# 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


_UpperCAmelCase						=		"""Create a default config file for Accelerate with only a few flags set."""




def 				UpperCamelCase						(       __lowercase						: Tuple="no"						,__lowercase						: str = default_json_config_file						,__lowercase						: Tuple = False	):
		'''simple docstring'''




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




def 				UpperCamelCase						(       __lowercase						: List[str]						,__lowercase						: Optional[int]	):
		'''simple docstring'''




		A_		:				Dict     =      parser.add_parser('default'						,parents=SCREAMING_SNAKE_CASE_						,help=SCREAMING_SNAKE_CASE_						,formatter_class=SCREAMING_SNAKE_CASE_	)
		parser.add_argument(
		    '--config_file'						,default=SCREAMING_SNAKE_CASE_						,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=SCREAMING_SNAKE_CASE_						,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=SCREAMING_SNAKE_CASE_	)
		return parser




def 				UpperCamelCase						(       __lowercase						: int	):
		'''simple docstring'''




		A_		:				Tuple     =      write_basic_config(args.mixed_precision						,args.save_location	)
		if config_file:
				print(f'''accelerate configuration saved at {config_file}'''	)










def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if divisor % 5 == 0 or divisor % 2 == 0:
    return 0
  lowerCamelCase :					List[Any]    =				1
  lowerCamelCase :					Union[str, Any]    =				1
  while repunit:
    lowerCamelCase :					Union[str, Any]    =				(10 * repunit + 1) % divisor
    repunit_index += 1
  return repunit_index






def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 1000000	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				limit - 1
  if divisor % 2 == 0:
    divisor += 1
  while least_divisible_repunit(SCREAMING_SNAKE_CASE_	) <= limit:
    divisor += 2
  return divisor


if __name__ == "__main__":
   print(f'''{solution() = }''')



