


'''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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


A   : Dict             =			16
A   : Any             =			32





def       lowercase_      (		lowercase__				,  lowercase__ = 16						)   ->List[Any]:
						_snake_case:   int  					=		AutoTokenizer.from_pretrained('bert-base-cased'						)
						_snake_case:   Dict  					=		load_dataset('glue'				,  'mrpc'						)

						def tokenize_function(lowercase__						):
												# max_length=None => use the model max length (it's actually the default)
												_snake_case:   Optional[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():
												_snake_case:   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
						_snake_case:   Dict  					=		tokenized_datasets.rename_column('label'				,  'labels'						)

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

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

						# Instantiate dataloaders.
						_snake_case:   List[Any]  					=		DataLoader(
						    tokenized_datasets['train']				,  shuffle=lowercase__				,  collate_fn=lowercase__				,  batch_size=lowercase__						)
						_snake_case:   int  					=		DataLoader(
						    tokenized_datasets['validation']				,  shuffle=lowercase__				,  collate_fn=lowercase__				,  batch_size=lowercase__						)

						return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		A   : Union[str, Any]             =			mocked_dataloaders  # noqa: F811





def       lowercase_      (		lowercase__				,  lowercase__						)   ->Any:
						# For testing only
						if os.environ.get('TESTING_MOCKED_DATALOADERS'				,  lowercase__						) == "1":
												_snake_case:   int  					=		2
						# Initialize accelerator
						_snake_case:   List[Any]  					=		Accelerator(cpu=args.cpu				,  mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_snake_case:   int  					=		config['lr']
						_snake_case:   List[Any]  					=		int(config['num_epochs']						)
						_snake_case:   Tuple  					=		int(config['seed']						)
						_snake_case:   Optional[int]  					=		int(config['batch_size']						)

						_snake_case:   Optional[Any]  					=		evaluate.load('glue'				,  'mrpc'						)

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

						set_seed(lowercase__						)
						_snake_case:   int  					=		get_dataloaders(lowercase__				,  lowercase__						)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						_snake_case:   Optional[int]  					=		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).
						_snake_case:   Tuple  					=		model.to(accelerator.device						)

						# Instantiate optimizer
						_snake_case:   Union[str, Any]  					=		AdamW(params=model.parameters()				,  lr=lowercase__						)

						# Instantiate scheduler
						_snake_case:   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.
						_snake_case:   List[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						)
																		_snake_case:   Optional[int]  					=		model(**lowercase__						)
																		_snake_case:   Optional[Any]  					=		outputs.loss
																		_snake_case:   Any  					=		loss / gradient_accumulation_steps
																		accelerator.backward(lowercase__						)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												_snake_case:   List[Any]  					=		0
												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():
																								_snake_case:   int  					=		model(**lowercase__						)
																		_snake_case:   Optional[int]  					=		outputs.logits.argmax(dim=-1						)
																		_snake_case:   Optional[int]  					=		accelerator.gather((predictions, batch['labels'])						)
																		# New Code #
																		# First we check if it's a distributed system
																		if accelerator.use_distributed:
																								# Then see if we're on the last batch of our eval dataloader
																								if step == len(lowercase__						) - 1:
																														# Last batch needs to be truncated on distributed systems as it contains additional samples
																														_snake_case:   str  					=		predictions[: len(eval_dataloader.dataset						) - samples_seen]
																														_snake_case:   List[Any]  					=		references[: len(eval_dataloader.dataset						) - samples_seen]
																								else:
																														# Otherwise we add the number of samples seen
																														samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																		metric.add_batch(
																		    predictions=lowercase__				,  references=lowercase__				,  )

												_snake_case:   Optional[int]  					=		metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''				,  lowercase__						)





def       lowercase_      (		)   ->List[str]:
						_snake_case:   Optional[int]  					=		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.'						)
						_snake_case:   int  					=		parser.parse_args()
						_snake_case:   Union[str, Any]  					=		{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(lowercase__				,  lowercase__						)


if __name__ == "__main__":
		main()












'''simple docstring'''






def       lowercase_      (		lowercase__ = 50						)   ->int:
						_snake_case:   Union[str, Any]  					=		[[0] * 3 for _ in range(length + 1						)]

						for row_length in range(length + 1						):
												for tile_length in range(2				,  5						):
																		for tile_start in range(row_length - tile_length + 1						):
																								different_colour_ways_number[row_length][tile_length - 2] += (
																								    different_colour_ways_number[row_length - tile_start - tile_length][
																								        tile_length - 2
																								    ]
																								    + 1
																								)

						return sum(different_colour_ways_number[length]						)


if __name__ == "__main__":
		print(F'{solution() = }')





