from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase							:      Any      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/deit-base-distilled-patch16-224''': (
        '''https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'''
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}



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


  lowercase		:     List[Any]            =				"deit"







  def __init__(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=7_68      ,	SCREAMING_SNAKE_CASE__    :					str=12      ,	SCREAMING_SNAKE_CASE__    :					Any=12      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=30_72      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Any=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					Any=1e-12      ,	SCREAMING_SNAKE_CASE__    :					str=2_24      ,	SCREAMING_SNAKE_CASE__    :					Any=16      ,	SCREAMING_SNAKE_CASE__    :					int=3      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=True      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=16      ,	**SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	)    ->				Union[str, Any]:
         super().__init__(**SCREAMING_SNAKE_CASE__     )

         A						:     int         =hidden_size
         A						:     Tuple         =num_hidden_layers
         A						:     Dict         =num_attention_heads
         A						:     Dict         =intermediate_size
         A						:     Any         =hidden_act
         A						:     Tuple         =hidden_dropout_prob
         A						:     int         =attention_probs_dropout_prob
         A						:     Dict         =initializer_range
         A						:     int         =layer_norm_eps
         A						:     int         =image_size
         A						:     Tuple         =patch_size
         A						:     str         =num_channels
         A						:     str         =qkv_bias
         A						:     Optional[Any]         =encoder_stride







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


  lowercase		:     Dict            =				version.parse("1.11"			)







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]     )







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				float:
         return 1e-4







# 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()






