
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__	:  Any      =   namedtuple('ExampleNamedTuple', 'a b c')






class UpperCAmelCase     (		unittest.TestCase     ):





   '''simple docstring'''


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						str	    = torch.randn(5    ,    2  )
      _A:						str	    = torch.device('''cuda'''  ) if torch.cuda.is_available() else torch.device('''cpu'''  )

      _A:						List[Any]	    = send_to_device(lowerCAmelCase_    ,    lowerCAmelCase_  )
      self.assertTrue(torch.equal(resulta.cpu()    ,    lowerCAmelCase_  )  )

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

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

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

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


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      with self.assertRaises(lowerCAmelCase_  ) as cm:
         _A:						Tuple	    = recursively_apply(torch.tensor    ,    (torch.tensor(1  ), 1)    ,    error_on_other_type=lowerCAmelCase_  )
      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 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      _A:						Any	    = torch.tensor([1, 2, 3, 4, 5]  )
      self.assertEqual(listify(lowerCAmelCase_  )    ,    [1, 2, 3, 4, 5]  )

      _A:						Optional[int]	    = torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]  )
      self.assertEqual(listify(lowerCAmelCase_  )    ,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]  )

      _A:						Tuple	    = torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]  )
      self.assertEqual(
          listify(lowerCAmelCase_  )    ,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]  )


   def 				__magic_name__       (      self  :							str  ):

      """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 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      _A:						Dict	    = RegressionModel()
      _A:						Any	    = model.forward
      _A:						Optional[Any]	    = convert_outputs_to_fpaa(model.forward  )
      _A:						int	    = extract_model_from_parallel(lowerCAmelCase_    ,    keep_fpaa_wrapper=lowerCAmelCase_  )
      _A:						Any	    = pickle.dumps(lowerCAmelCase_  )


   @require_cuda
   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						Dict	    = RegressionModel()
      _A:						Tuple	    = model.forward
      _A:						int	    = torch.cuda.amp.autocast(dtype=torch.floataa  )(model.forward  )
      _A:						Dict	    = convert_outputs_to_fpaa(model.forward  )
      _A:						str	    = extract_model_from_parallel(lowerCAmelCase_    ,    keep_fpaa_wrapper=lowerCAmelCase_  )
      _A:						List[str]	    = pickle.dumps(lowerCAmelCase_  )


   @require_cuda
   @require_torch_min_version(version='''2.0'''  )
   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      _A:						List[Any]	    = RegressionModel()
      _A:						Dict	    = model.forward
      _A:						Optional[int]	    = torch.cuda.amp.autocast(dtype=torch.floataa  )(model.forward  )
      _A:						Optional[int]	    = convert_outputs_to_fpaa(model.forward  )
      _A:						List[Any]	    = torch.compile(model.forward    ,    backend='''inductor'''  )
      _A:						List[str]	    = torch.randn(4    ,    1_0  ).cuda()
      _A:						List[Any]	    = model(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      _A:						int	    = RegressionModel()
      # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
      _A:						Optional[Any]	    = torch.nn.parallel.DataParallel(lowerCAmelCase_  )
      _A:						List[str]	    = extract_model_from_parallel(lowerCAmelCase_  )

      self.assertEqual(lowerCAmelCase_    ,    lowerCAmelCase_  )


   @require_torch_min_version(version='''2.0'''  )
   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = RegressionModel()
      _A:						str	    = torch.compile(lowerCAmelCase_  )

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

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


   def 				__magic_name__       (      self  :							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''']  )  )





from __future__ import annotations






def 						lowerCamelCase__							(      a	,	a   )   ->     Union[str, Any]:
   _A:						Any	    = 0
   _A:						str	    = len(a   ) - 1
   while left <= right:
      _A:						Any	    = (left + right) // 2
      if left == right:
         if sorted_list[middle] < item:
            _A:						Union[str, Any]	    = middle + 1
         break
      elif sorted_list[middle] < item:
         _A:						Optional[Any]	    = middle + 1
      else:
         _A:						str	    = middle - 1
   sorted_list.insert(a	,	a   )
   return sorted_list






def 						lowerCamelCase__							(      a	,	a   )   ->     int:
   _A:						List[Any]	    = []
   while left and right:
      if left[0][0] < right[0][0]:
         result.append(left.pop(0   )   )
      else:
         result.append(right.pop(0   )   )
   return result + left + right






def 						lowerCamelCase__							(      a   )   ->     List[Any]:
   _A:						Dict	    = len(a   )
   if length <= 1:
      return list_ad
   _A:						str	    = length // 2
   return merge(sortlist_ad(list_ad[:middle]   )	,	sortlist_ad(list_ad[middle:]   )   )






def 						lowerCamelCase__							(      a   )   ->     list[int]:

   if len(a   ) <= 1:
      return collection
   _A:						str	    = []
   _A:						Tuple	    = False
   for i in range(0	,	len(a   )	,	2   ):
      if i == len(a   ) - 1:
         _A:						Dict	    = True
      else:
         if collection[i] < collection[i + 1]:
            two_paired_list.append([collection[i], collection[i + 1]]   )
         else:
            two_paired_list.append([collection[i + 1], collection[i]]   )
   _A:						List[Any]	    = sortlist_ad(a   )
   _A:						Union[str, Any]	    = [i[0] for i in sorted_list_ad]
   result.append(sorted_list_ad[-1][1]   )
   if has_last_odd_item:
      _A:						List[Any]	    = collection[-1]
      _A:						Optional[Any]	    = binary_search_insertion(a	,	a   )
   _A:						Optional[int]	    = False
   for i in range(len(a   ) - 1   ):
      if result[i] == collection[-1] and has_last_odd_item:
         _A:						Tuple	    = True
      _A:						Optional[Any]	    = sorted_list_ad[i][1]
      # If last_odd_item is inserted before the item's index,
      # you should forward index one more.
      if is_last_odd_item_inserted_before_this_index:
         _A:						int	    = result[: i + 2] + binary_search_insertion(result[i + 2 :]	,	a   )
      else:
         _A:						Union[str, Any]	    = result[: i + 1] + binary_search_insertion(result[i + 1 :]	,	a   )

   return result


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



