


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					:       Tuple			    =     """true"""



def UpperCamelCase_				(				__a			,				__a=82			,				__a=16      )	->   List[Any]:
      set_seed(42      )
      a__	:  Any     =   RegressionModel()
      a__	:  Optional[Any]     =   deepcopy(__a      )
      a__	:  Dict     =   RegressionDataset(length=__a      )
      a__	:  List[str]     =   DataLoader(__a			,				batch_size=__a      )
      model.to(accelerator.device      )
      a__,	a__	:  int     =   accelerator.prepare(__a			,				__a      )
      return model, ddp_model, dataloader



def UpperCamelCase_				(				__a			,				__a=False      )	->   str:
      a__	:  Dict     =   AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"      )
      a__	:  str     =   load_dataset("glue"			,				"mrpc"			,				split="validation"      )

      def tokenize_function(__a      ):
            a__	:  List[str]     =   tokenizer(examples["sentence1"]			,				examples["sentence2"]			,				truncation=__a			,				max_length=__a      )
            return outputs

      with accelerator.main_process_first():
            a__	:  int     =   dataset.map(
                __a			,				batched=__a			,				remove_columns=["idx", "sentence1", "sentence2"]			,				)

      a__	:  Optional[Any]     =   tokenized_datasets.rename_column("label"			,				"labels"      )

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

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



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



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



def UpperCamelCase_				(				__a			,				__a=82			,				__a=False			,				__a=False			,				__a=16      )	->   Optional[int]:
      a__,	a__,	a__	:  Optional[Any]     =   get_basic_setup(__a			,				__a			,				__a      )
      a__,	a__	:  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 UpperCamelCase_				(				__a = False			,				__a = False      )	->   Optional[Any]:
      a__	:  Any     =   evaluate.load("glue"			,				"mrpc"      )
      a__,	a__	:  List[Any]     =   get_mrpc_setup(__a			,				__a      )
      # First do baseline
      a__,	a__,	a__	:  Any     =   setup["no"]
      model.to(__a      )
      model.eval()
      for batch in dataloader:
            batch.to(__a      )
            with torch.inference_mode():
                  a__	:  str     =   model(**__a      )
            a__	:  List[str]     =   outputs.logits.argmax(dim=-1      )
            metric.add_batch(predictions=__a			,				references=batch["labels"]      )
      a__	:  Optional[Any]     =   metric.compute()

      # Then do distributed
      a__,	a__,	a__	:  Any     =   setup["ddp"]
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  a__	:  Optional[Any]     =   model(**__a      )
            a__	:  Any     =   outputs.logits.argmax(dim=-1      )
            a__	:  str     =   batch["labels"]
            a__,	a__	:  Optional[int]     =   accelerator.gather_for_metrics((preds, references)      )
            metric.add_batch(predictions=__a			,				references=__a      )
      a__	:  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 UpperCamelCase_				(				)	->   Dict:
      a__	:  int     =   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__	:  Dict     =   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__	:  Optional[Any]     =   Accelerator()
      test_torch_metrics(__a			,				512      )
      accelerator.state._reset_state()



def UpperCamelCase_				(				__a      )	->   Dict:
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
    main()








def   __lowerCamelCase   (				__a  :int       )						->							Dict:






		"""simple docstring"""

		A__     =  len(__a       )
		A__     =  sum(__a       )

		A__     =  [[False for x in range(s + 1       )] for y in range(n + 1       )]

		for i in range(1       ,	n + 1       ):
				A__     =  True

		for i in range(1       ,	s + 1       ):
				A__     =  False

		for i in range(1       ,	n + 1       ):
				for j in range(1       ,	s + 1       ):
						A__     =  dp[i][j - 1]

						if arr[i - 1] <= j:
								A__     =  dp[i][j] or dp[i - 1][j - arr[i - 1]]

		for j in range(int(s / 2       )       ,	-1       ,	-1       ):
				if dp[n][j] is True:
						A__     =  s - 2 * j
						break

		return diff



