



'''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__:     Any										=    "true"


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		List[str]=82     ,   _lowerCAmelCase						:		Any=16						)   ->     Any:
							set_seed(42						)
							UpperCAmelCase				: List[Any]											=	RegressionModel()
							UpperCAmelCase				: List[Any]											=	deepcopy(_lowerCAmelCase						)
							UpperCAmelCase				: str											=	RegressionDataset(length=_lowerCAmelCase						)
							UpperCAmelCase				: Tuple											=	DataLoader(_lowerCAmelCase     ,   batch_size=_lowerCAmelCase						)
							model.to(accelerator.device						)
							UpperCAmelCase							,	UpperCAmelCase				: Optional[Any]											=	accelerator.prepare(_lowerCAmelCase     ,   _lowerCAmelCase						)
							return model, ddp_model, dataloader


def 				snake_case_  (      _lowerCAmelCase						:		Accelerator     ,   _lowerCAmelCase						:		Tuple=False						)   ->     Optional[Any]:
							UpperCAmelCase				: List[str]											=	AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''						)
							UpperCAmelCase				: Tuple											=	load_dataset('''glue'''     ,   '''mrpc'''     ,   split='''validation'''						)

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

							with accelerator.main_process_first():
														UpperCAmelCase				: Union[str, Any]											=	dataset.map(
														    _lowerCAmelCase     ,   batched=_lowerCAmelCase     ,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,   )

							UpperCAmelCase				: Any											=	tokenized_datasets.rename_column('''label'''     ,   '''labels'''						)

							def collate_fn(_lowerCAmelCase						:		Union[str, Any]						):
														if use_longest:
																					return tokenizer.pad(_lowerCAmelCase     ,   padding='''longest'''     ,   return_tensors='''pt'''						)
														return tokenizer.pad(_lowerCAmelCase     ,   padding='''max_length'''     ,   max_length=128     ,   return_tensors='''pt'''						)

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


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


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


def 				snake_case_  (      _lowerCAmelCase						:		Accelerator     ,   _lowerCAmelCase						:		List[Any]=82     ,   _lowerCAmelCase						:		Dict=False     ,   _lowerCAmelCase						:		List[Any]=False     ,   _lowerCAmelCase						:		Tuple=16						)   ->     Optional[int]:
							UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: str											=	get_basic_setup(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)
							UpperCAmelCase							,	UpperCAmelCase				: int											=	generate_predictions(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)
							assert (
							    len(_lowerCAmelCase						) == num_samples
							), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_lowerCAmelCase						)}"""


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

							# Then do distributed
							UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Any											=	setup['''ddp''']
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					UpperCAmelCase				: Union[str, Any]											=	model(**_lowerCAmelCase						)
														UpperCAmelCase				: Optional[int]											=	outputs.logits.argmax(dim=-1						)
														UpperCAmelCase				: Union[str, Any]											=	batch['''labels''']
														UpperCAmelCase							,	UpperCAmelCase				: Tuple											=	accelerator.gather_for_metrics((preds, references)						)
														metric.add_batch(predictions=_lowerCAmelCase     ,   references=_lowerCAmelCase						)
							UpperCAmelCase				: Optional[int]											=	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 				snake_case_  (      )   ->     List[str]:
							UpperCAmelCase				: List[Any]											=	Accelerator(split_batches=_lowerCAmelCase     ,   dispatch_batches=_lowerCAmelCase						)
							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(_lowerCAmelCase     ,   _lowerCAmelCase						)
																												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				: List[str]											=	Accelerator(split_batches=_lowerCAmelCase     ,   dispatch_batches=_lowerCAmelCase						)
																					if accelerator.is_local_main_process:
																												print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""						)
																					test_torch_metrics(_lowerCAmelCase     ,   99						)
																					accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print('''**Test last batch is not dropped when perfectly divisible**'''						)
							UpperCAmelCase				: Tuple											=	Accelerator()
							test_torch_metrics(_lowerCAmelCase     ,   512						)
							accelerator.state._reset_state()


def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     Union[str, Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
						main()










'''simple docstring'''






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=A__    )
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= field(default="""language-modeling"""							,	metadata={"""include_in_asdict_even_if_is_default""": True}    )

	lowerCamelCase__    				= Features({"""text""": Value("""string"""    )}    )
	lowerCamelCase__    				= Features({}    )
	lowerCamelCase__    				= "text"







	@property
	def   A			(		self    :  Optional[Any]	)    ->	Dict[str, str]:
								return {self.text_column: "text"}





