

"""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


snake_case_   :   Union[str, Any]        =       """Create a default config file for Accelerate with only a few flags set."""



def   lowercase_	(    _lowercase       :		Optional[int]="no"							,      _lowercase       :		str = default_json_config_file							,      _lowercase       :		bool = False	):




					'''simple docstring'''






					UpperCAmelCase			:   Optional[int]      =				Path(_lowercase	)
					path.parent.mkdir(parents=_lowercase							,      exist_ok=_lowercase	)
					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
					UpperCAmelCase			:   Union[str, Any]      =				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}"""	)
					UpperCAmelCase			:   Any      =				{
					    "compute_environment": "LOCAL_MACHINE",
					    "mixed_precision": mixed_precision,
					}
					if torch.cuda.is_available():
										UpperCAmelCase			:   Dict      =				torch.cuda.device_count()
										UpperCAmelCase			:   Dict      =				num_gpus
										UpperCAmelCase			:   str      =				False
										if num_gpus > 1:
															UpperCAmelCase			:   Optional[int]      =				"MULTI_GPU"
										else:
															UpperCAmelCase			:   Any      =				"NO"
					elif is_xpu_available() and use_xpu:
										UpperCAmelCase			:   Union[str, Any]      =				torch.xpu.device_count()
										UpperCAmelCase			:   int      =				num_xpus
										UpperCAmelCase			:   str      =				False
										if num_xpus > 1:
															UpperCAmelCase			:   int      =				"MULTI_XPU"
										else:
															UpperCAmelCase			:   Optional[Any]      =				"NO"
					elif is_npu_available():
										UpperCAmelCase			:   Optional[Any]      =				torch.npu.device_count()
										UpperCAmelCase			:   Dict      =				num_npus
										UpperCAmelCase			:   Tuple      =				False
										if num_npus > 1:
															UpperCAmelCase			:   Tuple      =				"MULTI_NPU"
										else:
															UpperCAmelCase			:   List[Any]      =				"NO"
					else:
										UpperCAmelCase			:   Union[str, Any]      =				0
										UpperCAmelCase			:   Tuple      =				True
										UpperCAmelCase			:   Dict      =				1
										UpperCAmelCase			:   Union[str, Any]      =				"NO"
					UpperCAmelCase			:   Tuple      =				ClusterConfig(**_lowercase	)
					config.to_json_file(_lowercase	)
					return path



def   lowercase_	(    _lowercase       :		str							,      _lowercase       :		Tuple	):




					'''simple docstring'''






					UpperCAmelCase			:   Union[str, Any]      =				parser.add_parser("default"							,      parents=_lowercase							,      help=_lowercase							,      formatter_class=_lowercase	)
					parser.add_argument(
					    "--config_file"							,      default=_lowercase							,      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=_lowercase							,      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=_lowercase	)
					return parser




def   lowercase_	(    _lowercase       :		Union[str, Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   Dict      =				write_basic_config(args.mixed_precision							,      args.save_location	)
					if config_file:
										print(F"""accelerate configuration saved at {config_file}"""	)





"""simple docstring"""






def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		Tuple							,      _lowercase       :		int							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				[False] * len(_lowercase	)
					UpperCAmelCase			:   List[str]      =				[]
					queue.append(_lowercase	)
					UpperCAmelCase			:   List[Any]      =				True

					while queue:
										UpperCAmelCase			:   Any      =				queue.pop(0	)
										for ind in range(len(graph[u]	)	):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(_lowercase	)
																				UpperCAmelCase			:   Optional[int]      =				True
																				UpperCAmelCase			:   int      =				u

					return visited[t]



def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		Dict							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				[-1] * (len(_lowercase	))
					UpperCAmelCase			:   List[Any]      =				0
					while bfs(_lowercase							,      _lowercase							,      _lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[str]      =				float("Inf"	)
										UpperCAmelCase			:   str      =				sink

										while s != source:
															# Find the minimum value in select path
															UpperCAmelCase			:   Tuple      =				min(_lowercase							,      graph[parent[s]][s]	)
															UpperCAmelCase			:   Optional[Any]      =				parent[s]

										max_flow += path_flow
										UpperCAmelCase			:   List[str]      =				sink

										while v != source:
															UpperCAmelCase			:   Dict      =				parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															UpperCAmelCase			:   Dict      =				parent[v]
					return max_flow


snake_case_   :   Optional[Any]        =       [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

snake_case_     ,      snake_case_   :   str        =       0, 5




print(ford_fulkerson(graph, source, sink))


