






'''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     :   Tuple	      =		'true'
def     A__      (					__lowerCAmelCase		:      int ,     __lowerCAmelCase		:      Union[str, Any]=82 ,     __lowerCAmelCase		:      Tuple=16    ):
						set_seed(42    )
						lowerCamelCase__								=	RegressionModel()
						lowerCamelCase__								=	deepcopy(__lowerCAmelCase    )
						lowerCamelCase__								=	RegressionDataset(length=__lowerCAmelCase    )
						lowerCamelCase__								=	DataLoader(__lowerCAmelCase ,     batch_size=__lowerCAmelCase    )
						model.to(accelerator.device    )
						lowerCamelCase__							,      lowerCamelCase__								=	accelerator.prepare(__lowerCAmelCase ,     __lowerCAmelCase    )
						return model, ddp_model, dataloader
def     A__      (					__lowerCAmelCase		:      Accelerator ,     __lowerCAmelCase		:      Optional[Any]=False    ):
						lowerCamelCase__								=	AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""    )
						lowerCamelCase__								=	load_dataset("""glue""" ,     """mrpc""" ,     split="""validation"""    )

						def tokenize_function(__lowerCAmelCase		:      str    ):
												lowerCamelCase__								=	tokenizer(examples["""sentence1"""] ,     examples["""sentence2"""] ,     truncation=__lowerCAmelCase ,     max_length=__lowerCAmelCase    )
												return outputs

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

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

						def collate_fn(__lowerCAmelCase		:      Dict    ):
												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     A__      (					__lowerCAmelCase		:      Union[str, Any] ,     __lowerCAmelCase		:      Optional[Any]    ):
						lowerCamelCase__								=	Accelerator(dispatch_batches=__lowerCAmelCase ,     split_batches=__lowerCAmelCase    )
						lowerCamelCase__								=	get_dataloader(__lowerCAmelCase ,     not dispatch_batches    )
						lowerCamelCase__								=	AutoModelForSequenceClassification.from_pretrained(
						    """hf-internal-testing/mrpc-bert-base-cased""" ,     return_dict=__lowerCAmelCase    )
						lowerCamelCase__							,      lowerCamelCase__								=	accelerator.prepare(__lowerCAmelCase ,     __lowerCAmelCase    )
						return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def     A__      (					__lowerCAmelCase		:      Optional[Any] ,     __lowerCAmelCase		:      List[Any] ,     __lowerCAmelCase		:      str    ):
						lowerCamelCase__								=	[]
						for batch in dataloader:
												lowerCamelCase__							,      lowerCamelCase__								=	batch.values()
												with torch.no_grad():
																		lowerCamelCase__								=	model(__lowerCAmelCase    )
																		lowerCamelCase__							,      lowerCamelCase__								=	accelerator.gather_for_metrics((logit, target)    )
																		logits_and_targets.append((logit, target)    )
						lowerCamelCase__							,      lowerCamelCase__								=	[], []
						for logit, targ in logits_and_targets:
												logits.append(__lowerCAmelCase    )
												targs.append(__lowerCAmelCase    )
						lowerCamelCase__							,      lowerCamelCase__								=	torch.cat(__lowerCAmelCase    ), torch.cat(__lowerCAmelCase    )
						return logits, targs
def     A__      (					__lowerCAmelCase		:      Accelerator ,     __lowerCAmelCase		:      Tuple=82 ,     __lowerCAmelCase		:      str=False ,     __lowerCAmelCase		:      Optional[Any]=False ,     __lowerCAmelCase		:      int=16    ):
						lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	get_basic_setup(__lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase    )
						lowerCamelCase__							,      lowerCamelCase__								=	generate_predictions(__lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase    )
						assert (
						    len(__lowerCAmelCase    ) == num_samples
						), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowerCAmelCase    )}'''
def     A__      (					__lowerCAmelCase		:      bool = False ,     __lowerCAmelCase		:      bool = False    ):
						lowerCamelCase__								=	evaluate.load("""glue""" ,     """mrpc"""    )
						lowerCamelCase__							,      lowerCamelCase__								=	get_mrpc_setup(__lowerCAmelCase ,     __lowerCAmelCase    )
						# First do baseline
						lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	setup["""no"""]
						model.to(__lowerCAmelCase    )
						model.eval()
						for batch in dataloader:
												batch.to(__lowerCAmelCase    )
												with torch.inference_mode():
																		lowerCamelCase__								=	model(**__lowerCAmelCase    )
												lowerCamelCase__								=	outputs.logits.argmax(dim=-1    )
												metric.add_batch(predictions=__lowerCAmelCase ,     references=batch["""labels"""]    )
						lowerCamelCase__								=	metric.compute()

						# Then do distributed
						lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	setup["""ddp"""]
						model.eval()
						for batch in dataloader:
												with torch.inference_mode():
																		lowerCamelCase__								=	model(**__lowerCAmelCase    )
												lowerCamelCase__								=	outputs.logits.argmax(dim=-1    )
												lowerCamelCase__								=	batch["""labels"""]
												lowerCamelCase__							,      lowerCamelCase__								=	accelerator.gather_for_metrics((preds, references)    )
												metric.add_batch(predictions=__lowerCAmelCase ,     references=__lowerCAmelCase    )
						lowerCamelCase__								=	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     A__      (					):
						lowerCamelCase__								=	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]:
																		lowerCamelCase__								=	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**"""    )
						lowerCamelCase__								=	Accelerator()
						test_torch_metrics(__lowerCAmelCase ,     512    )
						accelerator.state._reset_state()
def     A__      (					__lowerCAmelCase		:      List[Any]    ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
							main()








import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

__A							:			List[Any]         =			"python tqdm regex requests packaging filelock numpy tokenizers".split()
if sys.version_info < (3, 7):
 pkgs_to_check_at_runtime.append("dataclasses")
if sys.version_info < (3, 8):
 pkgs_to_check_at_runtime.append("importlib_metadata")

for pkg in pkgs_to_check_at_runtime:
 if pkg in deps:
  if pkg == "tokenizers":
   # must be loaded here, or else tqdm check may fail
   from .utils import is_tokenizers_available

   if not is_tokenizers_available():
    continue  # not required, check version only if installed

  require_version_core(deps[pkg])
 else:
  raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=None				)							-> Union[str, Any]:




    """simple docstring"""




    require_version(deps[pkg]							,     _SCREAMING_SNAKE_CASE				)





