



import torch

from transformers import AutoTokenizer







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   def __init__(    self:     Dict  ,lowerCamelCase_:     Optional[Any]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       int  		=					AutoTokenizer.from_pretrained(lowerCamelCase_							)


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     Dict							)	->      str:

       if isinstance(lowerCamelCase_  ,lowerCamelCase_							) and all(isinstance(_x  ,lowerCamelCase_							) for _x in x							):
           UpperCAmelCase_     :       Any  		=					None
           for l in x:
               UpperCAmelCase_     :       Tuple  		=					self.tokenizer(
                   lowerCamelCase_  ,padding="""max_length"""  ,max_length=384  ,truncation=lowerCamelCase_  ,return_tensors="""pt"""  ,)
               UpperCAmelCase_     :       Tuple  		=					torch.tensor([len(lowerCamelCase_							)]							)
               if d is not None:
                   for k in d.keys():
                       UpperCAmelCase_     :       List[Any]  		=					torch.cat((d[k], t[k])  ,0							)
               else:
                   UpperCAmelCase_     :       List[Any]  		=					t

           UpperCAmelCase_     :       Optional[Any]  		=					torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""							)							)
           UpperCAmelCase_     :       List[Any]  		=					torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""							)							)

       elif isinstance(lowerCamelCase_  ,lowerCamelCase_							) and all(isinstance(_x  ,lowerCamelCase_							) for _x in x							):
           UpperCAmelCase_     :       List[Any]  		=					self.tokenizer(
               lowerCamelCase_  ,padding="""max_length"""  ,max_length=384  ,truncation=lowerCamelCase_  ,return_tensors="""pt"""  ,)

       else:
           raise Exception(
               """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
               """ strings` for supports are supported."""							)

       return d






   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     str  ,lowerCamelCase_:     int=0.7_0							)	->      Optional[int]:
       UpperCAmelCase_     :       Union[str, Any]  		=					[]
       for idx in range(len(W_query["""input_ids"""]							)							):
           UpperCAmelCase_     :       List[Any]  		=					range(p_start.shape[1]							)

           UpperCAmelCase_     :       str  		=					[]
           for start_id in start_indexes:
               for end_id in end_indexes:
                   if start_id < end_id:
                       output.append(
                           (
                               start_id,
                               end_id,
                               p_start[idx][start_id].item(),
                               p_end[idx][end_id].item(),
                           )							)

           output.sort(key=lambda lowerCamelCase_							: (tup[2] * tup[3])  ,reverse=lowerCamelCase_							)
           UpperCAmelCase_     :       str  		=					[]
           for k in range(len(lowerCamelCase_							)							):
               if output[k][2] * output[k][3] >= thresh:
                   UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					output[k][0], output[k][1]
                   UpperCAmelCase_     :       Dict  		=					self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]							)
                   temp.append((decoded, output[k][2] * output[k][3])							)

           final_outputs.append(lowerCamelCase_							)

       return final_outputs





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


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {
    '''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				(     __snake_case ):






   '''simple docstring'''






   A__      : List[Any]       =						"layoutlmv2"


   def __init__(    self:     Tuple  ,lowerCamelCase_:     Dict=30522  ,lowerCamelCase_:     str=768  ,lowerCamelCase_:     Any=12  ,lowerCamelCase_:     Tuple=12  ,lowerCamelCase_:     int=3072  ,lowerCamelCase_:     int="gelu"  ,lowerCamelCase_:     Dict=0.1  ,lowerCamelCase_:     List[Any]=0.1  ,lowerCamelCase_:     List[Any]=512  ,lowerCamelCase_:     Tuple=2  ,lowerCamelCase_:     Dict=0.0_2  ,lowerCamelCase_:     Dict=1e-12  ,lowerCamelCase_:     Dict=0  ,lowerCamelCase_:     List[str]=1024  ,lowerCamelCase_:     Optional[Any]=128  ,lowerCamelCase_:     List[Any]=32  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     Dict=256  ,lowerCamelCase_:     int=64  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     Tuple=[7, 7, 256]  ,lowerCamelCase_:     Union[str, Any]=128  ,lowerCamelCase_:     Any=128  ,lowerCamelCase_:     int=True  ,lowerCamelCase_:     Union[str, Any]=True  ,lowerCamelCase_:     Optional[int]=False  ,lowerCamelCase_:     List[str]=None  ,**lowerCamelCase_:     str  ,)	->      Any:
       super().__init__(
           vocab_size=lowerCamelCase_  ,hidden_size=lowerCamelCase_  ,num_hidden_layers=lowerCamelCase_  ,num_attention_heads=lowerCamelCase_  ,intermediate_size=lowerCamelCase_  ,hidden_act=lowerCamelCase_  ,hidden_dropout_prob=lowerCamelCase_  ,attention_probs_dropout_prob=lowerCamelCase_  ,max_position_embeddings=lowerCamelCase_  ,type_vocab_size=lowerCamelCase_  ,initializer_range=lowerCamelCase_  ,layer_norm_eps=lowerCamelCase_  ,pad_token_id=lowerCamelCase_  ,**lowerCamelCase_  ,)
       UpperCAmelCase_     :       Optional[int]  		=					max_ad_position_embeddings
       UpperCAmelCase_     :       Optional[int]  		=					max_rel_pos
       UpperCAmelCase_     :       Optional[int]  		=					rel_pos_bins
       UpperCAmelCase_     :       List[str]  		=					fast_qkv
       UpperCAmelCase_     :       int  		=					max_rel_ad_pos
       UpperCAmelCase_     :       int  		=					rel_ad_pos_bins
       UpperCAmelCase_     :       Optional[Any]  		=					convert_sync_batchnorm
       UpperCAmelCase_     :       str  		=					image_feature_pool_shape
       UpperCAmelCase_     :       List[Any]  		=					coordinate_size
       UpperCAmelCase_     :       int  		=					shape_size
       UpperCAmelCase_     :       Optional[Any]  		=					has_relative_attention_bias
       UpperCAmelCase_     :       str  		=					has_spatial_attention_bias
       UpperCAmelCase_     :       Tuple  		=					has_visual_segment_embedding
       UpperCAmelCase_     :       Optional[Any]  		=					(
           detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
       )


   @classmethod
   def       A__    (    self:     Any							)	->      Dict:
       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": [[32], [64], [128], [256], [512]],
           "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
           "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
           "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
           "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
           "MODEL.POST_NMS_TOPK_TEST": 1000,
           "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": 14,
           "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
           "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
           "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
           "MODEL.RESNETS.DEPTH": 101,
           "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
           "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.RESNETS.NUM_GROUPS": 32,
           "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
           "MODEL.RESNETS.STRIDE_IN_1X1": False,
       }






   def       A__    (    self:     Tuple							)	->      int:
       UpperCAmelCase_     :       Any  		=					detectrona.config.get_cfg()
       for k, v in self.detectrona_config_args.items():
           UpperCAmelCase_     :       Optional[int]  		=					k.split("""."""							)
           UpperCAmelCase_     :       List[str]  		=					detectrona_config
           for attribute in attributes[:-1]:
               UpperCAmelCase_     :       Optional[Any]  		=					getattr(lowerCamelCase_  ,lowerCamelCase_							)
           setattr(lowerCamelCase_  ,attributes[-1]  ,lowerCamelCase_							)

       return detectrona_config
