




"""simple docstring"""




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


__A :		Any      							=    logging.get_logger(__name__)

__A :		List[str]      							=    {
    '''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		( _UpperCAmelCase				):
   lowercase       :  int      =				'layoutlmv2'







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=3_0522    ,      SCREAMING_SNAKE_CASE_=768    ,      SCREAMING_SNAKE_CASE_=12    ,      SCREAMING_SNAKE_CASE_=12    ,      SCREAMING_SNAKE_CASE_=3072    ,      SCREAMING_SNAKE_CASE_="gelu"    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=0.1    ,      SCREAMING_SNAKE_CASE_=512    ,      SCREAMING_SNAKE_CASE_=2    ,      SCREAMING_SNAKE_CASE_=0.02    ,      SCREAMING_SNAKE_CASE_=1e-12    ,      SCREAMING_SNAKE_CASE_=0    ,      SCREAMING_SNAKE_CASE_=1024    ,      SCREAMING_SNAKE_CASE_=128    ,      SCREAMING_SNAKE_CASE_=32    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=256    ,      SCREAMING_SNAKE_CASE_=64    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=[7, 7, 256]    ,      SCREAMING_SNAKE_CASE_=128    ,      SCREAMING_SNAKE_CASE_=128    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
       super().__init__(
           vocab_size=SCREAMING_SNAKE_CASE_    ,      hidden_size=SCREAMING_SNAKE_CASE_    ,      num_hidden_layers=SCREAMING_SNAKE_CASE_    ,      num_attention_heads=SCREAMING_SNAKE_CASE_    ,      intermediate_size=SCREAMING_SNAKE_CASE_    ,      hidden_act=SCREAMING_SNAKE_CASE_    ,      hidden_dropout_prob=SCREAMING_SNAKE_CASE_    ,      attention_probs_dropout_prob=SCREAMING_SNAKE_CASE_    ,      max_position_embeddings=SCREAMING_SNAKE_CASE_    ,      type_vocab_size=SCREAMING_SNAKE_CASE_    ,      initializer_range=SCREAMING_SNAKE_CASE_    ,      layer_norm_eps=SCREAMING_SNAKE_CASE_    ,      pad_token_id=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_    ,      )
       UpperCamelCase				:   Union[str, Any]          =							max_ad_position_embeddings
       UpperCamelCase				:   Union[str, Any]          =							max_rel_pos
       UpperCamelCase				:   Dict          =							rel_pos_bins
       UpperCamelCase				:   Optional[Any]          =							fast_qkv
       UpperCamelCase				:   List[str]          =							max_rel_ad_pos
       UpperCamelCase				:   List[Any]          =							rel_ad_pos_bins
       UpperCamelCase				:   Optional[int]          =							convert_sync_batchnorm
       UpperCamelCase				:   Optional[int]          =							image_feature_pool_shape
       UpperCamelCase				:   str          =							coordinate_size
       UpperCamelCase				:   Union[str, Any]          =							shape_size
       UpperCamelCase				:   Union[str, Any]          =							has_relative_attention_bias
       UpperCamelCase				:   int          =							has_spatial_attention_bias
       UpperCamelCase				:   Optional[Any]          =							has_visual_segment_embedding
       UpperCamelCase				:   Tuple          =							(
           detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
       )







   @classmethod
   def       a_							(							self ):
       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       a_							(							self ):
       UpperCamelCase				:   Optional[int]          =							detectrona.config.get_cfg()
       for k, v in self.detectrona_config_args.items():
           UpperCamelCase				:   Dict          =							k.split(""".""" )
           UpperCamelCase				:   Optional[int]          =							detectrona_config
           for attribute in attributes[:-1]:
               UpperCamelCase				:   List[Any]          =							getattr(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
           setattr(SCREAMING_SNAKE_CASE_    ,      attributes[-1]    ,      SCREAMING_SNAKE_CASE_ )

       return detectrona_config










"""simple docstring"""




class 							lowerCamelCase		:







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None ):
       UpperCamelCase				:   Optional[Any]          =							data
       UpperCamelCase				:   str          =							None







   def __repr__(							self ):
       UpperCamelCase				:   str          =							""""""
       UpperCamelCase				:   Any          =							self
       while temp:
           string_rep += f'<{temp.data}> ---> '
           UpperCamelCase				:   Optional[int]          =							temp.next
       string_rep += "<END>"
       return string_rep







def 					A_			(		snake_case_     :       Tuple    ):







    '''simple docstring'''

    # if elements_list is empty
    if not elements_list:
        raise Exception("""The Elements List is empty"""    )

    # Set first element as Head
    UpperCamelCase				:   List[Any]          =							Node(elements_list[0]    )
    UpperCamelCase				:   str          =							head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        UpperCamelCase				:   Optional[int]          =							Node(snake_case_    )
        UpperCamelCase				:   Optional[Any]          =							current.next
    return head


__A :		List[Any]      							=    [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('''Creating Linked List from List.''')
__A :		int      							=    make_linked_list(list_data)
print('''Linked List:''')


print(linked_list)




