
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,
)


__snake_case					:							str           =							namedtuple('ExampleNamedTuple', 'a b c')


class 	UpperCamelCase__       (  unittest.TestCase):

						'''simple docstring'''



						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:str 		=    torch.randn(5  , 2	)
									UpperCAmelCase__		:str 		=    torch.device('cuda'	) if torch.cuda.is_available() else torch.device('cpu'	)

									UpperCAmelCase__		:Union[str, Any] 		=    send_to_device(lowercase_  , lowercase_	)
									self.assertTrue(torch.equal(resulta.cpu()  , lowercase_	)	)

									UpperCAmelCase__		:Tuple 		=    send_to_device((tensor, [tensor, tensor], 1)  , lowercase_	)
									self.assertIsInstance(lowercase_  , lowercase_	)
									self.assertTrue(torch.equal(resulta[0].cpu()  , lowercase_	)	)
									self.assertIsInstance(resulta[1]  , lowercase_	)
									self.assertTrue(torch.equal(resulta[1][0].cpu()  , lowercase_	)	)
									self.assertTrue(torch.equal(resulta[1][1].cpu()  , lowercase_	)	)
									self.assertEqual(resulta[2]  , 1	)

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

									UpperCAmelCase__		:List[Any] 		=    send_to_device(ExampleNamedTuple(a=lowercase_  , b=[tensor, tensor]  , c=1	)  , lowercase_	)
									self.assertIsInstance(lowercase_  , lowercase_	)
									self.assertTrue(torch.equal(resulta.a.cpu()  , lowercase_	)	)
									self.assertIsInstance(resulta.b  , lowercase_	)
									self.assertTrue(torch.equal(resulta.b[0].cpu()  , lowercase_	)	)
									self.assertTrue(torch.equal(resulta.b[1].cpu()  , lowercase_	)	)
									self.assertEqual(resulta.c  , 1	)

									UpperCAmelCase__		:Tuple 		=    send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}	)  , lowercase_	)
									self.assertIsInstance(lowercase_  , lowercase_	)
									self.assertTrue(torch.equal(resulta['a'].cpu()  , lowercase_	)	)
									self.assertIsInstance(resulta['b']  , lowercase_	)
									self.assertTrue(torch.equal(resulta['b'][0].cpu()  , lowercase_	)	)
									self.assertTrue(torch.equal(resulta['b'][1].cpu()  , lowercase_	)	)
									self.assertEqual(resulta['c']  , 1	)


						def    A__       (  self	)  ->Tuple:
									with self.assertRaises(lowercase_	) as cm:
												UpperCAmelCase__		:str 		=    recursively_apply(torch.tensor  , (torch.tensor(1	), 1)  , error_on_other_type=lowercase_	)
									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    A__       (  self	)  ->Tuple:
									UpperCAmelCase__		:Dict 		=    torch.tensor([1, 2, 3, 4, 5]	)
									self.assertEqual(listify(lowercase_	)  , [1, 2, 3, 4, 5]	)

									UpperCAmelCase__		:str 		=    torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]	)
									self.assertEqual(listify(lowercase_	)  , [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]	)

									UpperCAmelCase__		:Tuple 		=    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(lowercase_	)  , [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]	)


						def    A__       (  self	)  ->List[Any]:
									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    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		:Tuple 		=    RegressionModel()
									UpperCAmelCase__		:Optional[Any] 		=    model.forward
									UpperCAmelCase__		:int 		=    convert_outputs_to_fpaa(model.forward	)
									UpperCAmelCase__		:Optional[int] 		=    extract_model_from_parallel(lowercase_  , keep_fpaa_wrapper=lowercase_	)
									UpperCAmelCase__		:List[Any] 		=    pickle.dumps(lowercase_	)


						@require_cuda
						def    A__       (  self	)  ->List[str]:
									UpperCAmelCase__		:int 		=    RegressionModel()
									UpperCAmelCase__		:str 		=    model.forward
									UpperCAmelCase__		:Tuple 		=    torch.cuda.amp.autocast(dtype=torch.floataa	)(model.forward	)
									UpperCAmelCase__		:List[Any] 		=    convert_outputs_to_fpaa(model.forward	)
									UpperCAmelCase__		:Optional[Any] 		=    extract_model_from_parallel(lowercase_  , keep_fpaa_wrapper=lowercase_	)
									UpperCAmelCase__		:str 		=    pickle.dumps(lowercase_	)


						@require_cuda
						@require_torch_min_version(version='2.0'	)
						def    A__       (  self	)  ->int:
									UpperCAmelCase__		:int 		=    RegressionModel()
									UpperCAmelCase__		:Union[str, Any] 		=    model.forward
									UpperCAmelCase__		:Dict 		=    torch.cuda.amp.autocast(dtype=torch.floataa	)(model.forward	)
									UpperCAmelCase__		:Optional[int] 		=    convert_outputs_to_fpaa(model.forward	)
									UpperCAmelCase__		:int 		=    torch.compile(model.forward  , backend='inductor'	)
									UpperCAmelCase__		:Union[str, Any] 		=    torch.randn(4  , 10	).cuda()
									UpperCAmelCase__		:Union[str, Any] 		=    model(lowercase_	)


						def    A__       (  self	)  ->Any:
									UpperCAmelCase__		:int 		=    RegressionModel()
									# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
									UpperCAmelCase__		:List[Any] 		=    torch.nn.parallel.DataParallel(lowercase_	)
									UpperCAmelCase__		:Union[str, Any] 		=    extract_model_from_parallel(lowercase_	)

									self.assertEqual(lowercase_  , lowercase_	)


						@require_torch_min_version(version='2.0'	)
						def    A__       (  self	)  ->int:
									UpperCAmelCase__		:Optional[Any] 		=    RegressionModel()
									UpperCAmelCase__		:Any 		=    torch.compile(lowercase_	)

									# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
									UpperCAmelCase__		:Optional[int] 		=    torch.nn.parallel.DataParallel(lowercase_	)
									UpperCAmelCase__		:int 		=    torch.compile(lowercase_	)
									UpperCAmelCase__		:int 		=    extract_model_from_parallel(lowercase_	)

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


						def    A__       (  self	)  ->Dict:
									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']	)	)







'''simple docstring'''
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if right <= left:
        return
    snake_case_			:     Dict        =    left
    snake_case_			:     Tuple        =    right
    snake_case_			:     List[str]        =    sorting[left]
    while i <= b:
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Union[str, Any]        =    sorting[i], sorting[a]
            a += 1
            i += 1
        elif sorting[i] > pivot:
            snake_case_      ,			snake_case_			:     List[str]        =    sorting[i], sorting[b]
            b -= 1
        else:
            i += 1
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		a - 1							)
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		b + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if left < right:
        snake_case_			:     Dict        =    lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		pivot_index - 1							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		pivot_index + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[Any]        =    sorting[right]
    snake_case_			:     Optional[int]        =    left
    for i in range(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Dict        =    sorting[i], sorting[store_index]
            store_index += 1
    snake_case_      ,			snake_case_			:     Optional[Any]        =    sorting[store_index], sorting[right]
    return store_index

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if len(__SCREAMING_SNAKE_CASE							) <= 1:
        return sorting
    return (
        three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
        + [i for i in sorting if i == sorting[0]]
        + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)

  a_           =    input("Enter numbers separated by a comma:\n").strip()
  a_           =    [int(item) for item in user_input.split(",")]
  quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
  print(unsorted)




