


'''simple docstring'''



from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__UpperCAmelCase      :Optional[int]			    =       logging.get_logger(__name__)



class      a    :


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			str
     SCREAMING_SNAKE_CASE	:			str							       =       None

     @staticmethod
     def 			lowerCamelCase__			(    )						->							Optional[Any]:
          raise NotImplementedError

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		List[Any]							,			snake_case					:		int							,			snake_case					:		str							,			**snake_case					:		int      )						->							str:
          raise NotImplementedError

     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		Optional[int]      )						->							str:
          raise NotImplementedError

     def 			lowerCamelCase__			(    self					:		Any      )						->							List[Any]:
          if not self.is_available():
               raise RuntimeError(
                   f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'      )






     @classmethod
     def 			lowerCamelCase__			(    cls					:		str      )						->							Optional[int]:
          return f'`pip install {cls.pip_package or cls.name}`'



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			int							       =       "optuna"

     @staticmethod
     def 			lowerCamelCase__			(    )						->							Union[str, Any]:
          return is_optuna_available()

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		List[Any]							,			snake_case					:		int							,			snake_case					:		str							,			**snake_case					:		Tuple      )						->							Tuple:
          return run_hp_search_optuna(snake_case							,			snake_case							,			snake_case							,			**snake_case      )






     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		Tuple      )						->							Union[str, Any]:
          return default_hp_space_optuna(snake_case      )



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			str							       =       "ray"
     SCREAMING_SNAKE_CASE	:			Tuple							       =       "'ray[tune]'"

     @staticmethod
     def 			lowerCamelCase__			(    )						->							int:
          return is_ray_available()

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		Tuple							,			snake_case					:		int							,			snake_case					:		str							,			**snake_case					:		Tuple      )						->							List[str]:
          return run_hp_search_ray(snake_case							,			snake_case							,			snake_case							,			**snake_case      )






     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		List[str]      )						->							List[Any]:
          return default_hp_space_ray(snake_case      )



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Tuple							       =       "sigopt"

     @staticmethod
     def 			lowerCamelCase__			(    )						->							str:
          return is_sigopt_available()

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		List[Any]							,			snake_case					:		int							,			snake_case					:		str							,			**snake_case					:		Tuple      )						->							List[Any]:
          return run_hp_search_sigopt(snake_case							,			snake_case							,			snake_case							,			**snake_case      )






     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		int      )						->							List[str]:
          return default_hp_space_sigopt(snake_case      )



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			int							       =       "wandb"

     @staticmethod
     def 			lowerCamelCase__			(    )						->							List[str]:
          return is_wandb_available()

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Union[str, Any]							,			snake_case					:		int							,			snake_case					:		str							,			**snake_case					:		int      )						->							Optional[int]:
          return run_hp_search_wandb(snake_case							,			snake_case							,			snake_case							,			**snake_case      )






     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		List[Any]      )						->							Tuple:
          return default_hp_space_wandb(snake_case      )



__UpperCAmelCase      :Optional[int]			    =       {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Tuple  							=      [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(_lowercase  ) > 0:
          __UpperCAmelCase     :			int  							=      available_backends[0].name
          if len(_lowercase  ) > 1:
               logger.info(
                   F'{len(_lowercase  )} hyperparameter search backends available. Using {name} as the default.'  )
          return name
     raise RuntimeError(
         '''No hyperparameter search backend available.\n'''
         + '''\n'''.join(
             F' - To install {backend.name} run {backend.pip_install()}'
             for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()  )  )



'''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


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




def      _a     (						_lowercase :     List[Any]="no"      , _lowercase :     str = default_json_config_file      , _lowercase :     bool = False  ):





     '''simple docstring'''






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




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





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[int]  							=      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      _a     (						_lowercase :     List[Any]  ):





     '''simple docstring'''






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