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


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

__lowerCamelCase :     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 		a__						( A__ ):
					A     	=							'layoutlmv2'

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







							"""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_  :		Tuple    				=   max_ad_position_embeddings
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   max_rel_pos
							SCREAMING_SNAKE_CASE_  :		int    				=   rel_pos_bins
							SCREAMING_SNAKE_CASE_  :		Any    				=   fast_qkv
							SCREAMING_SNAKE_CASE_  :		str    				=   max_rel_ad_pos
							SCREAMING_SNAKE_CASE_  :		Dict    				=   rel_ad_pos_bins
							SCREAMING_SNAKE_CASE_  :		Dict    				=   convert_sync_batchnorm
							SCREAMING_SNAKE_CASE_  :		Any    				=   image_feature_pool_shape
							SCREAMING_SNAKE_CASE_  :		int    				=   coordinate_size
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   shape_size
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   has_relative_attention_bias
							SCREAMING_SNAKE_CASE_  :		Dict    				=   has_spatial_attention_bias
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   has_visual_segment_embedding
							SCREAMING_SNAKE_CASE_  :		str    				=   (
							    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
							)

					@classmethod
					def     __UpperCamelCase       (   self				:	Optional[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": [[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				:	Tuple   ):







							"""simple docstring"""






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

							return detectrona_config







import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[Any]          =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__lowerCamelCase :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
