




"""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


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






def 						_snake_case					( _snake_case							: Tuple="no"    ,  _snake_case							: str = default_json_config_file    ,  _snake_case							: bool = False							)   -> Optional[int]:






      '''simple docstring'''


      _A        =       Path(_snake_case							)
      path.parent.mkdir(parents=_snake_case    ,  exist_ok=_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        =       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(**_snake_case							)
      config.to_json_file(_snake_case							)
      return path






def 						_snake_case					( _snake_case							: Dict    ,  _snake_case							: Optional[Any]							)   -> int:






      '''simple docstring'''


      _A        =       parser.add_parser('default'    ,  parents=_snake_case    ,  help=_snake_case    ,  formatter_class=_snake_case							)
      parser.add_argument(
          '--config_file'    ,  default=_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=_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=_snake_case							)
      return parser







def 						_snake_case					( _snake_case							: List[str]							)   -> str:






      '''simple docstring'''


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







"""simple docstring"""

def 						_snake_case					( _snake_case							: int							)   -> list:






      '''simple docstring'''


      _A        =       int(_snake_case							)
      if n_element < 1:
            _A        =       ValueError('a should be a positive number'							)
            raise my_error

      _A        =       [1]
      _A       ,   _A       ,   _A        =       (0, 0, 0)
      _A        =       1
      while index < n_element:
            while hamming_list[i] * 2 <= hamming_list[-1]:
                  i += 1
            while hamming_list[j] * 3 <= hamming_list[-1]:
                  j += 1
            while hamming_list[k] * 5 <= hamming_list[-1]:
                  k += 1
            hamming_list.append(
                min(hamming_list[i] * 2    ,  hamming_list[j] * 3    ,  hamming_list[k] * 5							)							)
            index += 1
      return hamming_list


if __name__ == "__main__":
       a         =      input('''Enter the last number (nth term) of the Hamming Number Series: ''')
       print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
       a         =      hamming(int(n))
       print('''-----------------------------------------------------''')
       print(F'''The list with nth numbers is: {hamming_numbers}''')
       print('''-----------------------------------------------------''')

