






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


lowerCamelCase : Any						   =       16
lowerCamelCase : Optional[Any]						   =       32
def       snake_case_						(     lowerCAmelCase_       :						Accelerator       ,       lowerCAmelCase_       :						int = 16      ):
							__lowercase      :							str				=     AutoTokenizer.from_pretrained("""bert-base-cased"""      )
							__lowercase      :							str				=     load_dataset("""glue"""       ,       """mrpc"""      )

							def tokenize_function(lowerCAmelCase_       :						str      ):
														# max_length=None => use the model max length (it's actually the default)
														__lowercase      :							Any				=     tokenizer(examples["""sentence1"""]       ,       examples["""sentence2"""]       ,       truncation=lowerCAmelCase_       ,       max_length=lowerCAmelCase_      )
														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():
														__lowercase      :							Optional[int]				=     datasets.map(
														    lowerCAmelCase_       ,       batched=lowerCAmelCase_       ,       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
							__lowercase      :							str				=     tokenized_datasets.rename_column("""label"""       ,       """labels"""      )

							def collate_fn(lowerCAmelCase_       :						Dict      ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__lowercase      :							str				=     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":
																					__lowercase      :							str				=     16
														elif accelerator.mixed_precision != "no":
																					__lowercase      :							Union[str, Any]				=     8
														else:
																					__lowercase      :							Any				=     None

														return tokenizer.pad(
														    lowerCAmelCase_       ,       padding="""longest"""       ,       max_length=lowerCAmelCase_       ,       pad_to_multiple_of=lowerCAmelCase_       ,       return_tensors="""pt"""       ,       )

							# Instantiate dataloaders.
							__lowercase      :							Union[str, Any]				=     DataLoader(
							    tokenized_datasets["""train"""]       ,       shuffle=lowerCAmelCase_       ,       collate_fn=lowerCAmelCase_       ,       batch_size=lowerCAmelCase_       ,       drop_last=lowerCAmelCase_      )
							__lowercase      :							int				=     DataLoader(
							    tokenized_datasets["""validation"""]       ,       shuffle=lowerCAmelCase_       ,       collate_fn=lowerCAmelCase_       ,       batch_size=lowerCAmelCase_       ,       drop_last=(accelerator.mixed_precision == """fp8""")       ,       )

							return train_dataloader, eval_dataloader
def       snake_case_						(     lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						Any      ):
							# Initialize accelerator
							__lowercase      :							Optional[int]				=     Accelerator(cpu=args.cpu       ,       mixed_precision=args.mixed_precision      )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__lowercase      :							Any				=     config["""lr"""]
							__lowercase      :							Union[str, Any]				=     int(config["""num_epochs"""]      )
							__lowercase      :							Optional[int]				=     int(config["""seed"""]      )
							__lowercase      :							Optional[Any]				=     int(config["""batch_size"""]      )

							__lowercase      :							Optional[int]				=     evaluate.load("""glue"""       ,       """mrpc"""      )

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

							set_seed(lowerCAmelCase_      )
							__lowercase      :							List[str]				=     get_dataloaders(lowerCAmelCase_       ,       lowerCAmelCase_      )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__lowercase      :							Tuple				=     AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""       ,       return_dict=lowerCAmelCase_      )

							# 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).
							__lowercase      :							Any				=     model.to(accelerator.device      )
							# Instantiate optimizer
							__lowercase      :							int				=     AdamW(params=model.parameters()       ,       lr=lowerCAmelCase_      )

							# Instantiate scheduler
							__lowercase      :							List[Any]				=     get_linear_schedule_with_warmup(
							    optimizer=lowerCAmelCase_       ,       num_warmup_steps=100       ,       num_training_steps=(len(lowerCAmelCase_      ) * 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.

							__lowercase      :							List[str]				=     accelerator.prepare(
							    lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )

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

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

														__lowercase      :							List[Any]				=     metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}:"       ,       lowerCAmelCase_      )
def       snake_case_						(     ):
							__lowercase      :							Union[str, Any]				=     argparse.ArgumentParser(description="""Simple example of training script."""      )
							parser.add_argument(
							    """--mixed_precision"""       ,       type=lowerCAmelCase_       ,       default=lowerCAmelCase_       ,       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."""      )
							__lowercase      :							List[Any]				=     parser.parse_args()
							__lowercase      :							List[str]				=     {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(lowerCAmelCase_       ,       lowerCAmelCase_      )


if __name__ == "__main__":
	main()







def       snake_case_		(     lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						int      ):
							return int((input_a, input_a).count(0      ) == 0      )
def       snake_case_		(     ):
							assert and_gate(0       ,       0      ) == 0
							assert and_gate(0       ,       1      ) == 0
							assert and_gate(1       ,       0      ) == 0
							assert and_gate(1       ,       1      ) == 1


if __name__ == "__main__":
	test_and_gate()
	print(and_gate(1, 0))
	print(and_gate(0, 0))
	print(and_gate(0, 1))
	print(and_gate(1, 1))