"""simple docstring"""







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 					a__ (						UpperCAmelCase__    :     int     )->		Optional[Any]:
						if "model" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""model."""						,     """"""     )
						if "norm1" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""norm1"""						,     """attention.output.LayerNorm"""     )
						if "norm2" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""norm2"""						,     """output.LayerNorm"""     )
						if "norm" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""norm"""						,     """LayerNorm"""     )
						if "transformer" in orig_key:
												UpperCAmelCase__   		=						orig_key.split("""."""     )[0].split("""_"""     )[-1]
												UpperCAmelCase__   		=						orig_key.replace(f'transformer_{layer_num}'						,     f'encoder.layer.{layer_num}'     )
						if "mha.attn" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""mha.attn"""						,     """attention.self"""     )
						if "mha" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""mha"""						,     """attention"""     )
						if "W_q" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""W_q"""						,     """self.query"""     )
						if "W_k" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""W_k"""						,     """self.key"""     )
						if "W_v" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""W_v"""						,     """self.value"""     )
						if "ff.0" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""ff.0"""						,     """intermediate.dense"""     )
						if "ff.2" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""ff.2"""						,     """output.dense"""     )
						if "ff" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""ff"""						,     """output.dense"""     )
						if "mlm_class" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""mlm.mlm_class"""						,     """cls.predictions.decoder"""     )
						if "mlm" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""mlm"""						,     """cls.predictions.transform"""     )
						if "backbone.backbone.encoders" in orig_key:
												UpperCAmelCase__   		=						orig_key.replace("""backbone.backbone.encoders"""						,     """encoder.layer"""     )
						if "cls" not in orig_key:
												UpperCAmelCase__   		=						"""mra.""" + orig_key

						return orig_key


def 					a__ (						UpperCAmelCase__    :     str						,     UpperCAmelCase__    :     int     )->		Any:
						for key in orig_state_dict.copy().keys():
												UpperCAmelCase__   		=						orig_state_dict.pop(UpperCAmelCase__     )

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

						UpperCAmelCase__   		=						orig_state_dict["""cls.predictions.decoder.bias"""]
						UpperCAmelCase__   		=						torch.arange(UpperCAmelCase__     ).expand((1, -1)     ) + 2

						return orig_state_dict


def 					a__ (						UpperCAmelCase__    :     Union[str, Any]						,     UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     Dict     )->		Dict:
						UpperCAmelCase__   		=						torch.load(UpperCAmelCase__						,     map_location="""cpu"""     )["""model_state_dict"""]
						UpperCAmelCase__   		=						MraConfig.from_json_file(UpperCAmelCase__     )
						UpperCAmelCase__   		=						MraForMaskedLM(UpperCAmelCase__     )

						UpperCAmelCase__   		=						convert_checkpoint_helper(config.max_position_embeddings						,     UpperCAmelCase__     )

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

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


if __name__ == "__main__":
						UpperCamelCase_    :    Dict											=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Mra pytorch checkpoint."""
						)
						parser.add_argument(
						    """--config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help="""The json file for Mra model config.""",
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						UpperCamelCase_    :    Union[str, Any]											=						parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


"""simple docstring"""







def 					a__ (						UpperCAmelCase__    :     List[Any]     )->		Dict:
						UpperCAmelCase__   		=						[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
						for gap in gaps:
												for i in range(UpperCAmelCase__						,     len(UpperCAmelCase__     )     ):
																		UpperCAmelCase__   		=						collection[i]
																		UpperCAmelCase__   		=						i
																		while j >= gap and collection[j - gap] > insert_value:
																								UpperCAmelCase__   		=						collection[j - gap]
																								j -= gap
																		if j != i:
																								UpperCAmelCase__   		=						insert_value
						return collection


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

						testmod()
						UpperCamelCase_    :    Union[str, Any]											=						input("""Enter numbers separated by a comma:\n""").strip()
						UpperCamelCase_    :    int											=						[int(item) for item in user_input.split(""",""")]
						print(shell_sort(unsorted))

