






import argparse

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)
#
# 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			:	List[str]								=16
_UpperCamelCase			:	List[str]								=32



def  a__ (__lowercase							:Accelerator					,  __lowercase							:int = 16   )		-> Optional[int]:
				_A	:				Union[str, Any]   =      AutoTokenizer.from_pretrained('''bert-base-cased'''   )
				_A	:				Tuple   =      load_dataset('''glue'''					,  '''mrpc'''   )

				def tokenize_function(__lowercase							:int   ):
								# max_length=None => use the model max length (it's actually the default)
								_A	:				int   =      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
				# starting with the main process first:
				with accelerator.main_process_first():
								_A	:				List[str]   =      datasets.map(
								    __lowercase					,  batched=__lowercase					,  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	:				int   =      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.
								_A	:				Tuple   =      128 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	:				Tuple   =      16
								elif accelerator.mixed_precision != "no":
												_A	:				Union[str, Any]   =      8
								else:
												_A	:				Dict   =      None

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

				# Instantiate dataloaders.
				_A	:				Tuple   =      DataLoader(
				    tokenized_datasets['''train''']					,  shuffle=__lowercase					,  collate_fn=__lowercase					,  batch_size=__lowercase					,  drop_last=__lowercase   )
				_A	:				Optional[int]   =      DataLoader(
				    tokenized_datasets['''validation''']					,  shuffle=__lowercase					,  collate_fn=__lowercase					,  batch_size=__lowercase					,  drop_last=(accelerator.mixed_precision == '''fp8''')					,  )

				return train_dataloader, eval_dataloader



def  a__ (__lowercase							:Dict					,  __lowercase							:List[Any]   )		-> Union[str, Any]:
				# Initialize accelerator
				_A	:				Dict   =      Accelerator(cpu=args.cpu					,  mixed_precision=args.mixed_precision   )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_A	:				List[str]   =      config['''lr''']
				_A	:				Any   =      int(config['''num_epochs''']   )
				_A	:				List[str]   =      int(config['''seed''']   )
				_A	:				Optional[int]   =      int(config['''batch_size''']   )

				_A	:				List[str]   =      evaluate.load('''glue'''					,  '''mrpc'''   )

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

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

				# 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	:				Union[str, Any]   =      AdamW(params=model.parameters()					,  lr=__lowercase   )

				# Instantiate scheduler
				_A	:				Tuple   =      get_linear_schedule_with_warmup(
				    optimizer=__lowercase					,  num_warmup_steps=100					,  num_training_steps=(len(__lowercase   ) * 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	:				Any   =      accelerator.prepare(
				    __lowercase					,  __lowercase					,  __lowercase					,  __lowercase					,  __lowercase   )

				# Now we train the model
				for epoch in range(__lowercase   ):
								model.train()
								for step, batch in enumerate(__lowercase   ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device   )
												_A	:				Union[str, Any]   =      model(**__lowercase   )
												_A	:				List[str]   =      outputs.loss
												_A	:				Any   =      loss / gradient_accumulation_steps
												accelerator.backward(__lowercase   )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								_A	:				Union[str, Any]   =      metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}:"""					,  __lowercase   )



def  a__ ()		-> Optional[int]:
				_A	:				List[Any]   =      argparse.ArgumentParser(description='''Simple example of training script.'''   )
				parser.add_argument(
				    '''--mixed_precision'''					,  type=__lowercase					,  default=__lowercase					,  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.'''   )
				_A	:				str   =      parser.parse_args()
				_A	:				int   =      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(__lowercase					,  __lowercase   )


if __name__ == "__main__":
	main()











from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
_UpperCamelCase			:	List[str]								=HfArgumentParser(InitializationArguments)
_UpperCamelCase			:	Dict								=parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
_UpperCamelCase			:	List[Any]								=AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
_UpperCamelCase			:	str								={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
_UpperCamelCase			:	int								=AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
_UpperCamelCase			:	List[str]								=AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)



