





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


a__:      List[Any]									=		'true'


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


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

       def tokenize_function(UpperCamelCase__			:       int  ):
              A__											=						tokenizer(examples['''sentence1'''] ,     examples['''sentence2'''] ,     truncation=UpperCamelCase__ ,     max_length=UpperCamelCase__  )
              return outputs

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

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

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

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


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


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


def 	UpperCamelCase__(       UpperCamelCase__			:       Accelerator ,     UpperCamelCase__			:       int=82 ,     UpperCamelCase__			:       Optional[int]=False ,     UpperCamelCase__			:       Optional[int]=False ,     UpperCamelCase__			:       int=16  )->Optional[int]:
       A__					,	A__					,	A__											=						get_basic_setup(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
       A__					,	A__											=						generate_predictions(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__  )
       assert (
           len(UpperCamelCase__  ) == num_samples
       ), f"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCamelCase__  )}"


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

       # Then do distributed
       A__					,	A__					,	A__											=						setup['''ddp''']
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     A__											=						model(**UpperCamelCase__  )
              A__											=						outputs.logits.argmax(dim=-1  )
              A__											=						batch['''labels''']
              A__					,	A__											=						accelerator.gather_for_metrics((preds, references)  )
              metric.add_batch(predictions=UpperCamelCase__ ,     references=UpperCamelCase__  )
       A__											=						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 	UpperCamelCase__(       )->Optional[Any]:
       A__											=						Accelerator(split_batches=UpperCamelCase__ ,     dispatch_batches=UpperCamelCase__  )
       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(UpperCamelCase__ ,     UpperCamelCase__  )
                            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__											=						Accelerator(split_batches=UpperCamelCase__ ,     dispatch_batches=UpperCamelCase__  )
                     if accelerator.is_local_main_process:
                            print(f"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"  )
                     test_torch_metrics(UpperCamelCase__ ,     99  )
                     accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print('''**Test last batch is not dropped when perfectly divisible**'''  )
       A__											=						Accelerator()
       test_torch_metrics(UpperCamelCase__ ,     5_12  )
       accelerator.state._reset_state()


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int]  )->Optional[int]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
       main()












class     SCREAMING_SNAKE_CASE__ :



 def __init__(							self,__lowerCamelCase ):
        A__											=						set_counts
        A__											=						max(__lowerCamelCase )
        A__											=						len(__lowerCamelCase )
        A__											=						[1] * num_sets
        A__											=						list(range(__lowerCamelCase ) )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						self.get_parent(__lowerCamelCase )
        A__											=						self.get_parent(__lowerCamelCase )

        if src_parent == dst_parent:
               return False

        if self.ranks[dst_parent] >= self.ranks[src_parent]:
               self.set_counts[dst_parent] += self.set_counts[src_parent]
               A__											=						0
               A__											=						dst_parent
               if self.ranks[dst_parent] == self.ranks[src_parent]:
                      self.ranks[dst_parent] += 1
               A__											=						self.set_counts[dst_parent]
        else:
               self.set_counts[src_parent] += self.set_counts[dst_parent]
               A__											=						0
               A__											=						src_parent
               A__											=						self.set_counts[src_parent]

        A__											=						max(self.max_set,__lowerCamelCase )
        return True




 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        if self.parents[disj_set] == disj_set:
               return disj_set
        A__											=						self.get_parent(self.parents[disj_set] )
        return self.parents[disj_set]





