




"""simple docstring"""





def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   List[str]							     =   1
						snake_case__    :   List[str]							     =   0
						snake_case__    :   List[Any]							     =   1
						snake_case__    :   str							     =   1
						snake_case__    :   List[Any]							     =   3
						snake_case__    :   Any							     =   3
						snake_case__    :   Any							     =   limit
						snake_case__    :   str							     =   0

						snake_case__    :   int							     =   ''''''
						while counter != decimal + 1:
												if 4 * q + r - t < n * t:
																		result += str(lowerCamelCase						)
																		if counter == 0:
																								result += "."

																		if decimal == counter:
																								break

																		counter += 1
																		snake_case__    :   Tuple							     =   10 * (r - n * t)
																		snake_case__    :   Optional[int]							     =   ((10 * (3 * q + r)) // t) - 10 * n
																		q *= 10
																		snake_case__    :   List[str]							     =   nr
												else:
																		snake_case__    :   Tuple							     =   (2 * q + r) * l
																		snake_case__    :   Optional[int]							     =   (q * (7 * k) + 2 + (r * l)) // (t * l)
																		q *= k
																		t *= l
																		l += 2
																		k += 1
																		snake_case__    :   Any							     =   nn
																		snake_case__    :   List[Any]							     =   nr
						return result

def 						lowerCAmelCase							(	):
						'''simple docstring'''


						print(f'''{calculate_pi(50						) = }'''						)
						import doctest

						doctest.testmod()


if __name__ == "__main__":
							main()








"""simple docstring"""





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def 						lowerCAmelCase							(	lowerCamelCase						):
						'''simple docstring'''


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

						return orig_key

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case__    :   int							     =   orig_state_dict.pop(lowerCamelCase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		snake_case__    :   Optional[int]							     =   val

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

						return orig_state_dict

def 						lowerCAmelCase							(	lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   List[Any]							     =   torch.load(lowerCamelCase ,     map_location='''cpu'''						)['''model_state_dict''']
						snake_case__    :   Optional[Any]							     =   MraConfig.from_json_file(lowerCamelCase						)
						snake_case__    :   List[Any]							     =   MraForMaskedLM(lowerCamelCase						)

						snake_case__    :   List[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__":
							_lowercase		 =      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."""
							)
							_lowercase		 =      parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


