





'''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


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






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






def 	_lowerCAmelCase      (  __snake_case  :			Tuple			,							__snake_case  :			Any      )					->					List[Any]:
     __A						:			List[str]								=	parser.add_parser('default'			,							parents=__a			,							help=__a			,							formatter_class=__a      )
     parser.add_argument(
         '--config_file'			,							default=__a			,							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=__a			,							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=__a      )
     return parser







def 	_lowerCAmelCase      (  __snake_case  :			Dict      )					->					List[Any]:
     __A						:			List[Any]								=	write_basic_config(args.mixed_precision			,							args.save_location      )
     if config_file:
          print(f'accelerate configuration saved at {config_file}'      )






'''simple docstring'''




import itertools
import math






def 	_lowerCAmelCase      (  __snake_case  :			int      )					->					bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5			,							int(math.sqrt(__snake_case      ) + 1      )			,							6      ):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True






def 	_lowerCAmelCase      (  )					->					List[Any]:
     __A						:			Optional[Any]								=	2
     while True:
          if is_prime(__snake_case      ):
               yield num
          num += 1






def 	_lowerCAmelCase      (  __snake_case  :			int = 1_00_01      )					->					int:
     return next(itertools.islice(prime_generator()			,							nth - 1			,							__snake_case      )      )


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