
"""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"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case					  =						logging.get_logger(__name__)

snake_case					  =						{
    # pcqm4mv1 now deprecated
    'graphormer-base': 'https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json',
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}




class 	UpperCamelCase      (			__magic_name__ ):

				"""simple docstring"""



				UpperCAmelCase_							: Dict		    =							"graphormer"
				UpperCAmelCase_							: Optional[int]		    =							["past_key_values"]


				def __init__(							self						,      lowercase__ = 1						,      lowercase__ = 512 * 9						,      lowercase__ = 512 * 3						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 512						,      lowercase__ = 128						,      lowercase__ = 5						,      lowercase__ = 1024						,      lowercase__ = "multi_hop"						,      lowercase__ = 512						,      lowercase__ = False						,      lowercase__ = 12						,      lowercase__ = 768						,      lowercase__ = 768						,      lowercase__ = 32						,      lowercase__ = 0.1						,      lowercase__ = 0.1						,      lowercase__ = 0.0						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = False						,      lowercase__ = "gelu"						,      lowercase__ = None						,      lowercase__ = False						,      lowercase__ = 0						,      lowercase__ = False						,      lowercase__ = 0.0						,      lowercase__ = 8						,      lowercase__ = None						,      lowercase__ = None						,      lowercase__ = True						,      lowercase__ = True						,      lowercase__=0						,      lowercase__=1						,      lowercase__=2						,      **lowercase__						,      )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      num_classes
									SCREAMING_SNAKE_CASE           =      num_atoms
									SCREAMING_SNAKE_CASE           =      num_in_degree
									SCREAMING_SNAKE_CASE           =      num_out_degree
									SCREAMING_SNAKE_CASE           =      num_edges
									SCREAMING_SNAKE_CASE           =      num_spatial
									SCREAMING_SNAKE_CASE           =      num_edge_dis
									SCREAMING_SNAKE_CASE           =      edge_type
									SCREAMING_SNAKE_CASE           =      multi_hop_max_dist
									SCREAMING_SNAKE_CASE           =      spatial_pos_max
									SCREAMING_SNAKE_CASE           =      max_nodes
									SCREAMING_SNAKE_CASE           =      num_hidden_layers
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      embedding_dim
									SCREAMING_SNAKE_CASE           =      ffn_embedding_dim
									SCREAMING_SNAKE_CASE           =      num_attention_heads
									SCREAMING_SNAKE_CASE           =      dropout
									SCREAMING_SNAKE_CASE           =      attention_dropout
									SCREAMING_SNAKE_CASE           =      layerdrop
									SCREAMING_SNAKE_CASE           =      encoder_normalize_before
									SCREAMING_SNAKE_CASE           =      pre_layernorm
									SCREAMING_SNAKE_CASE           =      apply_graphormer_init
									SCREAMING_SNAKE_CASE           =      activation_fn
									SCREAMING_SNAKE_CASE           =      embed_scale
									SCREAMING_SNAKE_CASE           =      freeze_embeddings
									SCREAMING_SNAKE_CASE           =      num_trans_layers_to_freeze
									SCREAMING_SNAKE_CASE           =      share_input_output_embed
									SCREAMING_SNAKE_CASE           =      traceable
									SCREAMING_SNAKE_CASE           =      q_noise
									SCREAMING_SNAKE_CASE           =      qn_block_size

									# These parameters are here for future extensions
									# atm, the model only supports self attention
									SCREAMING_SNAKE_CASE           =      kdim
									SCREAMING_SNAKE_CASE           =      vdim
									SCREAMING_SNAKE_CASE           =      self_attention
									SCREAMING_SNAKE_CASE           =      bias

									super().__init__(
									    pad_token_id=lowercase__						,      bos_token_id=lowercase__						,      eos_token_id=lowercase__						,      **lowercase__						,      )
