



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


UpperCAmelCase							=       logging.get_logger(__name__)

UpperCAmelCase							=       {
    """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 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   str       =       """layoutlmv2"""


							def __init__(				self	,	a_=30_522	,	a_=768	,	a_=12	,	a_=12	,	a_=3_072	,	a_="gelu"	,	a_=0.1	,	a_=0.1	,	a_=512	,	a_=2	,	a_=0.02	,	a_=1E-1_2	,	a_=0	,	a_=1_024	,	a_=128	,	a_=32	,	a_=True	,	a_=256	,	a_=64	,	a_=True	,	a_=[7, 7, 256]	,	a_=128	,	a_=128	,	a_=True	,	a_=True	,	a_=False	,	a_=None	,	**a_	,	):
											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_	,	)
											a__					   =		max_ad_position_embeddings
											a__					   =		max_rel_pos
											a__					   =		rel_pos_bins
											a__					   =		fast_qkv
											a__					   =		max_rel_ad_pos
											a__					   =		rel_ad_pos_bins
											a__					   =		convert_sync_batchnorm
											a__					   =		image_feature_pool_shape
											a__					   =		coordinate_size
											a__					   =		shape_size
											a__					   =		has_relative_attention_bias
											a__					   =		has_spatial_attention_bias
											a__					   =		has_visual_segment_embedding
											a__					   =		(
											    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": 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		):
											a__					   =		detectrona.config.get_cfg()
											for k, v in self.detectrona_config_args.items():
															a__					   =		k.split("""."""		)
															a__					   =		detectrona_config
															for attribute in attributes[:-1]:
																			a__					   =		getattr(a_	,	a_		)
															setattr(a_	,	attributes[-1]	,	a_		)

											return detectrona_config






from collections import defaultdict

def       A_   (						__a  :					str		):

				"""simple docstring"""
				a__					   =		defaultdict(__a		)
				# 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}""")

