



'''simple docstring'''






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


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

							return orig_key


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]   ,      _lowerCAmelCase  :						Dict  )      ->   Any:
							for key in orig_state_dict.copy().keys():
														UpperCAmelCase :       Any 							=		orig_state_dict.pop(_lowerCAmelCase  )

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

							UpperCAmelCase :       Dict 							=		orig_state_dict['''cls.predictions.decoder.bias''']
							UpperCAmelCase :       Union[str, Any] 							=		torch.arange(_lowerCAmelCase  ).expand((1, -1)  ) + 2

							return orig_state_dict


def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						int  )      ->   Optional[int]:
							UpperCAmelCase :       str 							=		torch.load(_lowerCAmelCase   ,      map_location='''cpu'''  )['''model_state_dict''']
							UpperCAmelCase :       str 							=		YosoConfig.from_json_file(_lowerCAmelCase  )
							UpperCAmelCase :       int 							=		YosoForMaskedLM(_lowerCAmelCase  )

							UpperCAmelCase :       Tuple 							=		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__":
						UpperCamelCase__:   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."
						)
						UpperCamelCase__:   List[Any]								=			parser.parse_args()
						convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










'''simple docstring'''






from __future__ import annotations


def  snake_case_     (					_lowerCAmelCase  :						list[int | float]   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						int  )      ->   int | float:
							if len(_lowerCAmelCase  ) == 0:
														raise ValueError('''find_max() arg is an empty sequence'''  )
							if (
							    left >= len(_lowerCAmelCase  )
							    or left < -len(_lowerCAmelCase  )
							    or right >= len(_lowerCAmelCase  )
							    or right < -len(_lowerCAmelCase  )
							):
														raise IndexError('''list index out of range'''  )
							if left == right:
														return nums[left]
							UpperCAmelCase :       List[Any] 							=		(left + right) >> 1  # the middle
							UpperCAmelCase :       Optional[Any] 							=		find_max(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )  # find max in range[left, mid]
							UpperCAmelCase :       Dict 							=		find_max(_lowerCAmelCase   ,      mid + 1   ,      _lowerCAmelCase  )  # find max in range[mid + 1, right]

							return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)





