





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


lowerCAmelCase							:       str					 =	'true'






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   Tuple=82       ,			UpperCamelCase__   :   Any=16					)      ->    int:

						"""simple docstring"""





						set_seed(42					)
						__SCREAMING_SNAKE_CASE:				Dict           =   RegressionModel()
						__SCREAMING_SNAKE_CASE:				int           =   deepcopy(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				int           =   RegressionDataset(length=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   DataLoader(UpperCamelCase__       ,			batch_size=UpperCamelCase__					)
						model.to(accelerator.device					)
						__SCREAMING_SNAKE_CASE:				int           =   accelerator.prepare(UpperCamelCase__       ,			UpperCamelCase__					)
						return model, ddp_model, dataloader






def 		lowerCAmelCase  (	UpperCamelCase__   :   Accelerator       ,			UpperCamelCase__   :   str=False					)      ->    Optional[int]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   load_dataset('''glue'''       ,			'''mrpc'''       ,			split='''validation'''					)

						def tokenize_function(UpperCamelCase__   :   str					):
												__SCREAMING_SNAKE_CASE:				Any           =   tokenizer(examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=UpperCamelCase__       ,			max_length=UpperCamelCase__					)
												return outputs

						with accelerator.main_process_first():
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   dataset.map(
												    UpperCamelCase__       ,			batched=UpperCamelCase__       ,			remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,			)

						__SCREAMING_SNAKE_CASE:				Tuple           =   tokenized_datasets.rename_column('''label'''       ,			'''labels'''					)

						def collate_fn(UpperCamelCase__   :   int					):
												if use_longest:
																		return tokenizer.pad(UpperCamelCase__       ,			padding='''longest'''       ,			return_tensors='''pt'''					)
												return tokenizer.pad(UpperCamelCase__       ,			padding='''max_length'''       ,			max_length=128       ,			return_tensors='''pt'''					)

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






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   Union[str, Any]					)      ->    Any:

						"""simple docstring"""





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






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]       ,			UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   List[Any]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[str]           =   []
						for batch in dataloader:
												__SCREAMING_SNAKE_CASE:				str           =   batch.values()
												with torch.no_grad():
																		__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model(UpperCamelCase__					)
																		__SCREAMING_SNAKE_CASE:				Tuple           =   accelerator.gather_for_metrics((logit, target)					)
																		logits_and_targets.append((logit, target)					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   [], []
						for logit, targ in logits_and_targets:
												logits.append(UpperCamelCase__					)
												targs.append(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Any           =   torch.cat(UpperCamelCase__					), torch.cat(UpperCamelCase__					)
						return logits, targs






def 		lowerCAmelCase  (	UpperCamelCase__   :   Accelerator       ,			UpperCamelCase__   :   Union[str, Any]=82       ,			UpperCamelCase__   :   List[str]=False       ,			UpperCamelCase__   :   Any=False       ,			UpperCamelCase__   :   Dict=16					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   get_basic_setup(UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[Any]           =   generate_predictions(UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__					)
						assert (
						    len(UpperCamelCase__					) == num_samples
						), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCamelCase__					)}"""






def 		lowerCAmelCase  (	UpperCamelCase__   :   bool = False       ,			UpperCamelCase__   :   bool = False					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   evaluate.load('''glue'''       ,			'''mrpc'''					)
						__SCREAMING_SNAKE_CASE:				List[Any]           =   get_mrpc_setup(UpperCamelCase__       ,			UpperCamelCase__					)
						# First do baseline
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   setup['no']
						model.to(UpperCamelCase__					)
						model.eval()
						for batch in dataloader:
												batch.to(UpperCamelCase__					)
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE:				str           =   model(**UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   outputs.logits.argmax(dim=-1					)
												metric.add_batch(predictions=UpperCamelCase__       ,			references=batch['''labels''']					)
						__SCREAMING_SNAKE_CASE:				Dict           =   metric.compute()

						# Then do distributed
						__SCREAMING_SNAKE_CASE:				int           =   setup['ddp']
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model(**UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				Dict           =   outputs.logits.argmax(dim=-1					)
												__SCREAMING_SNAKE_CASE:				List[Any]           =   batch['labels']
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   accelerator.gather_for_metrics((preds, references)					)
												metric.add_batch(predictions=UpperCamelCase__       ,			references=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[Any]           =   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 		lowerCAmelCase  (	)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[str]           =   Accelerator(split_batches=UpperCamelCase__       ,			dispatch_batches=UpperCamelCase__					)
						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(UpperCamelCase__       ,			UpperCamelCase__					)
																								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:				List[Any]           =   Accelerator(split_batches=UpperCamelCase__       ,			dispatch_batches=UpperCamelCase__					)
																		if accelerator.is_local_main_process:
																								print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""					)
																		test_torch_metrics(UpperCamelCase__       ,			99					)
																		accelerator.state._reset_state()
						if accelerator.is_local_main_process:
												print('''**Test last batch is not dropped when perfectly divisible**'''					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Accelerator()
						test_torch_metrics(UpperCamelCase__       ,			512					)
						accelerator.state._reset_state()






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]					)      ->    List[str]:

						"""simple docstring"""





						main()


if __name__ == "__main__":
							main()







'''simple docstring'''
def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int      ):
 return 1 if input_a == input_a else 0


def       _lowercase							(					):
 assert xnor_gate(0,					0      ) == 1
 assert xnor_gate(0,					1      ) == 0
 assert xnor_gate(1,					0      ) == 0
 assert xnor_gate(1,					1      ) == 1


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






