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


lowercase_											=	logging.get_logger(__name__)

lowercase_											=	{
    """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   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     lowerCAmelCase_     =	'''layoutlmv2'''



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







       """simple docstring"""




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



     @classmethod
     def 				UpperCAmelCase__    (							self    :    List[Any]				):







       """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    :    Any				):







       """simple docstring"""




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

       return detectrona_config


from collections import defaultdict


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					defaultdict[str, int]			=						defaultdict(snake_case    )
  # Creating a dictionary containing count of each word
  for word in sentence.split():
    occurrence[word] += 1
  return occurrence


if __name__ == "__main__":
   for word, count in word_occurrence("""INPUT STRING""").items():
      print(f'''{word}: {count}''')
