code: 
  
  
  
  
  
  
  """simple docstring"""
  # Copyright 2022 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
  
  from .config_args import default_config_file, load_config_from_file
  from .config_utils import SubcommandHelpFormatter
  
  
  snake_case   :   List[Any]    		=		"""Update an existing config file with the latest defaults while maintaining the old configuration."""
  
  
  def        A	(							__snake_case:   Optional[Any]     )						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						args.config_file
         if config_file is None and Path(__snake_case     ).exists():
                __magic_name__										=						default_config_file
         elif not Path(__snake_case     ).exists():
                raise ValueError(F"""The passed config file located at {config_file} doesn't exist."""     )
         __magic_name__										=						load_config_from_file(__snake_case     )
  
         if config_file.endswith('.json'     ):
                config.to_json_file(__snake_case     )
         else:
                config.to_yaml_file(__snake_case     )
         return config_file
  
  
  def        A	(							__snake_case:   List[str]   ,    __snake_case:   Any     )						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						parser.add_parser('update'   ,    parents=__snake_case   ,    help=__snake_case   ,    formatter_class=__snake_case     )
         parser.add_argument(
             '--config_file'   ,    default=__snake_case   ,    help=(
                 'The path to the config file to update. 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\'.'
             )   ,    )
  
         parser.set_defaults(func=__snake_case     )
         return parser
  
  
  
  
  
  
  def        A	(							__snake_case:   Optional[Any]     )						->		List[str]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						update_config(__snake_case     )
         print(F"""Sucessfully updated the configuration file at {config_file}."""     )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  import numpy as np
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]   ,    __snake_case:   Tuple     )						->		Dict:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						n_draws * class_counts / class_counts.sum()
         # floored means we don't overshoot n_samples, but probably undershoot
         __magic_name__										=						np.floor(__snake_case     )
         # we add samples according to how much "left over" probability
         # they had, until we arrive at n_samples
         __magic_name__										=						int(n_draws - floored.sum()     )
         if need_to_add > 0:
                __magic_name__										=						continuous - floored
                __magic_name__										=						np.sort(np.unique(__snake_case     )     )[::-1]
                # add according to remainder, but break ties
                # randomly to avoid biases
                for value in values:
                       ((__magic_name__)							,     )										=						np.where(remainder == value     )
                       # if we need_to_add less than what's in inds
                       # we draw randomly from them.
                       # if we need to add more, we add them all and
                       # go to the next value
                       __magic_name__										=						min(len(__snake_case     )   ,    __snake_case     )
                       __magic_name__										=						rng.choice(__snake_case   ,    size=__snake_case   ,    replace=__snake_case     )
                       floored[inds] += 1
                       need_to_add -= add_now
                       if need_to_add == 0:
                              break
         return floored.astype(np.intaa     )
  
  
  
  
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   str   ,    __snake_case:   int   ,    __snake_case:   Optional[Any]   ,    __snake_case:   Any=1_0     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__							,     __magic_name__										=						np.unique(__snake_case   ,    return_inverse=__snake_case     )
         __magic_name__										=						classes.shape[0]
         __magic_name__										=						np.bincount(__snake_case     )
         if np.min(__snake_case     ) < 2:
                raise ValueError('Minimum class count error'     )
         if n_train < n_classes:
                raise ValueError(
                    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)     )
         if n_test < n_classes:
                raise ValueError(
                    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)     )
         __magic_name__										=						np.split(np.argsort(__snake_case   ,    kind='mergesort'     )   ,    np.cumsum(__snake_case     )[:-1]     )
         for _ in range(__snake_case     ):
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
                __magic_name__										=						class_counts - n_i
                __magic_name__										=						approximate_mode(__snake_case   ,    __snake_case   ,    __snake_case     )
  
                __magic_name__										=						[]
                __magic_name__										=						[]
  
                for i in range(__snake_case     ):
                       __magic_name__										=						rng.permutation(class_counts[i]     )
                       __magic_name__										=						class_indices[i].take(__snake_case   ,    mode='clip'     )
                       train.extend(perm_indices_class_i[: n_i[i]]     )
                       test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]     )
                __magic_name__										=						rng.permutation(__snake_case     )
                __magic_name__										=						rng.permutation(__snake_case     )
  
                yield train, test