

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    :Optional[Any]      =			'true'

def       __snake_case						( __UpperCamelCase	: Any				,__UpperCamelCase	: List[Any]=82				,__UpperCamelCase	: Dict=16    ):



      """simple docstring"""

      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       __snake_case						( __UpperCamelCase	: Accelerator				,__UpperCamelCase	: Dict=False    ):



      """simple docstring"""

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

      def tokenize_function(__UpperCamelCase	: Optional[Any]    ):
            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	: Union[str, Any]    ):
            if use_longest:
                  return tokenizer.pad(__UpperCamelCase				,padding="longest"				,return_tensors="pt"    )
            return tokenizer.pad(__UpperCamelCase				,padding="max_length"				,max_length=128				,return_tensors="pt"    )

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

def       __snake_case						( __UpperCamelCase	: Optional[Any]				,__UpperCamelCase	: str    ):



      """simple docstring"""

      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       __snake_case						( __UpperCamelCase	: Dict				,__UpperCamelCase	: int				,__UpperCamelCase	: Optional[Any]    ):



      """simple docstring"""

      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       __snake_case						( __UpperCamelCase	: Accelerator				,__UpperCamelCase	: Dict=82				,__UpperCamelCase	: List[Any]=False				,__UpperCamelCase	: Dict=False				,__UpperCamelCase	: Optional[int]=16    ):



      """simple docstring"""

      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       __snake_case						( __UpperCamelCase	: bool = False				,__UpperCamelCase	: bool = False    ):



      """simple docstring"""

      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       __snake_case						( ):



      """simple docstring"""

      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				,512    )
      accelerator.state._reset_state()

def       __snake_case						( __UpperCamelCase	: List[Any]    ):



      """simple docstring"""

      main()


if __name__ == "__main__":
      main()
"""simple docstring"""



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     List[Any]								=			[1]
							for i in range(2			,  lowerCAmelCase							):
														factorials.append(factorials[-1] * i							)
							assert 0 <= k < factorials[-1] * n, "k out of bounds"

							A__       :     Union[str, Any]								=			[]
							A__       :     int								=			list(range(lowerCAmelCase							)							)

							# Find permutation
							while factorials:
														A__       :     Optional[int]								=			factorials.pop()
														A__				,							A__       :     Union[str, Any]								=			divmod(lowerCAmelCase			,  lowerCAmelCase							)
														permutation.append(elements[number]							)
														elements.remove(elements[number]							)
							permutation.append(elements[0]							)

							return permutation


if __name__ == "__main__":
				import doctest

				doctest.testmod()





