


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    :						int     =							'true'





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Tuple=82						,	UpperCAmelCase      :			List[str]=16      ):
      """simple docstring"""




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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Accelerator						,	UpperCAmelCase      :			List[Any]=False      ):
      """simple docstring"""




      __lowerCamelCase     :      str       						=						AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""      )
      __lowerCamelCase     :      Optional[int]       						=						load_dataset("""glue"""						,	"""mrpc"""						,	split="""validation"""      )

      def tokenize_function(UpperCAmelCase      :			Any      ):
            __lowerCamelCase     :      int       						=						tokenizer(examples["""sentence1"""]						,	examples["""sentence2"""]						,	truncation=UpperCAmelCase						,	max_length=UpperCAmelCase      )
            return outputs

      with accelerator.main_process_first():
            __lowerCamelCase     :      Tuple       						=						dataset.map(
                UpperCAmelCase						,	batched=UpperCAmelCase						,	remove_columns=["""idx""", """sentence1""", """sentence2"""]						,	)

      __lowerCamelCase     :      Optional[Any]       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Union[str, Any]      ):
      """simple docstring"""




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





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Dict      ):
      """simple docstring"""




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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Accelerator						,	UpperCAmelCase      :			Tuple=82						,	UpperCAmelCase      :			int=False						,	UpperCAmelCase      :			str=False						,	UpperCAmelCase      :			Union[str, Any]=16      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						get_basic_setup(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
      __lowerCamelCase						, __lowerCamelCase     :      int       						=						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      ):
      """simple docstring"""




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

      # Then do distributed
      __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      int       						=						setup["""ddp"""]
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  __lowerCamelCase     :      List[str]       						=						model(**UpperCAmelCase      )
            __lowerCamelCase     :      List[Any]       						=						outputs.logits.argmax(dim=-1      )
            __lowerCamelCase     :      List[Any]       						=						batch["""labels"""]
            __lowerCamelCase						, __lowerCamelCase     :      Union[str, Any]       						=						accelerator.gather_for_metrics((preds, references)      )
            metric.add_batch(predictions=UpperCAmelCase						,	references=UpperCAmelCase      )
      __lowerCamelCase     :      Dict       						=						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       (     ):
      """simple docstring"""




      __lowerCamelCase     :      str       						=						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]:
                  __lowerCamelCase     :      Optional[int]       						=						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**"""      )
      __lowerCamelCase     :      int       						=						Accelerator()
      test_torch_metrics(UpperCAmelCase						,	512      )
      accelerator.state._reset_state()





def   _UpperCAmelCase       (     UpperCAmelCase      :			int      ):
      """simple docstring"""




      main()


if __name__ == "__main__":
    main()







def   _UpperCAmelCase       (     UpperCAmelCase      :			list      ):
      """simple docstring"""




      __lowerCamelCase     :      Optional[int]       						=						len(UpperCAmelCase      )
      for _ in range(UpperCAmelCase      ):
            for i in range(_ % 2						,	arr_size - 1						,	2      ):
                  if arr[i + 1] < arr[i]:
                        __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						arr[i + 1], arr[i]

      return arr


if __name__ == "__main__":
    __UpperCamelCase    :						str     =							list(range(10, 0, -1))
    print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')



