



import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





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

							return orig_key





def 						__lowerCamelCase       (      __magic_name__	:  Union[str, Any]     , __magic_name__	:  str						):
							for key in orig_state_dict.copy().keys():
														a__:       Optional[int]											=orig_state_dict.pop(__magic_name__						)

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

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

							return orig_state_dict





def 						__lowerCamelCase       (      __magic_name__	:  List[Any]     , __magic_name__	:  str     , __magic_name__	:  Any						):
							a__:       int											=torch.load(__magic_name__     , map_location="cpu"						)["model_state_dict"]
							a__:       Dict											=MraConfig.from_json_file(__magic_name__						)
							a__:       str											=MraForMaskedLM(__magic_name__						)

							a__:       Optional[Any]											=convert_checkpoint_helper(config.max_position_embeddings     , __magic_name__						)

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

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


if __name__ == "__main__":
		__UpperCAmelCase						  =     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						  =     parser.parse_args()
		convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__UpperCAmelCase						  =     logging.get_logger(__name__)


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset

		from transformers import Trainer


		class 	lowerCamelCase__ (		_a   ):


								def __init__( self						:  Any      ,       _a						:  int = 1_0_1			):
															a__:       Dict											=length


								def __len__( self						:  Optional[Any]			):
															return self.length







								def __getitem__( self						:  Dict      ,       _a						:  int			):
															return i


		class 	lowerCamelCase__ :


								def __call__( self						:  Any      ,       _a						:  List[str]			):
															return {"input_ids": torch.tensor(_a			), "labels": torch.tensor(_a			)}


		class 	lowerCamelCase__ (		nn.Module   ):


								def __init__( self						:  Tuple			):
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															a__:       int											=nn.Linear(1_2_0      ,       8_0			)







								def   _lowerCamelCase			( self						:  str      ,       _a						:  List[Any]      ,       _a						:  List[str]=None			):
															if labels is not None:
																						return torch.tensor(0.0      ,       device=input_ids.device			), input_ids
															else:
																						return input_ids





def 						__lowerCamelCase       (      ):
							a__:       List[str]											=HfArgumentParser((TrainingArguments,)						)
							sys.argv += ["--output_dir", "./examples"]
							a__:       int											=parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    F"Process rank: {training_args.local_rank}, device: {training_args.device}, "
							    F"tpu_num_cores: {training_args.tpu_num_cores}"     , )

							# Essentially, what we want to verify in the distributed case is
							# that we get all samples back, in the right order.
							# (this is crucial for prediction for instance)
							for dataset_length in [1_001, 256, 15]:
														a__:       Optional[Any]											=DummyDataset(__magic_name__						)

														def compute_metrics(__magic_name__	:  EvalPrediction						) -> Dict:
																					a__:       List[str]											=list(range(len(__magic_name__						)						)						)
																					a__:       Union[str, Any]											=p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					return {"success": success}

														a__:       Union[str, Any]											=Trainer(
														    model=DummyModel()     , args=__magic_name__     , data_collator=DummyDataCollator()     , eval_dataset=__magic_name__     , compute_metrics=__magic_name__     , )
														a__:       str											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       Tuple											=2

														a__:       Optional[int]											=trainer.evaluate()
														logger.info(__magic_name__						)
														if metrics["eval_success"] is not True:
																					logger.error(__magic_name__						)
																					exit(1						)

														a__:       List[str]											=trainer.predict(__magic_name__						)
														logger.info(p.metrics						)
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics						)
																					exit(1						)

														a__:       List[str]											=None

							logger.info("🔥 All distributed tests successful"						)





def 						__lowerCamelCase       (      __magic_name__	:  Optional[Any]						):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()





