"""simple docstring"""




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


_a				:		int	   =       logging.get_logger(__name__)

_a				:		List[Any]	   =       {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[str]		 =		"beit"






     def __init__(		self    ,					a__=8192    ,					a__=768    ,					a__=12    ,					a__=12    ,					a__=3072    ,					a__="gelu"    ,					a__=0.0    ,					a__=0.0    ,					a__=0.0_2    ,					a__=1e-12    ,					a__=224    ,					a__=16    ,					a__=3    ,					a__=False    ,					a__=False    ,					a__=False    ,					a__=False    ,					a__=0.1    ,					a__=0.1    ,					a__=True    ,					a__=[3, 5, 7, 11]    ,					a__=[1, 2, 3, 6]    ,					a__=True    ,					a__=0.4    ,					a__=256    ,					a__=1    ,					a__=False    ,					a__=255    ,					**a__    ,					):
       super().__init__(**a__  )

       _lowerCAmelCase						:	Optional[int]        =					vocab_size
       _lowerCAmelCase						:	Union[str, Any]        =					hidden_size
       _lowerCAmelCase						:	Any        =					num_hidden_layers
       _lowerCAmelCase						:	List[str]        =					num_attention_heads
       _lowerCAmelCase						:	List[str]        =					intermediate_size
       _lowerCAmelCase						:	Dict        =					hidden_act
       _lowerCAmelCase						:	Optional[int]        =					hidden_dropout_prob
       _lowerCAmelCase						:	Any        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	str        =					initializer_range
       _lowerCAmelCase						:	Union[str, Any]        =					layer_norm_eps

       _lowerCAmelCase						:	Dict        =					image_size
       _lowerCAmelCase						:	str        =					patch_size
       _lowerCAmelCase						:	Optional[int]        =					num_channels
       _lowerCAmelCase						:	List[Any]        =					use_mask_token
       _lowerCAmelCase						:	Any        =					use_absolute_position_embeddings
       _lowerCAmelCase						:	List[Any]        =					use_relative_position_bias
       _lowerCAmelCase						:	Any        =					use_shared_relative_position_bias
       _lowerCAmelCase						:	List[str]        =					layer_scale_init_value
       _lowerCAmelCase						:	Any        =					drop_path_rate
       _lowerCAmelCase						:	Union[str, Any]        =					use_mean_pooling
       # decode head attributes (semantic segmentation)
       _lowerCAmelCase						:	Optional[Any]        =					out_indices
       _lowerCAmelCase						:	Optional[int]        =					pool_scales
       # auxiliary head attributes (semantic segmentation)
       _lowerCAmelCase						:	List[str]        =					use_auxiliary_head
       _lowerCAmelCase						:	Tuple        =					auxiliary_loss_weight
       _lowerCAmelCase						:	Union[str, Any]        =					auxiliary_channels
       _lowerCAmelCase						:	str        =					auxiliary_num_convs
       _lowerCAmelCase						:	List[Any]        =					auxiliary_concat_input
       _lowerCAmelCase						:	Any        =					semantic_loss_ignore_index







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Dict		 =		version.parse("1.11"		)






     @property
     def    __A  (		self  ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
           ]  )






     @property
     def    __A  (		self  ):
       return 1e-4






"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	bool = False     )	->				str:

  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected string as input, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )
  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected boolean as use_pascal parameter, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )

  _lowerCAmelCase						:	str        =					input_str.split("""_"""     )

  _lowerCAmelCase						:	Optional[int]        =					0 if use_pascal else 1

  _lowerCAmelCase						:	List[Any]        =					words[start_index:]

  _lowerCAmelCase						:	Union[str, Any]        =					[word[0].upper() + word[1:] for word in words_to_capitalize]

  _lowerCAmelCase						:	List[Any]        =					"""""" if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
     from doctest import testmod

     testmod()




