






"""simple docstring"""






class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       name
					A__							 =       val


	def __str__(      self						):


					"""simple docstring"""

					return f'''{self.__class__.__name__}({self.name}, {self.val})'''






	def __lt__(      self  ,					__a						):


					"""simple docstring"""

					return self.val < other.val





class       snake_case_       :



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       {}
					A__							 =       {}
					A__							 =       self.build_heap(__a						)


	def __getitem__(      self  ,					__a						):


					"""simple docstring"""

					return self.get_value(__a						)


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return (idx - 1) // 2


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return idx * 2 + 1


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return idx * 2 + 2


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return self.heap_dict[key]


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       len(__a						) - 1
					A__							 =       self.get_parent_idx(__a						)

					for idx, i in enumerate(__a						):
									A__							 =       idx
									A__							 =       i.val

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					while True:
									A__							 =       self.get_left_child_idx(__a						)  # noqa: E741
									A__							 =       self.get_right_child_idx(__a						)

									A__							 =       idx
									if l < len(__a						) and array[l] < array[idx]:
													A__							 =       l
									if r < len(__a						) and array[r] < array[smallest]:
													A__							 =       r

									if smallest != idx:
													A__ ,			A__							 =       array[smallest], array[idx]
													(
													    (
													    A__
													) ,			(
													    A__
													) ,			
													)							 =       (
													    self.idx_of_element[array[smallest]],
													    self.idx_of_element[array[idx]],
													)
													A__							 =       smallest
									else:
													break


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       self.get_parent_idx(__a						)
					while p >= 0 and self.heap[p] > self.heap[idx]:
									A__ ,			A__							 =       self.heap[idx], self.heap[p]
									A__ ,			A__							 =       (
									    self.idx_of_element[self.heap[idx]],
									    self.idx_of_element[self.heap[p]],
									)
									A__							 =       p
									A__							 =       self.get_parent_idx(__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return self.heap[0]


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__ ,			A__							 =       self.heap[-1], self.heap[0]
					A__ ,			A__							 =       (
					    self.idx_of_element[self.heap[-1]],
					    self.idx_of_element[self.heap[0]],
					)

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


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					self.heap.append(__a						)
					A__							 =       len(self.heap						) - 1
					A__							 =       node.val
					self.sift_up(len(self.heap						) - 1						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return len(self.heap						) == 0






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					assert (
					    self.heap[self.idx_of_element[node]].val > new_value
					), "newValue must be less that current value"
					A__							 =       new_value
					A__							 =       new_value
					self.sift_up(self.idx_of_element[node]						)





SCREAMING_SNAKE_CASE						:						List[str]					   =       Node('''R''', -1)
SCREAMING_SNAKE_CASE						:						Tuple					   =       Node('''B''', 6)
SCREAMING_SNAKE_CASE						:						List[Any]					   =       Node('''A''', 3)
SCREAMING_SNAKE_CASE						:						int					   =       Node('''X''', 1)
SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       Node('''E''', 4)

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

# Generating Min-Heap from array
SCREAMING_SNAKE_CASE						:						str					   =       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"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE						:						Dict					   =       '''Create a default config file for Accelerate with only a few flags set.'''



def  __lowerCamelCase					(					lowerCAmelCase__="no"       ,lowerCAmelCase__ = default_json_config_file       ,lowerCAmelCase__ = False				):
				A__							 =       Path(lowerCAmelCase__				)
				path.parent.mkdir(parents=lowerCAmelCase__       ,exist_ok=lowerCAmelCase__				)
				if path.exists():
								print(
								    f'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
								return False
				A__							 =       mixed_precision.lower()
				if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
								raise ValueError(
								    f'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
				A__							 =       {
				    'compute_environment': 'LOCAL_MACHINE',
				    'mixed_precision': mixed_precision,
				}
				if torch.cuda.is_available():
								A__							 =       torch.cuda.device_count()
								A__							 =       num_gpus
								A__							 =       False
								if num_gpus > 1:
												A__							 =       'MULTI_GPU'
								else:
												A__							 =       'NO'
				elif is_xpu_available() and use_xpu:
								A__							 =       torch.xpu.device_count()
								A__							 =       num_xpus
								A__							 =       False
								if num_xpus > 1:
												A__							 =       'MULTI_XPU'
								else:
												A__							 =       'NO'
				elif is_npu_available():
								A__							 =       torch.npu.device_count()
								A__							 =       num_npus
								A__							 =       False
								if num_npus > 1:
												A__							 =       'MULTI_NPU'
								else:
												A__							 =       'NO'
				else:
								A__							 =       0
								A__							 =       True
								A__							 =       1
								A__							 =       'NO'
				A__							 =       ClusterConfig(**lowerCAmelCase__				)
				config.to_json_file(lowerCAmelCase__				)
				return path



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       parser.add_parser('default'       ,parents=lowerCAmelCase__       ,help=lowerCAmelCase__       ,formatter_class=lowerCAmelCase__				)
				parser.add_argument(
				    '--config_file'       ,default=lowerCAmelCase__       ,help=(
				        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
				        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
				        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
				        'with \'huggingface\'.'
				    )       ,dest='save_location'       ,)

				parser.add_argument(
				    '--mixed_precision'       ,choices=['no', 'fp16', 'bf16']       ,type=lowerCAmelCase__       ,help='Whether or not to use mixed precision training. '
				    'Choose between FP16 and BF16 (bfloat16) training. '
				    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'       ,default='no'       ,)
				parser.set_defaults(func=lowerCAmelCase__				)
				return parser







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       write_basic_config(args.mixed_precision       ,args.save_location				)
				if config_file:
								print(f'''accelerate configuration saved at {config_file}'''				)


