




# 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


__magic_name__  :  Union[str, Any]					=       """Create a default config file for Accelerate with only a few flags set."""
def 		a_							( __lowerCAmelCase="no"   , __lowerCAmelCase = default_json_config_file   , __lowerCAmelCase = False				):
  lowerCAmelCase__										=      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
  lowerCAmelCase__										=      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}"""				)
  lowerCAmelCase__										=      {
      "compute_environment": "LOCAL_MACHINE",
      "mixed_precision": mixed_precision,
  }
  if torch.cuda.is_available():
    lowerCAmelCase__										=      torch.cuda.device_count()
    lowerCAmelCase__										=      num_gpus
    lowerCAmelCase__										=      False
    if num_gpus > 1:
      lowerCAmelCase__										=      "MULTI_GPU"
    else:
      lowerCAmelCase__										=      "NO"
  elif is_xpu_available() and use_xpu:
    lowerCAmelCase__										=      torch.xpu.device_count()
    lowerCAmelCase__										=      num_xpus
    lowerCAmelCase__										=      False
    if num_xpus > 1:
      lowerCAmelCase__										=      "MULTI_XPU"
    else:
      lowerCAmelCase__										=      "NO"
  elif is_npu_available():
    lowerCAmelCase__										=      torch.npu.device_count()
    lowerCAmelCase__										=      num_npus
    lowerCAmelCase__										=      False
    if num_npus > 1:
      lowerCAmelCase__										=      "MULTI_NPU"
    else:
      lowerCAmelCase__										=      "NO"
  else:
    lowerCAmelCase__										=      0
    lowerCAmelCase__										=      True
    lowerCAmelCase__										=      1
    lowerCAmelCase__										=      "NO"
  lowerCAmelCase__										=      ClusterConfig(**__lowerCAmelCase				)
  config.to_json_file(__lowerCAmelCase				)
  return path
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      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 		a_							( __lowerCAmelCase				):
  lowerCAmelCase__										=      write_basic_config(args.mixed_precision   , args.save_location				)
  if config_file:
    print(F"""accelerate configuration saved at {config_file}"""				)


from __future__ import annotations
def 		snake_case__	(		lowercase							,				lowercase					):
   print(F'''Vertex\tShortest Distance from vertex {src}'''					)
   for i, d in enumerate(lowercase					):
      print(F'''{i}\t\t{d}'''					)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   for j in range(lowercase					):
      lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	(graph[j][k] for k in ["src", "dst", "weight"])
      if distance[u] != float("inf"					) and distance[u] + w < distance[v]:
         return True
   return False
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							int       =	[float("inf"					)] * vertex_count
   lowerCAmelCase_:							Dict       =	0.0

   for _ in range(vertex_count - 1					):
      for j in range(lowercase					):
         lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Any       =	(graph[j][k] for k in ["src", "dst", "weight"])

         if distance[u] != float("inf"					) and distance[u] + w < distance[v]:
            lowerCAmelCase_:							List[str]       =	distance[u] + w

   lowerCAmelCase_:							str       =	check_negative_cycle(lowercase							,				lowercase							,				lowercase					)
   if negative_cycle_exists:
      raise Exception("Negative cycle found"					)

   return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a  :      List[str]            =    int(input("""Enter number of vertices: """).strip())
 a  :      Any            =    int(input("""Enter number of edges: """).strip())

 a  :      list[dict[str, int]]            =    [{} for _ in range(E)]

 for i in range(E):
  print("""Edge """, i + 1)
  a     , a     , a  :      Optional[int]            =    (
      int(x)
      for x in input("""Enter source, destination, weight: """).strip().split(""" """)
  )
  a  :      Optional[int]            =    {"""src""": src, """dst""": dest, """weight""": weight}

 a  :      List[Any]            =    int(input("""\nEnter shortest path source:""").strip())
 a  :      Tuple            =    bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)