import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def  snake_case						(__lowercase						)	->	List[str]:

						'''simple docstring'''



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

						return orig_key
def  snake_case						(__lowercase ,      __lowercase						)	->	int:

						'''simple docstring'''



						for key in orig_state_dict.copy().keys():
												_snake_case						:					List[str]    =   orig_state_dict.pop(__lowercase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_snake_case						:					Union[str, Any]    =   val

						_snake_case						:					List[Any]    =   orig_state_dict["cls.predictions.decoder.bias"]
						_snake_case						:					Union[str, Any]    =   torch.arange(__lowercase						).expand((1, -1)						) + 2

						return orig_state_dict
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	Dict:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   torch.load(__lowercase ,      map_location="cpu"						)["model_state_dict"]
						_snake_case						:					List[Any]    =   YosoConfig.from_json_file(__lowercase						)
						_snake_case						:					Any    =   YosoForMaskedLM(__lowercase						)

						_snake_case						:					Dict    =   convert_checkpoint_helper(config.max_position_embeddings ,      __lowercase						)

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

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[str]         =			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.'
				)
				__SCREAMING_SNAKE_CASE						:   Optional[int]         =			parser.parse_args()
				convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
def  snake_case						(__lowercase ,      __lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					Tuple    =   ""
						for word_or_phrase in separated:
												if not isinstance(__lowercase ,      __lowercase						):
																		raise Exception("join() accepts only strings to be joined"						)
												joined += word_or_phrase + separator
						return joined.strip(__lowercase						)


if __name__ == "__main__":
				from doctest import testmod

				testmod()