from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:      List[Any]      							=logging.get_logger(__name__)

_lowercase							:      Optional[Any]      							={
    '''google/vivit-b-16x2-kinetics400''': (
        '''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     List[str]            =				"vivit"







  def __init__(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=2_24      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=32      ,	SCREAMING_SNAKE_CASE__    :					str=[2, 16, 16]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=3      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=7_68      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]=12      ,	SCREAMING_SNAKE_CASE__    :					Any=12      ,	SCREAMING_SNAKE_CASE__    :					int=30_72      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="gelu_fast"      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					int=0.0      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					Dict=1e-06      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	**SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	)    ->				str:
         A						:     str         =hidden_size
         A						:     str         =num_hidden_layers
         A						:     int         =num_attention_heads
         A						:     List[str]         =intermediate_size
         A						:     int         =hidden_act
         A						:     Tuple         =hidden_dropout_prob
         A						:     Optional[int]         =attention_probs_dropout_prob
         A						:     Any         =initializer_range
         A						:     Any         =layer_norm_eps

         A						:     str         =image_size
         A						:     Tuple         =num_frames
         A						:     List[str]         =tubelet_size
         A						:     Any         =num_channels
         A						:     Optional[Any]         =qkv_bias

         super().__init__(**SCREAMING_SNAKE_CASE__     )







# 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


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



def A__  (			lowercase:     int=None					)				->		str:
       if subparsers is not None:
              A						:     List[str]         =subparsers.add_parser('config',		description=lowercase					)
       else:
              A						:     Union[str, Any]         =argparse.ArgumentParser('Accelerate config command',		description=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\'.'
           ),		)

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



def A__  (			lowercase:     Tuple					)				->		List[Any]:
       A						:     Union[str, Any]         =get_user_input()
       if args.config_file is not None:
              A						:     Optional[Any]         =args.config_file
       else:
              if not os.path.isdir(lowercase					):
                     os.makedirs(lowercase					)
              A						:     Union[str, Any]         =default_yaml_config_file

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



def A__  (			)				->		Optional[int]:
       A						:     Any         =config_command_parser()
       A						:     int         =parser.parse_args()
       config_command(lowercase					)


if __name__ == "__main__":
    main()






