



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




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

							return orig_key






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase  :							List[Any]				     =					orig_state_dict.pop(_lowerCamelCase						)

														if ("pooler" in key) or ("sen_class" in key):
																					continue
														else:
																					_lowerCAmelCase  :							Any				     =					val

							_lowerCAmelCase  :							Any				     =					orig_state_dict["cls.predictions.decoder.bias"]
							_lowerCAmelCase  :							Optional[int]				     =					torch.arange(_lowerCamelCase						).expand((1, -1)						) + 2

							return orig_state_dict






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					torch.load(_lowerCamelCase       ,				map_location="cpu"						)["model_state_dict"]
							_lowerCAmelCase  :							Optional[int]				     =					YosoConfig.from_json_file(_lowerCamelCase						)
							_lowerCAmelCase  :							List[str]				     =					YosoForMaskedLM(_lowerCamelCase						)

							_lowerCAmelCase  :							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__":
						_snake_case	=						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."
						)
						_snake_case	=						parser.parse_args()
						convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





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


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'roc_bert'







							def __init__(     self,    __a=3_0522,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.1,    __a=0.1,    __a=512,    __a=2,    __a=0.02,    __a=1E-12,    __a=True,    __a=0,    __a="absolute",    __a=None,    __a=True,    __a=True,    __a=768,    __a=910,    __a=512,    __a=2_4858,    __a=True,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					vocab_size
														_lowerCAmelCase  :							Optional[Any]				     =					max_position_embeddings
														_lowerCAmelCase  :							Union[str, Any]				     =					hidden_size
														_lowerCAmelCase  :							str				     =					num_hidden_layers
														_lowerCAmelCase  :							List[Any]				     =					num_attention_heads
														_lowerCAmelCase  :							int				     =					intermediate_size
														_lowerCAmelCase  :							Optional[int]				     =					hidden_act
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Optional[Any]				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							Optional[Any]				     =					type_vocab_size
														_lowerCAmelCase  :							int				     =					layer_norm_eps
														_lowerCAmelCase  :							Union[str, Any]				     =					use_cache
														_lowerCAmelCase  :							Optional[int]				     =					enable_pronunciation
														_lowerCAmelCase  :							Dict				     =					enable_shape
														_lowerCAmelCase  :							Optional[Any]				     =					pronunciation_embed_dim
														_lowerCAmelCase  :							Any				     =					pronunciation_vocab_size
														_lowerCAmelCase  :							List[str]				     =					shape_embed_dim
														_lowerCAmelCase  :							int				     =					shape_vocab_size
														_lowerCAmelCase  :							Optional[int]				     =					concat_input
														_lowerCAmelCase  :							Dict				     =					position_embedding_type
														_lowerCAmelCase  :							Tuple				     =					classifier_dropout
														super().__init__(pad_token_id=__a,    **__a)
