


'''simple docstring'''






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		if "model" in orig_key:
				snake_case_		:							List[str]			       =						orig_key.replace("""model."""				,      """""" )
		if "norm1" in orig_key:
				snake_case_		:							Dict			       =						orig_key.replace("""norm1"""				,      """attention.output.LayerNorm""" )
		if "norm2" in orig_key:
				snake_case_		:							List[str]			       =						orig_key.replace("""norm2"""				,      """output.LayerNorm""" )
		if "norm" in orig_key:
				snake_case_		:							Union[str, Any]			       =						orig_key.replace("""norm"""				,      """LayerNorm""" )
		if "transformer" in orig_key:
				snake_case_		:							List[str]			       =						orig_key.split(""".""" )[0].split("""_""" )[-1]
				snake_case_		:							List[Any]			       =						orig_key.replace(F'''transformer_{layer_num}'''				,      F'''encoder.layer.{layer_num}''' )
		if "mha.attn" in orig_key:
				snake_case_		:							Tuple			       =						orig_key.replace("""mha.attn"""				,      """attention.self""" )
		if "mha" in orig_key:
				snake_case_		:							List[str]			       =						orig_key.replace("""mha"""				,      """attention""" )
		if "W_q" in orig_key:
				snake_case_		:							str			       =						orig_key.replace("""W_q"""				,      """self.query""" )
		if "W_k" in orig_key:
				snake_case_		:							Any			       =						orig_key.replace("""W_k"""				,      """self.key""" )
		if "W_v" in orig_key:
				snake_case_		:							Optional[int]			       =						orig_key.replace("""W_v"""				,      """self.value""" )
		if "ff1" in orig_key:
				snake_case_		:							Any			       =						orig_key.replace("""ff1"""				,      """intermediate.dense""" )
		if "ff2" in orig_key:
				snake_case_		:							Any			       =						orig_key.replace("""ff2"""				,      """output.dense""" )
		if "ff" in orig_key:
				snake_case_		:							List[str]			       =						orig_key.replace("""ff"""				,      """output.dense""" )
		if "mlm_class" in orig_key:
				snake_case_		:							Union[str, Any]			       =						orig_key.replace("""mlm.mlm_class"""				,      """cls.predictions.decoder""" )
		if "mlm" in orig_key:
				snake_case_		:							Optional[Any]			       =						orig_key.replace("""mlm"""				,      """cls.predictions.transform""" )
		if "cls" not in orig_key:
				snake_case_		:							int			       =						"""yoso.""" + orig_key

		return orig_key





def      UpperCAmelCase      (		lowerCamelCase_  :str				,      lowerCamelCase_  :List[Any] ):







		'''simple docstring'''
		for key in orig_state_dict.copy().keys():
				snake_case_		:							Union[str, Any]			       =						orig_state_dict.pop(lowerCamelCase_ )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						snake_case_		:							str			       =						val

		snake_case_		:							Tuple			       =						orig_state_dict["""cls.predictions.decoder.bias"""]
		snake_case_		:							Optional[int]			       =						torch.arange(lowerCamelCase_ ).expand((1, -1) ) + 2

		return orig_state_dict





def      UpperCAmelCase      (		lowerCamelCase_  :Dict				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :List[str] ):







		'''simple docstring'''
		snake_case_		:							List[Any]			       =						torch.load(lowerCamelCase_				,      map_location="""cpu""" )["""model_state_dict"""]
		snake_case_		:							Optional[int]			       =						YosoConfig.from_json_file(lowerCamelCase_ )
		snake_case_		:							List[str]			       =						YosoForMaskedLM(lowerCamelCase_ )

		snake_case_		:							Optional[Any]			       =						convert_checkpoint_helper(config.max_position_embeddings				,      lowerCamelCase_ )

		print(model.load_state_dict(lowerCamelCase_ ) )
		model.eval()
		model.save_pretrained(lowerCamelCase_ )

		print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )


if __name__ == "__main__":
							__A	:	int     =     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The json file for YOSO model config.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							__A	:	int     =     parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




'''simple docstring'''






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


__A	:	Optional[int]     =     logging.get_logger(__name__)

__A	:	List[Any]     =     {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Union[str, Any]   		=  'luke'





							def __init__(  self		:List[str]  ,_UpperCamelCase		:Tuple=5_0_2_6_7  ,_UpperCamelCase		:Tuple=5_0_0_0_0_0  ,_UpperCamelCase		:str=7_6_8  ,_UpperCamelCase		:List[Any]=2_5_6  ,_UpperCamelCase		:List[str]=1_2  ,_UpperCamelCase		:str=1_2  ,_UpperCamelCase		:Union[str, Any]=3_0_7_2  ,_UpperCamelCase		:int="gelu"  ,_UpperCamelCase		:int=0.1  ,_UpperCamelCase		:Dict=0.1  ,_UpperCamelCase		:Dict=5_1_2  ,_UpperCamelCase		:Optional[Any]=2  ,_UpperCamelCase		:int=0.02  ,_UpperCamelCase		:Any=1E-1_2  ,_UpperCamelCase		:Tuple=True  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:Any=1  ,_UpperCamelCase		:int=0  ,_UpperCamelCase		:List[Any]=2  ,**_UpperCamelCase		:str  ,):
									super().__init__(pad_token_id=_UpperCamelCase  ,bos_token_id=_UpperCamelCase  ,eos_token_id=_UpperCamelCase  ,**_UpperCamelCase			)

									snake_case_		:							Any			       =						vocab_size
									snake_case_		:							List[Any]			       =						entity_vocab_size
									snake_case_		:							Optional[Any]			       =						hidden_size
									snake_case_		:							List[Any]			       =						entity_emb_size
									snake_case_		:							Dict			       =						num_hidden_layers
									snake_case_		:							Union[str, Any]			       =						num_attention_heads
									snake_case_		:							Dict			       =						hidden_act
									snake_case_		:							Dict			       =						intermediate_size
									snake_case_		:							Any			       =						hidden_dropout_prob
									snake_case_		:							List[Any]			       =						attention_probs_dropout_prob
									snake_case_		:							Optional[Any]			       =						max_position_embeddings
									snake_case_		:							Any			       =						type_vocab_size
									snake_case_		:							int			       =						initializer_range
									snake_case_		:							Union[str, Any]			       =						layer_norm_eps
									snake_case_		:							Tuple			       =						use_entity_aware_attention
									snake_case_		:							Tuple			       =						classifier_dropout