






"""simple docstring"""




from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      _lowerCamelCase=0							):
         UpperCamelCase_: str   				=    1.0 if scale is None else scale
         UpperCamelCase_: Any   				=    0.0 if loc is None else loc

         super().__init__(_lowerCamelCase  ,      [AffineTransform(loc=self.loc  ,      scale=self.scale  ,      event_dim=_lowerCamelCase							)]							)





       @property
       def       _a   ( self							):
         return self.base_dist.mean * self.scale + self.loc





       @property
       def       _a   ( self							):
         return self.base_dist.variance * self.scale**2





       @property
       def       _a   ( self							):
         return self.variance.sqrt()







class     _lowerCAmelCase(	nn.Module ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      **_lowerCamelCase							):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    args_dim
         UpperCamelCase_: str   				=    nn.ModuleList([nn.Linear(_lowerCamelCase  ,      _lowerCamelCase							) for dim in args_dim.values()]							)
         UpperCamelCase_: Optional[int]   				=    domain_map





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Optional[int]   				=    [proj(_lowerCamelCase							) for proj in self.proj]

         return self.domain_map(*_lowerCamelCase							)







class     _lowerCAmelCase(	nn.Module ):





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase							):
         super().__init__()
         UpperCamelCase_: Optional[int]   				=    function





       def       _a   ( self  ,      _lowerCamelCase  ,      *_lowerCamelCase							):
         return self.function(_lowerCamelCase  ,      *_lowerCamelCase							)







class     _lowerCAmelCase:





       """simple docstring"""







       a    :		type
       a    :		int
       a    :		Dict[str, int]





       def __init__( self  ,      _lowerCamelCase = 1							):
         UpperCamelCase_: List[Any]   				=    dim
         UpperCamelCase_: Optional[int]   				=    {k: dim * self.args_dim[k] for k in self.args_dim}





       def       _a   ( self  ,      _lowerCamelCase							):
         if self.dim == 1:
           return self.distribution_class(*_lowerCamelCase							)
         else:
           return Independent(self.distribution_class(*_lowerCamelCase							)  ,      1							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      ):
         UpperCamelCase_: Tuple   				=    self._base_distribution(_lowerCamelCase							)
         if loc is None and scale is None:
           return distr
         else:
           return AffineTransformed(_lowerCamelCase  ,      loc=_lowerCamelCase  ,      scale=_lowerCamelCase  ,      event_dim=self.event_dim							)





       @property
       def       _a   ( self							):
         return () if self.dim == 1 else (self.dim,)





       @property
       def       _a   ( self							):
         return len(self.event_shape							)





       @property
       def       _a   ( self							):
         return 0.0





       def       _a   ( self  ,      _lowerCamelCase							):
         return ParameterProjection(
             in_features=_lowerCamelCase  ,      args_dim=self.args_dim  ,      domain_map=LambdaLayer(self.domain_map							)  ,      )





       def       _a   ( self  ,      *_lowerCamelCase							):
         raise NotImplementedError()





       @staticmethod
       def       _a   ( _lowerCamelCase							):
         return (x + torch.sqrt(torch.square(_lowerCamelCase							) + 4.0							)) / 2.0







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Dict[str, int]									={"df": 1, "loc": 1, "scale": 1}
       a    :		type									=StudentT





       @classmethod
       def       _a   ( cls  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Tuple   				=    cls.squareplus(_lowerCamelCase							).clamp_min(torch.finfo(scale.dtype							).eps							)
         UpperCamelCase_: List[str]   				=    2.0 + cls.squareplus(_lowerCamelCase							)
         return df.squeeze(-1							), loc.squeeze(-1							), scale.squeeze(-1							)







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Dict[str, int]									={"loc": 1, "scale": 1}
       a    :		type									=Normal





       @classmethod
       def       _a   ( cls  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: int   				=    cls.squareplus(_lowerCamelCase							).clamp_min(torch.finfo(scale.dtype							).eps							)
         return loc.squeeze(-1							), scale.squeeze(-1							)







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Dict[str, int]									={"total_count": 1, "logits": 1}
       a    :		type									=NegativeBinomial





       @classmethod
       def       _a   ( cls  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Any   				=    cls.squareplus(_lowerCamelCase							)
         return total_count.squeeze(-1							), logits.squeeze(-1							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Optional[Any]   				=    distr_args
         if self.dim == 1:
           return self.distribution_class(total_count=_lowerCamelCase  ,      logits=_lowerCamelCase							)
         else:
           return Independent(self.distribution_class(total_count=_lowerCamelCase  ,      logits=_lowerCamelCase							)  ,      1							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None							):
         UpperCamelCase_: Optional[int]   				=    distr_args

         if scale is not None:
           # See scaling property of Gamma.
           logits += scale.log()

         return self._base_distribution((total_count, logits)							)







# 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


A_		:		Any		     =			'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'

def   snake_case ()	->    Union[str, Any]:
  UpperCamelCase_: Tuple   				=    _ask_options(
      'In which compute environment are you running?'     ,   ['This machine', 'AWS (Amazon SageMaker)']     ,   _convert_compute_environment     ,   )
  if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
    UpperCamelCase_: List[str]   				=    get_sagemaker_input()
  else:
    UpperCamelCase_: List[str]   				=    get_cluster_input()
  return config

def   snake_case (UpperCAmelCase__=None  )	->    Union[str, Any]:
  if subparsers is not None:
    UpperCamelCase_: List[Any]   				=    subparsers.add_parser('config'     ,   description=UpperCAmelCase__  )
  else:
    UpperCamelCase_: List[Any]   				=    argparse.ArgumentParser('Accelerate config command'     ,   description=UpperCAmelCase__  )

  parser.add_argument(
      '--config_file'     ,   default=UpperCAmelCase__     ,   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\'.'
      )     ,   )

  if subparsers is not None:
    parser.set_defaults(func=UpperCAmelCase__  )
  return parser

def   snake_case (UpperCAmelCase__  )	->    List[Any]:
  UpperCamelCase_: Union[str, Any]   				=    get_user_input()
  if args.config_file is not None:
    UpperCamelCase_: Tuple   				=    args.config_file
  else:
    if not os.path.isdir(UpperCAmelCase__  ):
      os.makedirs(UpperCAmelCase__  )
    UpperCamelCase_: Dict   				=    default_yaml_config_file

  if config_file.endswith('.json'  ):
    config.to_json_file(UpperCAmelCase__  )
  else:
    config.to_yaml_file(UpperCAmelCase__  )
  print(F'''accelerate configuration saved at {config_file}'''  )

def   snake_case ()	->    str:
  UpperCamelCase_: Tuple   				=    config_command_parser()
  UpperCamelCase_: int   				=    parser.parse_args()
  config_command(UpperCAmelCase__  )


if __name__ == "__main__":
   main()