



from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


a_       :int				  =				logging.get_logger(__name__)

a_       :Union[str, Any]				  =				{
    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
 import detectrona




class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     """layoutlmv2"""






     def __init__(	self  :  int,			_snake_case  :  Optional[Any]=3_0_5_2_2,			_snake_case  :  Union[str, Any]=7_6_8,			_snake_case  :  str=1_2,			_snake_case  :  Dict=1_2,			_snake_case  :  int=3_0_7_2,			_snake_case  :  List[str]="gelu",			_snake_case  :  Union[str, Any]=0.1,			_snake_case  :  List[str]=0.1,			_snake_case  :  Union[str, Any]=5_1_2,			_snake_case  :  str=2,			_snake_case  :  Any=0.0_2,			_snake_case  :  Optional[Any]=1e-12,			_snake_case  :  Optional[Any]=0,			_snake_case  :  str=1_0_2_4,			_snake_case  :  Union[str, Any]=1_2_8,			_snake_case  :  int=3_2,			_snake_case  :  int=True,			_snake_case  :  Optional[Any]=2_5_6,			_snake_case  :  List[Any]=6_4,			_snake_case  :  int=True,			_snake_case  :  Any=[7, 7, 2_5_6],			_snake_case  :  Union[str, Any]=1_2_8,			_snake_case  :  Dict=1_2_8,			_snake_case  :  Union[str, Any]=True,			_snake_case  :  List[str]=True,			_snake_case  :  Union[str, Any]=False,			_snake_case  :  Optional[int]=None,			**_snake_case  :  Optional[int],			)       ->List[Any]:
            super().__init__(
                vocab_size=_snake_case,			hidden_size=_snake_case,			num_hidden_layers=_snake_case,			num_attention_heads=_snake_case,			intermediate_size=_snake_case,			hidden_act=_snake_case,			hidden_dropout_prob=_snake_case,			attention_probs_dropout_prob=_snake_case,			max_position_embeddings=_snake_case,			type_vocab_size=_snake_case,			initializer_range=_snake_case,			layer_norm_eps=_snake_case,			pad_token_id=_snake_case,			**_snake_case,			)
            snake_case__							:     Any            =     max_ad_position_embeddings
            snake_case__							:     Union[str, Any]            =     max_rel_pos
            snake_case__							:     Optional[int]            =     rel_pos_bins
            snake_case__							:     int            =     fast_qkv
            snake_case__							:     str            =     max_rel_ad_pos
            snake_case__							:     List[Any]            =     rel_ad_pos_bins
            snake_case__							:     Union[str, Any]            =     convert_sync_batchnorm
            snake_case__							:     Any            =     image_feature_pool_shape
            snake_case__							:     Optional[int]            =     coordinate_size
            snake_case__							:     Union[str, Any]            =     shape_size
            snake_case__							:     Tuple            =     has_relative_attention_bias
            snake_case__							:     Optional[Any]            =     has_spatial_attention_bias
            snake_case__							:     Any            =     has_visual_segment_embedding
            snake_case__							:     List[Any]            =     (
                detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
            )






     @classmethod
     def        lowercase_       (	self  :  Any )       ->Any:
            return {
                "MODEL.MASK_ON": True,
                "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
                "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
                "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
                "MODEL.ANCHOR_GENERATOR.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
                "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
                "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_0_0_0,
                "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_0_0_0,
                "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_0_0_0,
                "MODEL.POST_NMS_TOPK_TEST": 1_0_0_0,
                "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
                "MODEL.ROI_HEADS.NUM_CLASSES": 5,
                "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
                "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
                "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
                "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 1_4,
                "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
                "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
                "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
                "MODEL.RESNETS.DEPTH": 1_0_1,
                "MODEL.RESNETS.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
                "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
                "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
                "MODEL.RESNETS.NUM_GROUPS": 3_2,
                "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
                "MODEL.RESNETS.STRIDE_IN_1X1": False,
            }






     def        lowercase_       (	self  :  List[str] )       ->Any:
            snake_case__							:     Any            =     detectrona.config.get_cfg()
            for k, v in self.detectrona_config_args.items():
                   snake_case__							:     Any            =     k.split('.' )
                   snake_case__							:     int            =     detectrona_config
                   for attribute in attributes[:-1]:
                          snake_case__							:     Optional[int]            =     getattr(_snake_case,			_snake_case )
                   setattr(_snake_case,			attributes[-1],			_snake_case )

            return detectrona_config











def 	lowercase_							(A			:			Dict				):
       snake_case__							:     Optional[int]            =     len(A				)
       for i in range(length - 1				):
              snake_case__							:     List[str]            =     False
              for j in range(length - 1 - i				):
                     if collection[j] > collection[j + 1]:
                            snake_case__							:     Optional[Any]            =     True
                            snake_case__     ,    snake_case__							:     Dict            =     collection[j + 1], collection[j]
              if not swapped:
                     break  # Stop iteration if the collection is sorted.
       return collection


if __name__ == "__main__":
 import doctest
 import time

 doctest.testmod()

 a_       :Any				  =				input("Enter numbers separated by a comma:").strip()
 a_       :Optional[Any]				  =				[int(item) for item in user_input.split(",")]
 a_       :Optional[int]				  =				time.process_time()
 print(*bubble_sort(unsorted), sep=",")
 print(F"""Processing time: {(time.process_time() - start)%1e9 + 7}""")






