





"""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
from ..auto import CONFIG_MAPPING


a_					=     logging.get_logger(__name__)

a_					=     {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	"""table-transformer"""
 _lowerCamelCase								=	["""past_key_values"""]
 _lowerCamelCase								=	{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }

 def __init__(       self   ,		__lowerCamelCase=True   ,		__lowerCamelCase=None   ,		__lowerCamelCase=3   ,		__lowerCamelCase=100   ,		__lowerCamelCase=6   ,		__lowerCamelCase=2048   ,		__lowerCamelCase=8   ,		__lowerCamelCase=6   ,		__lowerCamelCase=2048   ,		__lowerCamelCase=8   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=True   ,		__lowerCamelCase="relu"   ,		__lowerCamelCase=256   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=0.0_2   ,		__lowerCamelCase=1.0   ,		__lowerCamelCase=False   ,		__lowerCamelCase="sine"   ,		__lowerCamelCase="resnet50"   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False   ,		__lowerCamelCase=1   ,		__lowerCamelCase=5   ,		__lowerCamelCase=2   ,		__lowerCamelCase=1   ,		__lowerCamelCase=1   ,		__lowerCamelCase=5   ,		__lowerCamelCase=2   ,		__lowerCamelCase=0.1   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     if backbone_config is not None and use_timm_backbone:
         raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''    )

     if not use_timm_backbone:
         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''    )
             __A       :		int							=	CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']    )
         elif isinstance(__lowerCamelCase   ,		__lowerCamelCase    ):
             __A       :		str							=	backbone_config.get('''model_type'''    )
             __A       :		List[str]							=	CONFIG_MAPPING[backbone_model_type]
             __A       :		str							=	config_class.from_dict(__lowerCamelCase    )
         # set timm attributes to None
         __A					,			__A					,			__A       :		List[str]							=	None, None, None

     __A       :		Union[str, Any]							=	use_timm_backbone
     __A       :		Any							=	backbone_config
     __A       :		Optional[int]							=	num_channels
     __A       :		Dict							=	num_queries
     __A       :		Union[str, Any]							=	d_model
     __A       :		str							=	encoder_ffn_dim
     __A       :		Optional[int]							=	encoder_layers
     __A       :		List[str]							=	encoder_attention_heads
     __A       :		List[Any]							=	decoder_ffn_dim
     __A       :		int							=	decoder_layers
     __A       :		List[str]							=	decoder_attention_heads
     __A       :		Optional[int]							=	dropout
     __A       :		List[Any]							=	attention_dropout
     __A       :		Optional[int]							=	activation_dropout
     __A       :		str							=	activation_function
     __A       :		Tuple							=	init_std
     __A       :		List[Any]							=	init_xavier_std
     __A       :		Tuple							=	encoder_layerdrop
     __A       :		Union[str, Any]							=	decoder_layerdrop
     __A       :		Any							=	encoder_layers
     __A       :		Union[str, Any]							=	auxiliary_loss
     __A       :		Optional[int]							=	position_embedding_type
     __A       :		List[str]							=	backbone
     __A       :		List[Any]							=	use_pretrained_backbone
     __A       :		List[str]							=	dilation
     # Hungarian matcher
     __A       :		Optional[int]							=	class_cost
     __A       :		Tuple							=	bbox_cost
     __A       :		List[str]							=	giou_cost
     # Loss coefficients
     __A       :		int							=	mask_loss_coefficient
     __A       :		Optional[int]							=	dice_loss_coefficient
     __A       :		Optional[Any]							=	bbox_loss_coefficient
     __A       :		Optional[int]							=	giou_loss_coefficient
     __A       :		Dict							=	eos_coefficient
     super().__init__(is_encoder_decoder=__lowerCamelCase   ,		**__lowerCamelCase    )

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return self.encoder_attention_heads







 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return self.d_model






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	version.parse("""1.11"""	)

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ('''pixel_mask''', {0: '''batch'''}),
         ]    )

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 1e-5







 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 12











"""simple docstring"""



def 						__lowercase	(  snake_case_    :    list		)  ->list:



    '''simple docstring'''







    for i in range(len(snake_case_		) - 1							,0							,-1		):
        __A       :		Union[str, Any]							=	False

        for j in range(snake_case_							,0							,-1		):
            if unsorted[j] < unsorted[j - 1]:
                __A					,			__A       :		Union[str, Any]							=	unsorted[j - 1], unsorted[j]
                __A       :		Optional[int]							=	True

        for j in range(snake_case_		):
            if unsorted[j] > unsorted[j + 1]:
                __A					,			__A       :		Optional[Any]							=	unsorted[j + 1], unsorted[j]
                __A       :		Union[str, Any]							=	True

        if not swapped:
            break
    return unsorted


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       a_					=     input("""Enter numbers separated by a comma:\n""").strip()
       a_					=     [int(item) for item in user_input.split(""",""")]
       print(f'''{cocktail_shaker_sort(unsorted) = }''')




