
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


snake_case						=			logging.get_logger(__name__)

snake_case						=			{
    "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__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		lowerCamelCase           =	"""layoutlmv2"""



		def __init__(			self							: List[str]     ,  UpperCamelCase__							: str=3_0522     ,  UpperCamelCase__							: Any=768     ,  UpperCamelCase__							: Dict=12     ,  UpperCamelCase__							: List[str]=12     ,  UpperCamelCase__							: Optional[int]=3072     ,  UpperCamelCase__							: List[Any]="gelu"     ,  UpperCamelCase__							: Optional[int]=0.1     ,  UpperCamelCase__							: str=0.1     ,  UpperCamelCase__							: Optional[Any]=512     ,  UpperCamelCase__							: int=2     ,  UpperCamelCase__							: List[Any]=0.02     ,  UpperCamelCase__							: Optional[int]=1e-1_2     ,  UpperCamelCase__							: Union[str, Any]=0     ,  UpperCamelCase__							: int=1024     ,  UpperCamelCase__							: List[Any]=128     ,  UpperCamelCase__							: List[str]=32     ,  UpperCamelCase__							: Optional[int]=True     ,  UpperCamelCase__							: List[str]=256     ,  UpperCamelCase__							: List[str]=64     ,  UpperCamelCase__							: Dict=True     ,  UpperCamelCase__							: Tuple=[7, 7, 256]     ,  UpperCamelCase__							: Optional[Any]=128     ,  UpperCamelCase__							: int=128     ,  UpperCamelCase__							: Union[str, Any]=True     ,  UpperCamelCase__							: Any=True     ,  UpperCamelCase__							: Optional[int]=False     ,  UpperCamelCase__							: List[str]=None     ,  **UpperCamelCase__							: str     ,  )	-> Any:






				"""simple docstring"""







				super().__init__(
				    vocab_size=UpperCamelCase__     ,  hidden_size=UpperCamelCase__     ,  num_hidden_layers=UpperCamelCase__     ,  num_attention_heads=UpperCamelCase__     ,  intermediate_size=UpperCamelCase__     ,  hidden_act=UpperCamelCase__     ,  hidden_dropout_prob=UpperCamelCase__     ,  attention_probs_dropout_prob=UpperCamelCase__     ,  max_position_embeddings=UpperCamelCase__     ,  type_vocab_size=UpperCamelCase__     ,  initializer_range=UpperCamelCase__     ,  layer_norm_eps=UpperCamelCase__     ,  pad_token_id=UpperCamelCase__     ,  **UpperCamelCase__     ,  )
				snake_case     :					Optional[int]     =	max_ad_position_embeddings
				snake_case     :					Optional[int]     =	max_rel_pos
				snake_case     :					Optional[Any]     =	rel_pos_bins
				snake_case     :					int     =	fast_qkv
				snake_case     :					int     =	max_rel_ad_pos
				snake_case     :					str     =	rel_ad_pos_bins
				snake_case     :					Optional[int]     =	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     :					Optional[int]     =	has_relative_attention_bias
				snake_case     :					str     =	has_spatial_attention_bias
				snake_case     :					Any     =	has_visual_segment_embedding
				snake_case     :					Union[str, Any]     =	(
				    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
				)



		@classmethod
		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> Any:






				"""simple docstring"""







				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": [[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 			lowerCAmelCase							(			self							: Any			)	-> str:






				"""simple docstring"""







				snake_case     :					int     =	detectrona.config.get_cfg()
				for k, v in self.detectrona_config_args.items():
						snake_case     :					Optional[int]     =	k.split('''.'''			)
						snake_case     :					Optional[Any]     =	detectrona_config
						for attribute in attributes[:-1]:
								snake_case     :					List[Any]     =	getattr(UpperCamelCase__     ,  UpperCamelCase__			)
						setattr(UpperCamelCase__     ,  attributes[-1]     ,  UpperCamelCase__			)

				return detectrona_config




'''simple docstring'''
import torch

from transformers import AutoTokenizer






class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: List[str]			)	-> List[str]:






				"""simple docstring"""







				snake_case     :					List[Any]     =	AutoTokenizer.from_pretrained(UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: str			)	-> Dict:






				"""simple docstring"""







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

						snake_case     :					Union[str, Any]     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''			)			)
						snake_case     :					int     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''			)			)

				elif isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
						snake_case     :					Optional[int]     =	self.tokenizer(
						    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  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 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Any=0.70			)	-> Dict:






				"""simple docstring"""







				snake_case     :					str     =	[]
				for idx in range(len(W_query['''input_ids''']			)			):
						snake_case     :					Optional[int]     =	range(p_start.shape[1]			)

						snake_case     :					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 UpperCamelCase__			: (tup[2] * tup[3])     ,  reverse=UpperCamelCase__			)
						snake_case     :					int     =	[]
						for k in range(len(UpperCamelCase__			)			):
								if output[k][2] * output[k][3] >= thresh:
										snake_case ,snake_case     :					Tuple     =	output[k][0], output[k][1]
										snake_case     :					str     =	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(UpperCamelCase__			)

				return final_outputs


