

'''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 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     Any=82   , __magic_name__						:     Any=16						)				->  Optional[int]:
						set_seed(42						)
						lowercase :							Any    		=RegressionModel()
						lowercase :							Dict    		=deepcopy(__magic_name__						)
						lowercase :							List[Any]    		=RegressionDataset(length=__magic_name__						)
						lowercase :							Optional[int]    		=DataLoader(__magic_name__   , batch_size=__magic_name__						)
						model.to(accelerator.device						)
						lowercase		,	lowercase :							List[str]    		=accelerator.prepare(__magic_name__   , __magic_name__						)
						return model, ddp_model, dataloader






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

						def tokenize_function(__magic_name__						:     List[str]						):
												lowercase :							Optional[Any]    		=tokenizer(examples['''sentence1''']   , examples['''sentence2''']   , truncation=__magic_name__   , max_length=__magic_name__						)
												return outputs

						with accelerator.main_process_first():
												lowercase :							List[str]    		=dataset.map(
												    __magic_name__   , batched=__magic_name__   , remove_columns=['''idx''', '''sentence1''', '''sentence2''']   , )

						lowercase :							List[Any]    		=tokenized_datasets.rename_column('''label'''   , '''labels'''						)

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

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






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






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






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






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

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






def 				_lowerCAmelCase			(   __magic_name__						:     str						)				->  Tuple:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
							main()







"""simple docstring"""

def      lowercase__    (       lowerCAmelCase					:       str			)			->   bool:






    """simple docstring"""




    UpperCAmelCase          =       [int(lowerCAmelCase			) for i in ip_va_address.split('.'			) if i.isdigit()]
    return len(lowerCAmelCase			) == 4 and all(0 <= int(lowerCAmelCase			) <= 254 for octet in octets			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    input().strip()
   SCREAMING_SNAKE_CASE_            =    '''valid''' if is_ip_va_address_valid(ip) else '''invalid'''
   print(F'{ip} is a {valid_or_invalid} IP v4 address.')



