
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 (     lowerCamelCase__	):
    _lowercase      		='layoutlmv2'




    def __init__( self    ,    _UpperCamelCase=30_522    ,    _UpperCamelCase=768    ,    _UpperCamelCase=12    ,    _UpperCamelCase=12    ,    _UpperCamelCase=3_072    ,    _UpperCamelCase="gelu"    ,    _UpperCamelCase=0.1    ,    _UpperCamelCase=0.1    ,    _UpperCamelCase=512    ,    _UpperCamelCase=2    ,    _UpperCamelCase=0.02    ,    _UpperCamelCase=1e-1_2    ,    _UpperCamelCase=0    ,    _UpperCamelCase=1_024    ,    _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    ,    )    ->				Union[str, Any]:
       super().__init__(
           vocab_size=lowercase_    ,    hidden_size=lowercase_    ,    num_hidden_layers=lowercase_    ,    num_attention_heads=lowercase_    ,    intermediate_size=lowercase_    ,    hidden_act=lowercase_    ,    hidden_dropout_prob=lowercase_    ,    attention_probs_dropout_prob=lowercase_    ,    max_position_embeddings=lowercase_    ,    type_vocab_size=lowercase_    ,    initializer_range=lowercase_    ,    layer_norm_eps=lowercase_    ,    pad_token_id=lowercase_    ,    **lowercase_    ,    )
       lowerCAmelCase_      =     max_ad_position_embeddings
       lowerCAmelCase_      =     max_rel_pos
       lowerCAmelCase_      =     rel_pos_bins
       lowerCAmelCase_      =     fast_qkv
       lowerCAmelCase_      =     max_rel_ad_pos
       lowerCAmelCase_      =     rel_ad_pos_bins
       lowerCAmelCase_      =     convert_sync_batchnorm
       lowerCAmelCase_      =     image_feature_pool_shape
       lowerCAmelCase_      =     coordinate_size
       lowerCAmelCase_      =     shape_size
       lowerCAmelCase_      =     has_relative_attention_bias
       lowerCAmelCase_      =     has_spatial_attention_bias
       lowerCAmelCase_      =     has_visual_segment_embedding
       lowerCAmelCase_      =     (
           detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
       )




    @classmethod
    def 					__a						( self					)    ->				Tuple:
       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": 2_000,
           "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
           "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
           "MODEL.POST_NMS_TOPK_TEST": 1_000,
           "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					)    ->				List[Any]:
       lowerCAmelCase_      =     detectrona.config.get_cfg()
       for k, v in self.detectrona_config_args.items():
          lowerCAmelCase_      =     k.split("."					)
          lowerCAmelCase_      =     detectrona_config
          for attribute in attributes[:-1]:
             lowerCAmelCase_      =     getattr(lowercase_    ,    lowercase_					)
          setattr(lowercase_    ,    attributes[-1]    ,    lowercase_					)

       return detectrona_config





# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:
							lowerCAmelCase_      =     key
							lowerCAmelCase_      =     freq




				def __str__( self					)    ->				Any:
							return f"""Node(key={self.key}, freq={self.freq})"""






def        lowerCamelCase__    (    __lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  Dict   ,							__lowerCAmelCase  :  Optional[Any]   ,							__lowerCAmelCase  :  Optional[int]   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  List[Any]    ):




			"""simple docstring"""


			if i > j or i < 0 or j > len(__lowerCAmelCase    ) - 1:
						return

			lowerCAmelCase_      =     root[i][j]
			if parent == -1:  # root does not have a parent
						print(F"""{key[node]} is the root of the binary search tree."""    )
			elif is_left:
						print(F"""{key[node]} is the left child of key {parent}."""    )
			else:
						print(F"""{key[node]} is the right child of key {parent}."""    )

			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							node - 1   ,							key[node]   ,							__lowerCAmelCase    )
			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							node + 1   ,							__lowerCAmelCase   ,							key[node]   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    __lowerCAmelCase  :  Union[str, Any]    ):




			"""simple docstring"""


			nodes.sort(key=lambda __lowerCAmelCase    : node.key    )

			lowerCAmelCase_      =     len(__lowerCAmelCase    )

			lowerCAmelCase_      =     [nodes[i].key for i in range(__lowerCAmelCase    )]
			lowerCAmelCase_      =     [nodes[i].freq for i in range(__lowerCAmelCase    )]

			# This 2D array stores the overall tree cost (which's as minimized as possible);
			# for a single key, cost is equal to frequency of the key.
			lowerCAmelCase_      =     [[freqs[i] if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]
			# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
			# array
			lowerCAmelCase_      =     [[freqs[i] if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]
			# stores tree roots that will be used later for constructing binary search tree
			lowerCAmelCase_      =     [[i if i == j else 0 for j in range(__lowerCAmelCase    )] for i in range(__lowerCAmelCase    )]

			for interval_length in range(2   ,							n + 1    ):
						for i in range(n - interval_length + 1    ):
									lowerCAmelCase_      =     i + interval_length - 1

									lowerCAmelCase_      =     sys.maxsize  # set the value to "infinity"
									lowerCAmelCase_      =     total[i][j - 1] + freqs[j]

									# Apply Knuth's optimization
									# Loop without optimization: for r in range(i, j + 1):
									for r in range(root[i][j - 1]   ,							root[i + 1][j] + 1    ):  # r is a temporal root
												lowerCAmelCase_      =     dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
												lowerCAmelCase_      =     dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
												lowerCAmelCase_      =     left + total[i][j] + right

												if dp[i][j] > cost:
															lowerCAmelCase_      =     cost
															lowerCAmelCase_      =     r

			print("Binary search tree nodes:"    )
			for node in nodes:
						print(__lowerCAmelCase    )

			print(F"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""    )
			print_binary_search_tree(__lowerCAmelCase   ,							__lowerCAmelCase   ,							0   ,							n - 1   ,							-1   ,							__lowerCAmelCase    )






def        lowerCamelCase__    (    ):




			"""simple docstring"""


			lowerCAmelCase_      =     [Node(__lowerCAmelCase   ,							randint(1   ,							50    )    ) for i in range(10   ,							0   ,							-1    )]
			find_optimal_binary_search_tree(__lowerCAmelCase    )


if __name__ == "__main__":
			main()



