
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM



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

			return orig_key



def      _a     (   __UpperCamelCase       :  Optional[Any]	,__UpperCamelCase       :  List[Any]    ):
			for key in orig_state_dict.copy().keys():
						lowerCAmelCase__ :    Optional[int]				      =			orig_state_dict.pop(__UpperCamelCase    )

						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(__UpperCamelCase    ).expand((1, -1)    ) + 2

			return orig_state_dict



def      _a     (   __UpperCamelCase       :  Tuple	,__UpperCamelCase       :  Optional[int]	,__UpperCamelCase       :  Union[str, Any]    ):
			lowerCAmelCase__ :    str				      =			torch.load(__UpperCamelCase	,map_location='''cpu'''    )['''model_state_dict''']
			lowerCAmelCase__ :    int				      =			MraConfig.from_json_file(__UpperCamelCase    )
			lowerCAmelCase__ :    Any				      =			MraForMaskedLM(__UpperCamelCase    )

			lowerCAmelCase__ :    Tuple				      =			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__":
							A__       :   Tuple   						=					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."""
							)
							A__       :   List[Any]   						=					parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A__       :   Optional[Any]   						=					abspath(join(dirname(dirname(dirname(__file__))), """src"""))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)



def      _a     (   __UpperCamelCase       :  Tuple    ):
			from transformers.testing_utils import pytest_addoption_shared

			pytest_addoption_shared(__UpperCamelCase    )



def      _a     (   __UpperCamelCase       :  int    ):
			from transformers.testing_utils import pytest_terminal_summary_main

			lowerCAmelCase__ :    int				      =			terminalreporter.config.getoption('''--make-reports'''    )
			if make_reports:
						pytest_terminal_summary_main(__UpperCamelCase	,id=__UpperCamelCase    )



