################


UpperCAmelCase						:       Optional[Any]        =					16
UpperCAmelCase						:       Dict        =					32







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Any:
					# Initialize accelerator
					if args.with_tracking:
										__A     :						Dict          =    Accelerator(
										    cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						log_with='all' ,						project_dir=args.project_dir  )
					else:
										__A     :						Optional[int]          =    Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision  )

					if hasattr(args.checkpointing_steps ,						'isdigit'  ):
										if args.checkpointing_steps == "epoch":
															__A     :						List[Any]          =    args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															__A     :						str          =    int(args.checkpointing_steps  )
										else:
															raise ValueError(
															    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""  )
					else:
										__A     :						Optional[int]          =    None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__A     :						str          =    config['lr']
					__A     :						List[Any]          =    int(config['num_epochs']  )
					__A     :						Tuple          =    int(config['seed']  )
					__A     :						str          =    int(config['batch_size']  )

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

					__A     :						str          =    AutoTokenizer.from_pretrained('bert-base-cased'  )
					__A     :						Dict          =    load_dataset('glue' ,						'mrpc'  )
					__A     :						Tuple          =    evaluate.load('glue' ,						'mrpc'  )

					def tokenize_function(a  ):
										# max_length=None => use the model max length (it's actually the default)
										__A     :						Optional[Any]          =    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
					# starting with the main process first:
					with accelerator.main_process_first():
										__A     :						Union[str, Any]          =    datasets.map(
										    a ,						batched=a ,						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
					__A     :						List[Any]          =    tokenized_datasets.rename_column('label' ,						'labels'  )

					# If the batch size is too big we use gradient accumulation
					__A     :						int          =    1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										__A     :						Union[str, Any]          =    batch_size // MAX_GPU_BATCH_SIZE
										__A     :						Any          =    MAX_GPU_BATCH_SIZE

					def collate_fn(a  ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__A     :						Optional[Any]          =    1_28 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__A     :						int          =    16
										elif accelerator.mixed_precision != "no":
															__A     :						int          =    8
										else:
															__A     :						Union[str, Any]          =    None

										return tokenizer.pad(
										    a ,						padding='longest' ,						max_length=a ,						pad_to_multiple_of=a ,						return_tensors='pt' ,						)

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

					set_seed(a  )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__A     :						Optional[int]          =    AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,						return_dict=a  )

					# 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).
					__A     :						Tuple          =    model.to(accelerator.device  )

					# Instantiate optimizer
					__A     :						Optional[int]          =    AdamW(params=model.parameters() ,						lr=a  )

					# Instantiate scheduler
					__A     :						Optional[int]          =    get_linear_schedule_with_warmup(
					    optimizer=a ,						num_warmup_steps=1_00 ,						num_training_steps=(len(a  ) * 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.
					__A   ,    __A   ,    __A   ,    __A   ,    __A     :						Dict          =    accelerator.prepare(
					    a ,						a ,						a ,						a ,						a  )

					# We need to keep track of how many total steps we have iterated over
					__A     :						Union[str, Any]          =    0
					# We also need to keep track of the stating epoch so files are named properly
					__A     :						List[Any]          =    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  )
															__A     :						Union[str, Any]          =    os.path.basename(args.resume_from_checkpoint  )
										else:
															# Get the most recent checkpoint
															__A     :						Any          =    [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
															dirs.sort(key=os.path.getctime  )
															__A     :						Optional[int]          =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										__A     :						int          =    os.path.splitext(a  )[0]

										if "epoch" in training_difference:
															__A     :						Dict          =    int(training_difference.replace('epoch_' ,						''  )  ) + 1
															__A     :						str          =    None
										else:
															__A     :						Union[str, Any]          =    int(training_difference.replace('step_' ,						''  )  )
															__A     :						Any          =    resume_step // len(a  )
															resume_step -= starting_epoch * len(a  )

    # Now we train the model
					for epoch in range(a ,						a  ):
										model.train()
										if args.with_tracking:
															__A     :						Optional[int]          =    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
															__A     :						str          =    accelerator.skip_first_batches(a ,						a  )
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															__A     :						Optional[Any]          =    train_dataloader
										for step, batch in enumerate(a  ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device  )
															__A     :						Optional[int]          =    model(**a  )
															__A     :						int          =    outputs.loss
															__A     :						Tuple          =    loss / gradient_accumulation_steps
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(a  )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

															if isinstance(a ,						a  ):
																				__A     :						List[Any]          =    F"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														__A     :						int          =    os.path.join(args.output_dir ,						a  )
																									accelerator.save_state(a  )

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

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

										if checkpointing_steps == "epoch":
															__A     :						int          =    F"""epoch_{epoch}"""
															if args.output_dir is not None:
																				__A     :						Tuple          =    os.path.join(args.output_dir ,						a  )
															accelerator.save_state(a  )

					if args.with_tracking:
										accelerator.end_training()







def 			_SCREAMING_SNAKE_CASE    (       )				->				str:
					__A     :						str          =    argparse.ArgumentParser(description='Simple example of training script.'  )
					parser.add_argument(
					    '--mixed_precision' ,						type=a ,						default=a ,						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=a ,						default=a ,						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=a ,						default=a ,						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=a ,						default='.' ,						help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,						)
					parser.add_argument(
					    '--project_dir' ,						type=a ,						default='logs' ,						help='Location on where to store experiment tracking logs` and relevent project information' ,						)
					__A     :						Union[str, Any]          =    parser.parse_args()
					__A     :						Tuple          =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(a ,						a  )


if __name__ == "__main__":
				main()











from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase						:       str        =					logging.get_logger(__name__)

UpperCAmelCase						:       Optional[Any]        =					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Union[str, Any]    =     '''swiftformer'''



				def __init__(      self					, _A=3					, _A=[3, 3, 6, 4]					, _A=[48, 56, 112, 220]					, _A=4					, _A=[True, True, True, True]					, _A="gelu"					, _A=3					, _A=2					, _A=1					, _A=0.0					, _A=True					, _A=1e-5					, _A=1e-5					, **_A					, ):
									super().__init__(**_A					)
									__A     :						List[Any]          =    num_channels
									__A     :						List[Any]          =    depths
									__A     :						int          =    embed_dims
									__A     :						Union[str, Any]          =    mlp_ratio
									__A     :						List[str]          =    downsamples
									__A     :						str          =    hidden_act
									__A     :						Any          =    down_patch_size
									__A     :						Any          =    down_stride
									__A     :						Any          =    down_pad
									__A     :						List[Any]          =    drop_path_rate
									__A     :						str          =    use_layer_scale
									__A     :						Dict          =    layer_scale_init_value
									__A     :						Union[str, Any]          =    batch_norm_eps





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Optional[Any]    =     version.parse('''1.11'''       )



				@property
				def  UpperCAmelCase_		(      self					):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]					)





				@property
				def  UpperCAmelCase_		(      self					):
									return 1e-4




