



"""simple docstring"""
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     =			"""true"""





def      __magic_name__				(							lowercase							,			lowercase=82							,			lowercase=16 ):
   set_seed(42 )
   SCREAMING_SNAKE_CASE_: List[str]     			=RegressionModel()
   SCREAMING_SNAKE_CASE_: Dict     			=deepcopy(lowercase )
   SCREAMING_SNAKE_CASE_: str     			=RegressionDataset(length=lowercase )
   SCREAMING_SNAKE_CASE_: Dict     			=DataLoader(lowercase							,			batch_size=lowercase )
   model.to(accelerator.device )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=accelerator.prepare(lowercase							,			lowercase )
   return model, ddp_model, dataloader





def      __magic_name__				(							lowercase							,			lowercase=False ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased""" )
   SCREAMING_SNAKE_CASE_: List[str]     			=load_dataset("""glue"""							,			"""mrpc"""							,			split="""validation""" )

   def tokenize_function(lowercase ):
      SCREAMING_SNAKE_CASE_: int     			=tokenizer(examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=lowercase							,			max_length=lowercase )
      return outputs

   with accelerator.main_process_first():
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=dataset.map(
          lowercase							,			batched=lowercase							,			remove_columns=["""idx""", """sentence1""", """sentence2"""]							,			)

   SCREAMING_SNAKE_CASE_: Dict     			=tokenized_datasets.rename_column("""label"""							,			"""labels""" )

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

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





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





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=[]
   for batch in dataloader:
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=batch.values()
      with torch.no_grad():
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model(lowercase )
         SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=accelerator.gather_for_metrics((logit, target) )
         logits_and_targets.append((logit, target) )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Any     			=[], []
   for logit, targ in logits_and_targets:
      logits.append(lowercase )
      targs.append(lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=torch.cat(lowercase ), torch.cat(lowercase )
   return logits, targs





def      __magic_name__				(							lowercase							,			lowercase=82							,			lowercase=False							,			lowercase=False							,			lowercase=16 ):
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=get_basic_setup(lowercase							,			lowercase							,			lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: int     			=generate_predictions(lowercase							,			lowercase							,			lowercase )
   assert (
       len(lowercase ) == num_samples
   ), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowercase )}'''





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

   # Then do distributed
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=setup["""ddp"""]
   model.eval()
   for batch in dataloader:
      with torch.inference_mode():
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=model(**lowercase )
      SCREAMING_SNAKE_CASE_: Any     			=outputs.logits.argmax(dim=-1 )
      SCREAMING_SNAKE_CASE_: Dict     			=batch["""labels"""]
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=accelerator.gather_for_metrics((preds, references) )
      metric.add_batch(predictions=lowercase							,			references=lowercase )
   SCREAMING_SNAKE_CASE_: str     			=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      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=Accelerator(split_batches=lowercase							,			dispatch_batches=lowercase )
   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(lowercase							,			lowercase )
            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]:
         SCREAMING_SNAKE_CASE_: Dict     			=Accelerator(split_batches=lowercase							,			dispatch_batches=lowercase )
         if accelerator.is_local_main_process:
            print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
         test_torch_metrics(lowercase							,			99 )
         accelerator.state._reset_state()
   if accelerator.is_local_main_process:
      print("""**Test last batch is not dropped when perfectly divisible**""" )
   SCREAMING_SNAKE_CASE_: List[str]     			=Accelerator()
   test_torch_metrics(lowercase							,			512 )
   accelerator.state._reset_state()





def      __magic_name__				(							lowercase ):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			=False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      SCREAMING_SNAKE_CASE_: Tuple     			=True
      for i in range(0							,			len(lowercase ) - 1							,			2 ):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=input_list[i + 1], input_list[i]
            # swapping if elements not in order
            SCREAMING_SNAKE_CASE_: Tuple     			=False

      for i in range(1							,			len(lowercase ) - 1							,			2 ):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=input_list[i + 1], input_list[i]
            # swapping if elements not in order
            SCREAMING_SNAKE_CASE_: str     			=False
   return input_list


if __name__ == "__main__":
   print("""Enter list to be sorted""")
   _UpperCAmelCase     =			[int(x) for x in input().split()]
   # inputing elements of the list in one line
   _UpperCAmelCase     =			odd_even_sort(input_list)
   print("""The sorted list is""")
   print(sorted_list)



