


"""simple docstring"""




import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


__UpperCamelCase						=      '''true'''


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				int=82 ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]=16  )   ->						Optional[int]:
						set_seed(42  )
						SCREAMING_SNAKE_CASE													=					RegressionModel()
						SCREAMING_SNAKE_CASE													=					deepcopy(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					RegressionDataset(length=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					DataLoader(SCREAMING_SNAKE_CASE_ ,			batch_size=SCREAMING_SNAKE_CASE_  )
						model.to(accelerator.device  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					accelerator.prepare(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						return model, ddp_model, dataloader


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Accelerator ,			SCREAMING_SNAKE_CASE_		:				Optional[Any]=False  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'  )
						SCREAMING_SNAKE_CASE													=					load_dataset('glue' ,			'mrpc' ,			split='validation'  )

						def tokenize_function(SCREAMING_SNAKE_CASE_		:				Optional[int]  ):
												SCREAMING_SNAKE_CASE													=					tokenizer(examples['sentence1'] ,			examples['sentence2'] ,			truncation=SCREAMING_SNAKE_CASE_ ,			max_length=SCREAMING_SNAKE_CASE_  )
												return outputs

						with accelerator.main_process_first():
												SCREAMING_SNAKE_CASE													=					dataset.map(
												    SCREAMING_SNAKE_CASE_ ,			batched=SCREAMING_SNAKE_CASE_ ,			remove_columns=['idx', 'sentence1', 'sentence2'] ,			)

						SCREAMING_SNAKE_CASE													=					tokenized_datasets.rename_column('label' ,			'labels'  )

						def collate_fn(SCREAMING_SNAKE_CASE_		:				int  ):
												if use_longest:
																		return tokenizer.pad(SCREAMING_SNAKE_CASE_ ,			padding='longest' ,			return_tensors='pt'  )
												return tokenizer.pad(SCREAMING_SNAKE_CASE_ ,			padding='max_length' ,			max_length=1_28 ,			return_tensors='pt'  )

						return DataLoader(SCREAMING_SNAKE_CASE_ ,			shuffle=SCREAMING_SNAKE_CASE_ ,			collate_fn=SCREAMING_SNAKE_CASE_ ,			batch_size=16  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Tuple:
						SCREAMING_SNAKE_CASE													=					Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE_ ,			split_batches=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					get_dataloader(SCREAMING_SNAKE_CASE_ ,			not dispatch_batches  )
						SCREAMING_SNAKE_CASE													=					AutoModelForSequenceClassification.from_pretrained(
						    'hf-internal-testing/mrpc-bert-base-cased' ,			return_dict=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					accelerator.prepare(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				Any  )   ->						Optional[int]:
						SCREAMING_SNAKE_CASE													=					[]
						for batch in dataloader:
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					batch.values()
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE													=					model(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					accelerator.gather_for_metrics((logit, target)  )
																		logits_and_targets.append((logit, target)  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					[], []
						for logit, targ in logits_and_targets:
												logits.append(SCREAMING_SNAKE_CASE_  )
												targs.append(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					torch.cat(SCREAMING_SNAKE_CASE_  ), torch.cat(SCREAMING_SNAKE_CASE_  )
						return logits, targs


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Accelerator ,			SCREAMING_SNAKE_CASE_		:				List[str]=82 ,			SCREAMING_SNAKE_CASE_		:				List[str]=False ,			SCREAMING_SNAKE_CASE_		:				Optional[int]=False ,			SCREAMING_SNAKE_CASE_		:				List[Any]=16  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					get_basic_setup(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					generate_predictions(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						assert (
						    len(SCREAMING_SNAKE_CASE_  ) == num_samples
						), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE_  )}'


def     lowercase			(SCREAMING_SNAKE_CASE_		:				bool = False ,			SCREAMING_SNAKE_CASE_		:				bool = False  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					evaluate.load('glue' ,			'mrpc'  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					get_mrpc_setup(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						# First do baseline
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					setup['no']
						model.to(SCREAMING_SNAKE_CASE_  )
						model.eval()
						for batch in dataloader:
												batch.to(SCREAMING_SNAKE_CASE_  )
												with torch.inference_mode():
																		SCREAMING_SNAKE_CASE													=					model(**SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					outputs.logits.argmax(dim=-1  )
												metric.add_batch(predictions=SCREAMING_SNAKE_CASE_ ,			references=batch['labels']  )
						SCREAMING_SNAKE_CASE													=					metric.compute()

						# Then do distributed
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					setup['ddp']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		SCREAMING_SNAKE_CASE													=					model(**SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					outputs.logits.argmax(dim=-1  )
												SCREAMING_SNAKE_CASE													=					batch['labels']
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					accelerator.gather_for_metrics((preds, references)  )
												metric.add_batch(predictions=SCREAMING_SNAKE_CASE_ ,			references=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					metric.compute()

						for key in "accuracy f1".split():
												assert math.isclose(
												    baseline[key] ,			distributed[key]  ), F'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'


def     lowercase			()   ->						Tuple:
						SCREAMING_SNAKE_CASE													=					Accelerator(split_batches=SCREAMING_SNAKE_CASE_ ,			dispatch_batches=SCREAMING_SNAKE_CASE_  )
						if accelerator.is_local_main_process:
												datasets.utils.logging.set_verbosity_warning()
												transformers.utils.logging.set_verbosity_warning()
						else:
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						# These are a bit slower so they should only be ran on the GPU or TPU
						if torch.cuda.is_available() or is_tpu_available():
												if accelerator.is_local_main_process:
																		print('**Testing gather_for_metrics**'  )
												for split_batches in [True, False]:
																		for dispatch_batches in [True, False]:
																								if accelerator.is_local_main_process:
																														print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'  )
																								test_mrpc(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																								accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('**Test torch metrics**'  )
						for split_batches in [True, False]:
												for dispatch_batches in [True, False]:
																		SCREAMING_SNAKE_CASE													=					Accelerator(split_batches=SCREAMING_SNAKE_CASE_ ,			dispatch_batches=SCREAMING_SNAKE_CASE_  )
																		if accelerator.is_local_main_process:
																								print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'  )
																		test_torch_metrics(SCREAMING_SNAKE_CASE_ ,			99  )
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('**Test last batch is not dropped when perfectly divisible**'  )
						SCREAMING_SNAKE_CASE													=					Accelerator()
						test_torch_metrics(SCREAMING_SNAKE_CASE_ ,			5_12  )
						accelerator.state._reset_state()


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Dict:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
	main()






"""simple docstring"""




from __future__ import annotations


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						list[int]:
						return [ord(SCREAMING_SNAKE_CASE_  ) - 96 for elem in plain]


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list[int]  )   ->						str:
						return "".join(chr(elem + 96  ) for elem in encoded  )


def     lowercase			()   ->						None:
						SCREAMING_SNAKE_CASE													=					encode(input('-> '  ).strip().lower()  )
						print('Encoded: ' ,			SCREAMING_SNAKE_CASE_  )
						print('Decoded:' ,			decode(SCREAMING_SNAKE_CASE_  )  )


if __name__ == "__main__":
	main()


