

'''simple docstring'''

class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					str					,      lowercase_			:					Tuple					,      lowercase_			:					Optional[Any]	)						->     Optional[Any]:
      UpperCAmelCase	:     int						 =			name
      UpperCAmelCase	:     Optional[Any]						 =			val


     def __str__(		self			:					Tuple	)						->     List[str]:
      return f"""{self.__class__.__name__}({self.name}, {self.val})"""




     def __lt__(		self			:					Any					,      lowercase_			:					Union[str, Any]	)						->     int:
      return self.val < other.val




class 	A_			:






     '''simple docstring'''



     def __init__(		self			:					Optional[Any]					,      lowercase_			:					List[str]	)						->     Dict:
      UpperCAmelCase	:     Optional[Any]						 =			{}
      UpperCAmelCase	:     str						 =			{}
      UpperCAmelCase	:     Union[str, Any]						 =			self.build_heap(lowercase_	)


     def __getitem__(		self			:					List[Any]					,      lowercase_			:					List[Any]	)						->     str:
      return self.get_value(lowercase_	)


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					str	)						->     int:
      return (idx - 1) // 2


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					List[Any]	)						->     str:
      return idx * 2 + 1


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					List[Any]	)						->     str:
      return idx * 2 + 2


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					Any	)						->     List[Any]:
      return self.heap_dict[key]


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					int	)						->     Tuple:
      UpperCAmelCase	:     List[str]						 =			len(lowercase_	) - 1
      UpperCAmelCase	:     Optional[int]						 =			self.get_parent_idx(lowercase_	)

      for idx, i in enumerate(lowercase_	):
       UpperCAmelCase	:     Optional[Any]						 =			idx
       UpperCAmelCase	:     List[str]						 =			i.val

      for i in range(lowercase_					,      -1					,      -1	):
       self.sift_down(lowercase_					,      lowercase_	)
      return array


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					Optional[int]					,      lowercase_			:					Dict	)						->     str:
      while True:
       UpperCAmelCase	:     Union[str, Any]						 =			self.get_left_child_idx(lowercase_	)  # noqa: E741
       UpperCAmelCase	:     Union[str, Any]						 =			self.get_right_child_idx(lowercase_	)

       UpperCAmelCase	:     Optional[Any]						 =			idx
       if l < len(lowercase_	) and array[l] < array[idx]:
        UpperCAmelCase	:     Tuple						 =			l
       if r < len(lowercase_	) and array[r] < array[smallest]:
        UpperCAmelCase	:     Optional[Any]						 =			r

       if smallest != idx:
        UpperCAmelCase	:     str						 =			array[smallest], array[idx]
        (
            UpperCAmelCase			
        )	:     Union[str, Any]						 =			(
            self.idx_of_element[array[smallest]],
            self.idx_of_element[array[idx]],
        )
        UpperCAmelCase	:     Dict						 =			smallest
       else:
        break


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					List[str]	)						->     Any:
      UpperCAmelCase	:     Optional[int]						 =			self.get_parent_idx(lowercase_	)
      while p >= 0 and self.heap[p] > self.heap[idx]:
       UpperCAmelCase	:     Dict						 =			self.heap[idx], self.heap[p]
       UpperCAmelCase	:     List[Any]						 =			(
           self.idx_of_element[self.heap[idx]],
           self.idx_of_element[self.heap[p]],
       )
       UpperCAmelCase	:     Any						 =			p
       UpperCAmelCase	:     Any						 =			self.get_parent_idx(lowercase_	)


     def        UpperCAmelCase_	(		self			:					int	)						->     List[Any]:
      return self.heap[0]


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Dict:
      UpperCAmelCase	:     Optional[int]						 =			self.heap[-1], self.heap[0]
      UpperCAmelCase	:     Tuple						 =			(
          self.idx_of_element[self.heap[-1]],
          self.idx_of_element[self.heap[0]],
      )

      UpperCAmelCase	:     str						 =			self.heap.pop()
      del self.idx_of_element[x]
      self.sift_down(0					,      self.heap	)
      return x


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					Any	)						->     List[Any]:
      self.heap.append(lowercase_	)
      UpperCAmelCase	:     List[Any]						 =			len(self.heap	) - 1
      UpperCAmelCase	:     Dict						 =			node.val
      self.sift_up(len(self.heap	) - 1	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Dict:
      return len(self.heap	) == 0




     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					List[str]					,      lowercase_			:					str	)						->     int:
      assert (
          self.heap[self.idx_of_element[node]].val > new_value
      ), "newValue must be less that current value"
      UpperCAmelCase	:     Optional[int]						 =			new_value
      UpperCAmelCase	:     List[str]						 =			new_value
      self.sift_up(self.idx_of_element[node]	)



lowercase__         =      Node("R", -1)
lowercase__         =      Node("B", 6)
lowercase__         =      Node("A", 3)
lowercase__         =      Node("X", 1)
lowercase__         =      Node("E", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowercase__         =      MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
    print(i)

print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
    print(i)

if __name__ == "__main__":
    import doctest

    doctest.testmod()







'''simple docstring'''

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
      UpperCAmelCase	:     Any						 =			inspect.getfile(accelerate.test_utils	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_script.py']	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(
          mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_distributed_data_loop.py']	)
      UpperCAmelCase	:     List[Any]						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_ops.py']	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Optional[int]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     List[Any]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     int						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
      print(f"""Command: {cmd}"""	)
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      UpperCAmelCase	:     Optional[int]						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__	)]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)




     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[Any]:
      print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
      with patch_environment(omp_num_threads=1					,      cuda_visible_devices='0,1'	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


if __name__ == "__main__":
 lowercase__         =      Accelerator()
 lowercase__         =      (accelerator.state.process_index + 2, 10)
 lowercase__         =      torch.randint(0, 10, shape).to(accelerator.device)

 lowercase__         =      ""

 lowercase__         =      accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 lowercase__         =      accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 lowercase__         =      accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)




