


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


__snake_case      = "true"






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		str							,	SCREAMING_SNAKE_CASE_ :		Optional[int]=82							,	SCREAMING_SNAKE_CASE_ :		Optional[Any]=16						):





			"""simple docstring"""




			set_seed(42						)
			UpperCamelCase								=       RegressionModel()
			UpperCamelCase								=       deepcopy(UpperCAmelCase__						)
			UpperCamelCase								=       RegressionDataset(length=UpperCAmelCase__						)
			UpperCamelCase								=       DataLoader(UpperCAmelCase__							,	batch_size=UpperCAmelCase__						)
			model.to(accelerator.device						)
			UpperCamelCase	,	UpperCamelCase								=       accelerator.prepare(UpperCAmelCase__							,	UpperCAmelCase__						)
			return model, ddp_model, dataloader






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		List[Any]							,	SCREAMING_SNAKE_CASE_ :		str=False						):





			"""simple docstring"""




			UpperCamelCase								=       AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""						)
			UpperCamelCase								=       load_dataset("""glue"""							,	"""mrpc"""							,	split="""validation"""						)

			def tokenize_function(SCREAMING_SNAKE_CASE_ :		List[Any]						):
						UpperCamelCase								=       tokenizer(examples["""sentence1"""]							,	examples["""sentence2"""]							,	truncation=UpperCAmelCase__							,	max_length=UpperCAmelCase__						)
						return outputs

			with accelerator.main_process_first():
						UpperCamelCase								=       dataset.map(
						    UpperCAmelCase__							,	batched=UpperCAmelCase__							,	remove_columns=["""idx""", """sentence1""", """sentence2"""]							,	)

			UpperCamelCase								=       tokenized_datasets.rename_column("""label"""							,	"""labels"""						)

			def collate_fn(SCREAMING_SNAKE_CASE_ :		Optional[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 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		List[str]							,	SCREAMING_SNAKE_CASE_ :		List[str]						):





			"""simple docstring"""




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






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Optional[int]							,	SCREAMING_SNAKE_CASE_ :		str							,	SCREAMING_SNAKE_CASE_ :		List[str]						):





			"""simple docstring"""




			UpperCamelCase								=       []
			for batch in dataloader:
						UpperCamelCase	,	UpperCamelCase								=       batch.values()
						with torch.no_grad():
									UpperCamelCase								=       model(UpperCAmelCase__						)
									UpperCamelCase	,	UpperCamelCase								=       accelerator.gather_for_metrics((logit, target)						)
									logits_and_targets.append((logit, target)						)
			UpperCamelCase	,	UpperCamelCase								=       [], []
			for logit, targ in logits_and_targets:
						logits.append(UpperCAmelCase__						)
						targs.append(UpperCAmelCase__						)
			UpperCamelCase	,	UpperCamelCase								=       torch.cat(UpperCAmelCase__						), torch.cat(UpperCAmelCase__						)
			return logits, targs






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		str							,	SCREAMING_SNAKE_CASE_ :		List[Any]=82							,	SCREAMING_SNAKE_CASE_ :		Optional[int]=False							,	SCREAMING_SNAKE_CASE_ :		Union[str, Any]=False							,	SCREAMING_SNAKE_CASE_ :		str=16						):





			"""simple docstring"""




			UpperCamelCase	,	UpperCamelCase	,	UpperCamelCase								=       get_basic_setup(UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__						)
			UpperCamelCase	,	UpperCamelCase								=       generate_predictions(UpperCAmelCase__							,	UpperCAmelCase__							,	UpperCAmelCase__						)
			assert (
			    len(UpperCAmelCase__						) == num_samples
			), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCAmelCase__						)}'






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Optional[int] = False							,	SCREAMING_SNAKE_CASE_ :		str = False						):





			"""simple docstring"""




			UpperCamelCase								=       evaluate.load("""glue"""							,	"""mrpc"""						)
			UpperCamelCase	,	UpperCamelCase								=       get_mrpc_setup(UpperCAmelCase__							,	UpperCAmelCase__						)
			# First do baseline
			UpperCamelCase	,	UpperCamelCase	,	UpperCamelCase								=       setup["""no"""]
			model.to(UpperCAmelCase__						)
			model.eval()
			for batch in dataloader:
						batch.to(UpperCAmelCase__						)
						with torch.inference_mode():
									UpperCamelCase								=       model(**UpperCAmelCase__						)
						UpperCamelCase								=       outputs.logits.argmax(dim=-1						)
						metric.add_batch(predictions=UpperCAmelCase__							,	references=batch["""labels"""]						)
			UpperCamelCase								=       metric.compute()

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





			"""simple docstring"""




			UpperCamelCase								=       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]:
									UpperCamelCase								=       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**"""						)
			UpperCamelCase								=       Accelerator()
			test_torch_metrics(UpperCAmelCase__							,	512						)
			accelerator.state._reset_state()






def 				_lowercase     (		SCREAMING_SNAKE_CASE_ :		Dict						):





			"""simple docstring"""




			main()


if __name__ == "__main__":
			main()







'''simple docstring'''







import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""",							[None, 4_00 * 2**20, 6_00 * 2**20]					)
@pytest.mark.parametrize("""input_in_memory_max_size""",							["""default""", 0, 1_00 * 2**20, 9_00 * 2**20]					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if input_in_memory_max_size != "default":
              monkeypatch.setattr(datasets.config,							"""IN_MEMORY_MAX_SIZE""",							UpperCAmelCase__					)
       A_						       =   datasets.config.IN_MEMORY_MAX_SIZE
       if input_in_memory_max_size == "default":
              assert in_memory_max_size == 0
       else:
              assert in_memory_max_size == input_in_memory_max_size
       if dataset_size and in_memory_max_size:
              A_						       =   dataset_size < in_memory_max_size
       else:
              A_						       =   False
       A_						       =   is_small_dataset(UpperCAmelCase__					)
       assert result == expected
