




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


_a				     =  logging.get_logger(__name__)

_a				     =  {
    '''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 __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				"layoutlmv2"

	def __init__(							self	,   UpperCAmelCase=3_0522	,   UpperCAmelCase=768	,   UpperCAmelCase=12	,   UpperCAmelCase=12	,   UpperCAmelCase=3072	,   UpperCAmelCase="gelu"	,   UpperCAmelCase=0.1	,   UpperCAmelCase=0.1	,   UpperCAmelCase=512	,   UpperCAmelCase=2	,   UpperCAmelCase=0.02	,   UpperCAmelCase=1e-12	,   UpperCAmelCase=0	,   UpperCAmelCase=1024	,   UpperCAmelCase=128	,   UpperCAmelCase=32	,   UpperCAmelCase=True	,   UpperCAmelCase=256	,   UpperCAmelCase=64	,   UpperCAmelCase=True	,   UpperCAmelCase=[7, 7, 256]	,   UpperCAmelCase=128	,   UpperCAmelCase=128	,   UpperCAmelCase=True	,   UpperCAmelCase=True	,   UpperCAmelCase=False	,   UpperCAmelCase=None	,   **UpperCAmelCase	,   ):
							"""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	,   )
							_UpperCAmelCase     =      max_ad_position_embeddings
							_UpperCAmelCase     =      max_rel_pos
							_UpperCAmelCase     =      rel_pos_bins
							_UpperCAmelCase     =      fast_qkv
							_UpperCAmelCase     =      max_rel_ad_pos
							_UpperCAmelCase     =      rel_ad_pos_bins
							_UpperCAmelCase     =      convert_sync_batchnorm
							_UpperCAmelCase     =      image_feature_pool_shape
							_UpperCAmelCase     =      coordinate_size
							_UpperCAmelCase     =      shape_size
							_UpperCAmelCase     =      has_relative_attention_bias
							_UpperCAmelCase     =      has_spatial_attention_bias
							_UpperCAmelCase     =      has_visual_segment_embedding
							_UpperCAmelCase     =      (
							    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
							)

	@classmethod
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return {
							    "MODEL.MASK_ON": True,
							    "MODEL.PIXEL_STD": [57.3_75, 57.1_20, 58.3_95],
							    "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 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      detectrona.config.get_cfg()
							for k, v in self.detectrona_config_args.items():
													_UpperCAmelCase     =      k.split('.'   )
													_UpperCAmelCase     =      detectrona_config
													for attribute in attributes[:-1]:
																			_UpperCAmelCase     =      getattr(UpperCAmelCase	,   UpperCAmelCase   )
													setattr(UpperCAmelCase	,   attributes[-1]	,   UpperCAmelCase   )

							return detectrona_config








from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						if not inplace:
												_UpperCAmelCase     =      ma + ma
						else:
												ma += ma

						return ma


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      -1 * len(__lowerCAmelCase     )
						_UpperCAmelCase     =      list(range(len(tensor.shape[:zero_index]     )     )     )
						return tensor.permute(first_inds + [zero_index + i for i in inds]     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						return t.reshape(t.shape[:-no_dims] + (-1,)     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase = 1E-4     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      mask.expand(*value.shape     )
						return torch.sum(mask * value			,		dim=__lowerCAmelCase     ) / (eps + torch.sum(__lowerCAmelCase			,		dim=__lowerCAmelCase     ))


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase = 2.31_25			,		__lowerCAmelCase = 21.68_75			,		__lowerCAmelCase = 64     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      torch.linspace(__lowerCAmelCase			,		__lowerCAmelCase			,		no_bins - 1			,		device=pts.device     )
						_UpperCAmelCase     =      torch.sqrt(torch.sum((pts.unsqueeze(-2     ) - pts.unsqueeze(-3     )) ** 2			,		dim=-1     )     )
						return torch.bucketize(__lowerCAmelCase			,		__lowerCAmelCase     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  dict:





						"""simple docstring"""



						_UpperCAmelCase     =      dicts[0]
						_UpperCAmelCase     =      {}
						for k, v in first.items():
												_UpperCAmelCase     =      [d[k] for d in dicts]
												if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
																		_UpperCAmelCase     =      dict_multimap(__lowerCAmelCase			,		__lowerCAmelCase     )
												else:
																		_UpperCAmelCase     =      fn(__lowerCAmelCase     )

						return new_dict


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      v_bins.view(((1,) * len(x.shape     )) + (len(__lowerCAmelCase     ),)     )
						_UpperCAmelCase     =      x[..., None] - reshaped_bins
						_UpperCAmelCase     =      torch.argmin(torch.abs(__lowerCAmelCase     )			,		dim=-1     )
						return nn.functional.one_hot(__lowerCAmelCase			,		num_classes=len(__lowerCAmelCase     )     ).float()


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase = 0			,		__lowerCAmelCase = 0     )->  torch.Tensor:





						"""simple docstring"""



						_UpperCAmelCase     =      []
						for i, s in enumerate(data.shape[:no_batch_dims]     ):
												_UpperCAmelCase     =      torch.arange(__lowerCAmelCase     )
												_UpperCAmelCase     =      r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape     ) - i - 1)))     )
												ranges.append(__lowerCAmelCase     )

						_UpperCAmelCase     =      [slice(__lowerCAmelCase     ) for _ in range(len(data.shape     ) - no_batch_dims     )]
						_UpperCAmelCase     =      inds
						ranges.extend(__lowerCAmelCase     )
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(__lowerCAmelCase     )]


_a				     =  TypeVar('''T''')


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Dict[Any, Union[dict, list, tuple, Any]]:





						"""simple docstring"""



						_UpperCAmelCase     =      {}
						for k, v in dic.items():
												if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
																		_UpperCAmelCase     =      dict_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
												else:
																		_UpperCAmelCase     =      tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )

						return new_dict


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  Any:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  dict:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  list:





						"""simple docstring"""



						...


@overload
def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  tuple:





						"""simple docstring"""



						...


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						if isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return dict_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return [tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ) for x in tree]
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return tuple(tree_map(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ) for x in tree     )
						elif isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												return fn(__lowerCAmelCase     )
						else:
												print(type(__lowerCAmelCase     )     )
												raise ValueError('Not supported'     )

_a				     =  partial(tree_map, leaf_type=torch.Tensor)
