



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


__magic_name__       =     logging.get_logger(__name__)

__magic_name__       =     {
    '''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__       (       _snake_case       ):


					"""simple docstring"""



					A__  :				int					 =				'''layoutlmv2'''

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

					@classmethod
					def 				__UpperCAmelCase		(		self     :Optional[int]    ):
										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     :Dict    ):
										lowercase						   =       detectrona.config.get_cfg()
										for k, v in self.detectrona_config_args.items():
															lowercase						   =       k.split('.'    )
															lowercase						   =       detectrona_config
															for attribute in attributes[:-1]:
																				lowercase						   =       getattr(lowercase__      ,  lowercase__    )
															setattr(lowercase__      ,  attributes[-1]      ,  lowercase__    )

										return detectrona_config











import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       RoFormerConfig.from_json_file(_UpperCAmelCase				)
					print(f"""Building PyTorch model from configuration: {config}"""				)
					lowercase						   =       RoFormerForMaskedLM(_UpperCAmelCase				)

					# Load weights from tf checkpoint
					load_tf_weights_in_roformer(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)

					# Save pytorch-model
					print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
					torch.save(model.state_dict()       ,			_UpperCAmelCase       ,			_use_new_zipfile_serialization=_UpperCAmelCase				)


if __name__ == "__main__":
		__magic_name__       =     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(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT 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.'''
		)
		__magic_name__       =     parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






