


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__    :List[Any]       =				"true"

def 			lowerCAmelCase__					(     a__:						Optional[Any]     ,    a__:						Tuple=8_2     ,    a__:						Dict=1_6			)    ->						str:


							'''simple docstring'''





							set_seed(4_2			)
							_UpperCAmelCase											=     RegressionModel()
							_UpperCAmelCase											=     deepcopy(A__			)
							_UpperCAmelCase											=     RegressionDataset(length=A__			)
							_UpperCAmelCase											=     DataLoader(A__     ,    batch_size=A__			)
							model.to(accelerator.device			)
							_UpperCAmelCase						,			_UpperCAmelCase											=     accelerator.prepare(A__     ,    A__			)
							return model, ddp_model, dataloader

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						str=False			)    ->						Optional[int]:


							'''simple docstring'''





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

							def tokenize_function(a__:						Union[str, Any]			):
														_UpperCAmelCase											=     tokenizer(examples['sentence1']     ,    examples['sentence2']     ,    truncation=A__     ,    max_length=A__			)
														return outputs

							with accelerator.main_process_first():
														_UpperCAmelCase											=     dataset.map(
														    A__     ,    batched=A__     ,    remove_columns=['idx', 'sentence1', 'sentence2']     ,    )

							_UpperCAmelCase											=     tokenized_datasets.rename_column('label'     ,    'labels'			)

							def collate_fn(a__:						Optional[int]			):
														if use_longest:
																					return tokenizer.pad(A__     ,    padding='longest'     ,    return_tensors='pt'			)
														return tokenizer.pad(A__     ,    padding='max_length'     ,    max_length=1_2_8     ,    return_tensors='pt'			)

							return DataLoader(A__     ,    shuffle=A__     ,    collate_fn=A__     ,    batch_size=1_6			)

def 			lowerCAmelCase__					(     a__:						Dict     ,    a__:						int			)    ->						Tuple:


							'''simple docstring'''





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

def 			lowerCAmelCase__					(     a__:						Optional[Any]     ,    a__:						Optional[int]     ,    a__:						Dict			)    ->						Optional[Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     []
							for batch in dataloader:
														_UpperCAmelCase						,			_UpperCAmelCase											=     batch.values()
														with torch.no_grad():
																					_UpperCAmelCase											=     model(A__			)
																					_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(A__			)
														targs.append(A__			)
							_UpperCAmelCase						,			_UpperCAmelCase											=     torch.cat(A__			), torch.cat(A__			)
							return logits, targs

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						Tuple=8_2     ,    a__:						int=False     ,    a__:						Tuple=False     ,    a__:						Optional[int]=1_6			)    ->						Any:


							'''simple docstring'''





							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     get_basic_setup(A__     ,    A__     ,    A__			)
							_UpperCAmelCase						,			_UpperCAmelCase											=     generate_predictions(A__     ,    A__     ,    A__			)
							assert (
							    len(A__			) == num_samples
							), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A__			)}'''

def 			lowerCAmelCase__					(     a__:						List[Any] = False     ,    a__:						int = False			)    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     evaluate.load('glue'     ,    'mrpc'			)
							_UpperCAmelCase						,			_UpperCAmelCase											=     get_mrpc_setup(A__     ,    A__			)
							# First do baseline
							_UpperCAmelCase						,			_UpperCAmelCase						,			_UpperCAmelCase											=     setup['no']
							model.to(A__			)
							model.eval()
							for batch in dataloader:
														batch.to(A__			)
														with torch.inference_mode():
																					_UpperCAmelCase											=     model(**A__			)
														_UpperCAmelCase											=     outputs.logits.argmax(dim=-1			)
														metric.add_batch(predictions=A__     ,    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(**A__			)
														_UpperCAmelCase											=     outputs.logits.argmax(dim=-1			)
														_UpperCAmelCase											=     batch['labels']
														_UpperCAmelCase						,			_UpperCAmelCase											=     accelerator.gather_for_metrics((preds, references)			)
														metric.add_batch(predictions=A__     ,    references=A__			)
							_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 			lowerCAmelCase__					(     )    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     Accelerator(split_batches=A__     ,    dispatch_batches=A__			)
							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(A__     ,    A__			)
																												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=A__     ,    dispatch_batches=A__			)
																					if accelerator.is_local_main_process:
																												print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''			)
																					test_torch_metrics(A__     ,    9_9			)
																					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(A__     ,    5_1_2			)
							accelerator.state._reset_state()

def 			lowerCAmelCase__					(     a__:						Tuple			)    ->						List[str]:


							'''simple docstring'''





							main()


if __name__ == "__main__":
		main()





def 	_lowerCAmelCase (    A__ = 600_851_475_143						):

      try:
            lowercase__  =	int(A__						)
      except (TypeError, ValueError):
            raise TypeError('Parameter n must be int or castable to int.'						)
      if n <= 0:
            raise ValueError('Parameter n must be greater than or equal to one.'						)
      lowercase__  =	2
      lowercase__  =	0
      if n == 2:
            return 2
      while n > 2:
            while n % i != 0:
                  i += 1
            lowercase__  =	i
            while n % i == 0:
                  lowercase__  =	n // i
            i += 1
      return int(A__						)


if __name__ == "__main__":
       print(F'''{solution() = }''')
