






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


snake_case   :   List[str]    		=		logging.get_logger(__name__)

snake_case   :   Tuple    		=		{
    """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__   (							a_):







				"""simple docstring"""
				__UpperCAmelCase											=   """layoutlmv2"""







				def __init__(		self   :  Tuple							,						UpperCamelCase_   :  List[Any]=3_0_5_2_2							,						UpperCamelCase_   :  str=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  str=1_2							,						UpperCamelCase_   :  Dict=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu"							,						UpperCamelCase_   :  Dict=0.1							,						UpperCamelCase_   :  Dict=0.1							,						UpperCamelCase_   :  Optional[int]=5_1_2							,						UpperCamelCase_   :  Optional[Any]=2							,						UpperCamelCase_   :  str=0.02							,						UpperCamelCase_   :  Dict=1e-1_2							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=1_0_2_4							,						UpperCamelCase_   :  int=1_2_8							,						UpperCamelCase_   :  Dict=3_2							,						UpperCamelCase_   :  List[str]=True							,						UpperCamelCase_   :  Any=2_5_6							,						UpperCamelCase_   :  str=6_4							,						UpperCamelCase_   :  Optional[Any]=True							,						UpperCamelCase_   :  str=[7, 7, 2_5_6]							,						UpperCamelCase_   :  Optional[int]=1_2_8							,						UpperCamelCase_   :  Optional[int]=1_2_8							,						UpperCamelCase_   :  Tuple=True							,						UpperCamelCase_   :  Optional[Any]=True							,						UpperCamelCase_   :  Optional[Any]=False							,						UpperCamelCase_   :  int=None							,						**UpperCamelCase_   :  str							,						):





											'''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_							,						)
											__magic_name__										=						max_ad_position_embeddings
											__magic_name__										=						max_rel_pos
											__magic_name__										=						rel_pos_bins
											__magic_name__										=						fast_qkv
											__magic_name__										=						max_rel_ad_pos
											__magic_name__										=						rel_ad_pos_bins
											__magic_name__										=						convert_sync_batchnorm
											__magic_name__										=						image_feature_pool_shape
											__magic_name__										=						coordinate_size
											__magic_name__										=						shape_size
											__magic_name__										=						has_relative_attention_bias
											__magic_name__										=						has_spatial_attention_bias
											__magic_name__										=						has_visual_segment_embedding
											__magic_name__										=						(
											    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
											)







				@classmethod
				def 							a__ (		self   :  int   ):





											'''simple docstring'''

											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": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
											    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
											    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_0_0_0,
											    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_0_0_0,
											    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_0_0_0,
											    "MODEL.POST_NMS_TOPK_TEST": 1_0_0_0,
											    "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": 1_4,
											    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
											    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
											    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
											    "MODEL.RESNETS.DEPTH": 1_0_1,
											    "MODEL.RESNETS.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
											    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
											    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
											    "MODEL.RESNETS.NUM_GROUPS": 3_2,
											    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
											    "MODEL.RESNETS.STRIDE_IN_1X1": False,
											}







				def 							a__ (		self   :  Any   ):





											'''simple docstring'''

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

											return detectrona_config







"""simple docstring"""
def        A	(							__snake_case:   Dict   ,    __snake_case:   Tuple     )						->		int:

       """simple docstring"""





       sequence.append(__snake_case     )

       __magic_name__										=						0
       while sequence[index] != target:
              index += 1

       sequence.pop()

       if index == len(__snake_case     ):
              return None

       return index


if __name__ == "__main__":
 snake_case   :   Dict    		=		input("""Enter numbers separated by comma:\n""").strip()
 snake_case   :   List[str]    		=		[int(item) for item in user_input.split(""",""")]

 snake_case   :   Union[str, Any]    		=		input("""Enter a single number to be found in the list:\n""")
 snake_case   :   str    		=		int(target_input)
 snake_case   :   Any    		=		sentinel_linear_search(sequence, target)
 if result is not None:
  print(f"""{target} found at positions: {result}""")
 else:
  print("""Not found""")