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_     =	'''true'''



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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    int:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    load_dataset("""glue"""     ,	"""mrpc"""     ,	split="""validation"""  )

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

							with accelerator.main_process_first():
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    dataset.map(
														    __SCREAMING_SNAKE_CASE     ,	batched=__SCREAMING_SNAKE_CASE     ,	remove_columns=["""idx""", """sentence1""", """sentence2"""]     ,	)

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    tokenized_datasets.rename_column("""label"""     ,	"""labels"""  )

							def collate_fn(__SCREAMING_SNAKE_CASE  ):
														if use_longest:
																					return tokenizer.pad(__SCREAMING_SNAKE_CASE     ,	padding="""longest"""     ,	return_tensors="""pt"""  )
														return tokenizer.pad(__SCREAMING_SNAKE_CASE     ,	padding="""max_length"""     ,	max_length=128     ,	return_tensors="""pt"""  )

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



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



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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=82     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=16  )->    str:
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Tuple			  =    get_basic_setup(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    generate_predictions(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							assert (
							    len(__SCREAMING_SNAKE_CASE  ) == num_samples
							), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__SCREAMING_SNAKE_CASE  )}"""



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

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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Tuple:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
			main()



"""simple docstring"""





from decimal import Decimal, getcontext
from math import ceil, factorial



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    str:

							if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														raise TypeError("""Undefined for non-integers"""  )
							elif precision < 1:
														raise ValueError("""Undefined for non-natural numbers"""  )

							_SCREAMING_SNAKE_CASE		:						int			  =    precision
							_SCREAMING_SNAKE_CASE		:						Dict			  =    ceil(precision / 14  )
							_SCREAMING_SNAKE_CASE		:						int			  =    426_880 * Decimal(10_005  ).sqrt()
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    1
							_SCREAMING_SNAKE_CASE		:						str			  =    13_591_409
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    Decimal(__SCREAMING_SNAKE_CASE  )
							for k in range(1     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    factorial(6 * k  ) // (factorial(3 * k  ) * factorial(__SCREAMING_SNAKE_CASE  ) ** 3)
														linear_term += 545_140_134
														exponential_term *= -262_537_412_640_768_000
														partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
							return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
			lowerCAmelCase_     =	50
			print(F"The first {n} digits of pi is: {pi(n)}")


