





'''simple docstring'''

import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


UpperCAmelCase       :		int		=				namedtuple('ExampleNamedTuple', 'a b c')





class lowerCamelCase    (unittest.TestCase       ):







  def 					UpperCAmelCase_					(		self		)		->    Union[str, Any]:






        """simple docstring"""





        _snake_case		:						List[str]         =  torch.randn(5  ,  2		)
        _snake_case		:						Any         =  torch.device('''cuda'''		) if torch.cuda.is_available() else torch.device('''cpu'''		)

        _snake_case		:						Optional[int]         =  send_to_device(_A  ,  _A		)
        self.assertTrue(torch.equal(resulta.cpu()  ,  _A		)		)

        _snake_case		:						str         =  send_to_device((tensor, [tensor, tensor], 1)  ,  _A		)
        self.assertIsInstance(_A  ,  _A		)
        self.assertTrue(torch.equal(resulta[0].cpu()  ,  _A		)		)
        self.assertIsInstance(resulta[1]  ,  _A		)
        self.assertTrue(torch.equal(resulta[1][0].cpu()  ,  _A		)		)
        self.assertTrue(torch.equal(resulta[1][1].cpu()  ,  _A		)		)
        self.assertEqual(resulta[2]  ,  1		)

        _snake_case		:						Dict         =  send_to_device({'''a''': tensor, '''b''': [tensor, tensor], '''c''': 1}  ,  _A		)
        self.assertIsInstance(_A  ,  _A		)
        self.assertTrue(torch.equal(resulta['''a'''].cpu()  ,  _A		)		)
        self.assertIsInstance(resulta['''b''']  ,  _A		)
        self.assertTrue(torch.equal(resulta['''b'''][0].cpu()  ,  _A		)		)
        self.assertTrue(torch.equal(resulta['''b'''][1].cpu()  ,  _A		)		)
        self.assertEqual(resulta['''c''']  ,  1		)

        _snake_case		:						Dict         =  send_to_device(ExampleNamedTuple(a=_A  ,  b=[tensor, tensor]  ,  c=1		)  ,  _A		)
        self.assertIsInstance(_A  ,  _A		)
        self.assertTrue(torch.equal(resulta.a.cpu()  ,  _A		)		)
        self.assertIsInstance(resulta.b  ,  _A		)
        self.assertTrue(torch.equal(resulta.b[0].cpu()  ,  _A		)		)
        self.assertTrue(torch.equal(resulta.b[1].cpu()  ,  _A		)		)
        self.assertEqual(resulta.c  ,  1		)

        _snake_case		:						Optional[Any]         =  send_to_device(UserDict({'''a''': tensor, '''b''': [tensor, tensor], '''c''': 1}		)  ,  _A		)
        self.assertIsInstance(_A  ,  _A		)
        self.assertTrue(torch.equal(resulta['''a'''].cpu()  ,  _A		)		)
        self.assertIsInstance(resulta['''b''']  ,  _A		)
        self.assertTrue(torch.equal(resulta['''b'''][0].cpu()  ,  _A		)		)
        self.assertTrue(torch.equal(resulta['''b'''][1].cpu()  ,  _A		)		)
        self.assertEqual(resulta['''c''']  ,  1		)







  def 					UpperCAmelCase_					(		self		)		->    List[Any]:






        """simple docstring"""





        with self.assertRaises(_A		) as cm:
              _snake_case		:						int         =  recursively_apply(torch.tensor  ,  (torch.tensor(1		), 1)  ,  error_on_other_type=_A		)
        self.assertEqual(
            str(cm.exception		)  ,  '''Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'''  ,  )







  def 					UpperCAmelCase_					(		self		)		->    List[Any]:






        """simple docstring"""





        _snake_case		:						str         =  torch.tensor([1, 2, 3, 4, 5]		)
        self.assertEqual(listify(_A		)  ,  [1, 2, 3, 4, 5]		)

        _snake_case		:						Optional[int]         =  torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]		)
        self.assertEqual(listify(_A		)  ,  [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]		)

        _snake_case		:						List[Any]         =  torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]		)
        self.assertEqual(
            listify(_A		)  ,  [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]		)







  def 					UpperCAmelCase_					(		self		)		->    Optional[Any]:






        """simple docstring"""





        with patch_environment(aa=1  ,  BB=2		):
              self.assertEqual(os.environ.get('''AA'''		)  ,  '''1'''		)
              self.assertEqual(os.environ.get('''BB'''		)  ,  '''2'''		)

        self.assertNotIn('''AA'''  ,  os.environ		)
        self.assertNotIn('''BB'''  ,  os.environ		)







  def 					UpperCAmelCase_					(		self		)		->    Optional[Any]:






        """simple docstring"""





        _snake_case		:						Optional[Any]         =  RegressionModel()
        _snake_case		:						str         =  model.forward
        _snake_case		:						int         =  convert_outputs_to_fpaa(model.forward		)
        _snake_case		:						Tuple         =  extract_model_from_parallel(_A  ,  keep_fpaa_wrapper=_A		)
        _snake_case		:						Dict         =  pickle.dumps(_A		)







  @require_cuda
  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        _snake_case		:						Optional[Any]         =  RegressionModel()
        _snake_case		:						Optional[int]         =  model.forward
        _snake_case		:						Tuple         =  torch.cuda.amp.autocast(dtype=torch.floataa		)(model.forward		)
        _snake_case		:						Dict         =  convert_outputs_to_fpaa(model.forward		)
        _snake_case		:						Optional[int]         =  extract_model_from_parallel(_A  ,  keep_fpaa_wrapper=_A		)
        _snake_case		:						Optional[Any]         =  pickle.dumps(_A		)







  @require_cuda
  @require_torch_min_version(version='''2.0'''		)
  def 					UpperCAmelCase_					(		self		)		->    Tuple:






        """simple docstring"""





        _snake_case		:						int         =  RegressionModel()
        _snake_case		:						Tuple         =  model.forward
        _snake_case		:						int         =  torch.cuda.amp.autocast(dtype=torch.floataa		)(model.forward		)
        _snake_case		:						int         =  convert_outputs_to_fpaa(model.forward		)
        _snake_case		:						Tuple         =  torch.compile(model.forward  ,  backend='''inductor'''		)
        _snake_case		:						int         =  torch.randn(4  ,  10		).cuda()
        _snake_case		:						List[Any]         =  model(_A		)







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        _snake_case		:						Optional[Any]         =  RegressionModel()
        # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
        _snake_case		:						Dict         =  torch.nn.parallel.DataParallel(_A		)
        _snake_case		:						Dict         =  extract_model_from_parallel(_A		)

        self.assertEqual(_A  ,  _A		)







  @require_torch_min_version(version='''2.0'''		)
  def 					UpperCAmelCase_					(		self		)		->    int:






        """simple docstring"""





        _snake_case		:						Any         =  RegressionModel()
        _snake_case		:						int         =  torch.compile(_A		)

        # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
        _snake_case		:						Any         =  torch.nn.parallel.DataParallel(_A		)
        _snake_case		:						Dict         =  torch.compile(_A		)
        _snake_case		:						List[Any]         =  extract_model_from_parallel(_A		)

        self.assertEqual(compiled_model._orig_mod  ,  compiled_model_unwrapped._orig_mod		)







  def 					UpperCAmelCase_					(		self		)		->    List[str]:






        """simple docstring"""





        self.assertEqual(find_device([1, '''a''', torch.tensor([1, 2, 3]		)]		)  ,  torch.device('''cpu'''		)		)
        self.assertEqual(find_device({'''a''': 1, '''b''': torch.tensor([1, 2, 3]		)}		)  ,  torch.device('''cpu'''		)		)
        self.assertIsNone(find_device([1, '''a''']		)		)





import random


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[Any]							):





    """simple docstring"""


    def is_sorted(_SCREAMING_SNAKE_CASE       :		List[Any]							):
        for i in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
            if collection[i] > collection[i + 1]:
                return False
        return True

    while not is_sorted(_SCREAMING_SNAKE_CASE							):
        random.shuffle(_SCREAMING_SNAKE_CASE							)
    return collection


if __name__ == "__main__":
       UpperCamelCase__	:   Tuple								=     input("Enter numbers separated by a comma:\n").strip()
       UpperCamelCase__	:   List[str]								=     [int(item) for item in user_input.split(",")]
       print(bogo_sort(unsorted))


