



"""simple docstring"""




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


_lowerCAmelCase       :      Optional[int]							=							logging.get_logger(__name__)

_lowerCAmelCase       :      Optional[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 			UpperCAmelCase_						(       _UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:     Tuple				  =				'layoutlmv2'



      def __init__(  self  :			str  ,			A  :			Optional[int]=3_0_5_2_2  ,			A  :			List[Any]=7_6_8  ,			A  :			Tuple=1_2  ,			A  :			str=1_2  ,			A  :			str=3_0_7_2  ,			A  :			Optional[int]="gelu"  ,			A  :			int=0.1  ,			A  :			Any=0.1  ,			A  :			str=5_1_2  ,			A  :			str=2  ,			A  :			Optional[int]=0.02  ,			A  :			int=1e-12  ,			A  :			Any=0  ,			A  :			Union[str, Any]=1_0_2_4  ,			A  :			str=1_2_8  ,			A  :			List[str]=3_2  ,			A  :			Tuple=True  ,			A  :			Optional[Any]=2_5_6  ,			A  :			str=6_4  ,			A  :			Any=True  ,			A  :			Union[str, Any]=[7, 7, 2_5_6]  ,			A  :			Tuple=1_2_8  ,			A  :			int=1_2_8  ,			A  :			Optional[Any]=True  ,			A  :			int=True  ,			A  :			Dict=False  ,			A  :			Any=None  ,			**A  :			Tuple  ,			):
           super().__init__(
               vocab_size=A  ,			hidden_size=A  ,			num_hidden_layers=A  ,			num_attention_heads=A  ,			intermediate_size=A  ,			hidden_act=A  ,			hidden_dropout_prob=A  ,			attention_probs_dropout_prob=A  ,			max_position_embeddings=A  ,			type_vocab_size=A  ,			initializer_range=A  ,			layer_norm_eps=A  ,			pad_token_id=A  ,			**A  ,			)
           _UpperCAmelCase    :			Tuple              =					max_ad_position_embeddings
           _UpperCAmelCase    :			List[Any]              =					max_rel_pos
           _UpperCAmelCase    :			Union[str, Any]              =					rel_pos_bins
           _UpperCAmelCase    :			List[Any]              =					fast_qkv
           _UpperCAmelCase    :			str              =					max_rel_ad_pos
           _UpperCAmelCase    :			Dict              =					rel_ad_pos_bins
           _UpperCAmelCase    :			int              =					convert_sync_batchnorm
           _UpperCAmelCase    :			List[Any]              =					image_feature_pool_shape
           _UpperCAmelCase    :			Optional[Any]              =					coordinate_size
           _UpperCAmelCase    :			Dict              =					shape_size
           _UpperCAmelCase    :			List[Any]              =					has_relative_attention_bias
           _UpperCAmelCase    :			Any              =					has_spatial_attention_bias
           _UpperCAmelCase    :			str              =					has_visual_segment_embedding
           _UpperCAmelCase    :			Union[str, Any]              =					(
               detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
           )



      @classmethod
      def 		snake_case_      (  self  :			Dict		):
           return {
               "MODEL.MASK_ON": True,
               "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
               "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 		snake_case_      (  self  :			Tuple		):
           _UpperCAmelCase    :			str              =					detectrona.config.get_cfg()
           for k, v in self.detectrona_config_args.items():
                _UpperCAmelCase    :			Optional[int]              =					k.split("."		)
                _UpperCAmelCase    :			Union[str, Any]              =					detectrona_config
                for attribute in attributes[:-1]:
                     _UpperCAmelCase    :			Optional[int]              =					getattr(A  ,			A		)
                setattr(A  ,			attributes[-1]  ,			A		)

           return detectrona_config





"""simple docstring"""




def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     bool:





     '''simple docstring'''

     if not isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
          raise ValueError("Input series is not valid, valid series - [1, 2/3, 2]"       )
     if len(SCREAMING_SNAKE_CASE__       ) == 0:
          raise ValueError("Input list must be a non empty list"       )
     if len(SCREAMING_SNAKE_CASE__       ) == 1 and series[0] != 0:
          return True
     _UpperCAmelCase    :			Tuple              =					[]
     _UpperCAmelCase    :			Any              =					len(SCREAMING_SNAKE_CASE__       )
     for i in range(0		,						SCREAMING_SNAKE_CASE__       ):
          if series[i] == 0:
               raise ValueError("Input series cannot have 0 as an element"       )
          rec_series.append(1 / series[i]       )
     _UpperCAmelCase    :			Dict              =					rec_series[1] - rec_series[0]
     for index in range(2		,						SCREAMING_SNAKE_CASE__       ):
          if rec_series[index] - rec_series[index - 1] != common_diff:
               return False
     return True

def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     float:





     '''simple docstring'''

     if not isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
          raise ValueError("Input series is not valid, valid series - [2, 4, 6]"       )
     if len(SCREAMING_SNAKE_CASE__       ) == 0:
          raise ValueError("Input list must be a non empty list"       )
     _UpperCAmelCase    :			Any              =					0
     for val in series:
          answer += 1 / val
     return len(SCREAMING_SNAKE_CASE__       ) / answer


if __name__ == "__main__":
 import doctest

 doctest.testmod()
