

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


A_						:     str			  =				logging.get_logger(__name__)

A_						:     Optional[int]			  =				{
    '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    A_				( _a  ):


    '''simple docstring'''






    a__       							=    "layoutlmv2"


    def __init__(self       , lowercase__=30_522       , lowercase__=768       , lowercase__=12       , lowercase__=12       , lowercase__=3_072       , lowercase__="gelu"       , lowercase__=0.1       , lowercase__=0.1       , lowercase__=512       , lowercase__=2       , lowercase__=0.02       , lowercase__=1E-12       , lowercase__=0       , lowercase__=1_024       , lowercase__=128       , lowercase__=32       , lowercase__=True       , lowercase__=256       , lowercase__=64       , lowercase__=True       , lowercase__=[7, 7, 256]       , lowercase__=128       , lowercase__=128       , lowercase__=True       , lowercase__=True       , lowercase__=False       , lowercase__=None       , **lowercase__       , )       -> List[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__       , )
        __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        lowerCAmelCase_  (self					)       -> Optional[int]:
        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        lowerCAmelCase_  (self					)       -> List[Any]:
        __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(lowercase__       , lowercase__					)
            setattr(lowercase__       , attributes[-1]       , lowercase__					)

        return detectrona_config







from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

A_						:     Optional[int]			  =				{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     Queue()
    queue.put(SCREAMING_SNAKE_CASE )
    while not queue.empty():
        __UpperCAmelCase			   =     queue.get()
        for w in graph[v]:
            if w not in explored:
                explored.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.put(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     deque([start] )
    while queue:
        __UpperCAmelCase			   =     queue.popleft()
        for child in graph[v]:
            if child not in visited:
                visited.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.append(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE ) ->       None:






    '''simple docstring'''
    __UpperCAmelCase			   =     f'''from __main__ import G, {name}'''
    __UpperCAmelCase			   =     1_0_0_0_0
    __UpperCAmelCase			   =     timeit(f'''{name}(G, \'A\')'''    ,				setup=SCREAMING_SNAKE_CASE    ,				number=SCREAMING_SNAKE_CASE )
    print(f'''{name:<35} finished {number} runs in {res:.5f} seconds''' )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       benchmark_function('breadth_first_search')
       benchmark_function('breadth_first_search_with_deque')
       # breadth_first_search                finished 10000 runs in 0.20999 seconds
       # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds




