



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_							:      Tuple      =  logging.get_logger(__name__)

UpperCAmelCase_							:      Tuple      =  {
    "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   Optional[Any]				=	"""lxmert"""
	lowerCAmelCase	:   Dict				=	{}



	def __init__(       self			,	UpperCAmelCase__=30_522			,	UpperCAmelCase__=768			,	UpperCAmelCase__=12			,	UpperCAmelCase__=9_500			,	UpperCAmelCase__=1_600			,	UpperCAmelCase__=400			,	UpperCAmelCase__=3_072			,	UpperCAmelCase__="gelu"			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=0.1			,	UpperCAmelCase__=512			,	UpperCAmelCase__=2			,	UpperCAmelCase__=0.02			,	UpperCAmelCase__=1e-1_2			,	UpperCAmelCase__=9			,	UpperCAmelCase__=5			,	UpperCAmelCase__=5			,	UpperCAmelCase__=2_048			,	UpperCAmelCase__=4			,	UpperCAmelCase__=6.67			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	UpperCAmelCase__=True			,	**UpperCAmelCase__			,	):
							A__							=       vocab_size
							A__							=       hidden_size
							A__							=       num_attention_heads
							A__							=       hidden_act
							A__							=       intermediate_size
							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__							=       num_qa_labels
							A__							=       num_object_labels
							A__							=       num_attr_labels
							A__							=       l_layers
							A__							=       x_layers
							A__							=       r_layers
							A__							=       visual_feat_dim
							A__							=       visual_pos_dim
							A__							=       visual_loss_normalizer
							A__							=       task_matched
							A__							=       task_mask_lm
							A__							=       task_obj_predict
							A__							=       task_qa
							A__							=       visual_obj_loss
							A__							=       visual_attr_loss
							A__							=       visual_feat_loss
							A__							=       {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
							super().__init__(**UpperCAmelCase__		)










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





