


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_				:			Union[str, Any]   =	'true'







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      Any=82  ,   __A			:      Dict=16    )     ->		Dict:

       """simple docstring"""

       set_seed(42    )
       a_ :		List[str]							=  RegressionModel()
       a_ :		List[str]							=  deepcopy(__A    )
       a_ :		Optional[int]							=  RegressionDataset(length=__A    )
       a_ :		Any							=  DataLoader(__A  ,   batch_size=__A    )
       model.to(accelerator.device    )
       a_							,		a_ :		Union[str, Any]							=  accelerator.prepare(__A  ,   __A    )
       return model, ddp_model, dataloader







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Accelerator  ,   __A			:      Optional[int]=False    )     ->		Tuple:

       """simple docstring"""

       a_ :		List[Any]							=  AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'    )
       a_ :		Optional[Any]							=  load_dataset('glue'  ,   'mrpc'  ,   split='validation'    )

       def tokenize_function(__A			:      Any    ):
              a_ :		List[Any]							=  tokenizer(examples['sentence1']  ,   examples['sentence2']  ,   truncation=__A  ,   max_length=__A    )
              return outputs

       with accelerator.main_process_first():
              a_ :		Optional[int]							=  dataset.map(
                  __A  ,   batched=__A  ,   remove_columns=['idx', 'sentence1', 'sentence2']  ,   )

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

       def collate_fn(__A			:      Any    ):
              if use_longest:
                     return tokenizer.pad(__A  ,   padding='longest'  ,   return_tensors='pt'    )
              return tokenizer.pad(__A  ,   padding='max_length'  ,   max_length=1_28  ,   return_tensors='pt'    )

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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[Any]  ,   __A			:      int    )     ->		List[str]:

       """simple docstring"""

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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      Tuple  ,   __A			:      Union[str, Any]    )     ->		Any:

       """simple docstring"""

       a_ :		int							=  []
       for batch in dataloader:
              a_							,		a_ :		List[str]							=  batch.values()
              with torch.no_grad():
                     a_ :		Tuple							=  model(__A    )
                     a_							,		a_ :		List[Any]							=  accelerator.gather_for_metrics((logit, target)    )
                     logits_and_targets.append((logit, target)    )
       a_							,		a_ :		Union[str, Any]							=  [], []
       for logit, targ in logits_and_targets:
              logits.append(__A    )
              targs.append(__A    )
       a_							,		a_ :		Optional[Any]							=  torch.cat(__A    ), torch.cat(__A    )
       return logits, targs







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Accelerator  ,   __A			:      List[Any]=82  ,   __A			:      Tuple=False  ,   __A			:      Any=False  ,   __A			:      Tuple=16    )     ->		Any:

       """simple docstring"""

       a_							,		a_							,		a_ :		Optional[Any]							=  get_basic_setup(__A  ,   __A  ,   __A    )
       a_							,		a_ :		Optional[int]							=  generate_predictions(__A  ,   __A  ,   __A    )
       assert (
           len(__A    ) == num_samples
       ), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__A    )}"""







def 	SCREAMING_SNAKE_CASE_						(      __A			:      bool = False  ,   __A			:      bool = False    )     ->		Union[str, Any]:

       """simple docstring"""

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

       # Then do distributed
       a_							,		a_							,		a_ :		List[Any]							=  setup['ddp']
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     a_ :		List[str]							=  model(**__A    )
              a_ :		str							=  outputs.logits.argmax(dim=-1    )
              a_ :		Optional[int]							=  batch['labels']
              a_							,		a_ :		str							=  accelerator.gather_for_metrics((preds, references)    )
              metric.add_batch(predictions=__A  ,   references=__A    )
       a_ :		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 	SCREAMING_SNAKE_CASE_						(      )     ->		Dict:

       """simple docstring"""

       a_ :		Tuple							=  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]:
                     a_ :		int							=  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  ,   99    )
                     accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print('**Test last batch is not dropped when perfectly divisible**'    )
       a_ :		Any							=  Accelerator()
       test_torch_metrics(__A  ,   5_12    )
       accelerator.state._reset_state()







def 	SCREAMING_SNAKE_CASE_						(      __A			:      str    )     ->		Union[str, Any]:

       """simple docstring"""

       main()


if __name__ == "__main__":
   main()






def 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		int:

       """simple docstring"""

       if not isinstance(__A  ,   __A    ):
              raise ValueError('Input must be an integer'    )
       if input_num <= 0:
              raise ValueError('Input must be positive'    )
       return sum(
           divisor for divisor in range(1  ,   input_num // 2 + 1    ) if input_num % divisor == 0    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


