



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)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCAmelCase						  =     {
    '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
    '''tokenization_splinter''': ['''SplinterTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						  =     ['''SplinterTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase						  =     [
		    '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SplinterForQuestionAnswering''',
		    '''SplinterForPreTraining''',
		    '''SplinterLayer''',
		    '''SplinterModel''',
		    '''SplinterPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
		from .tokenization_splinter import SplinterTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_splinter_fast import SplinterTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_splinter import (
				    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SplinterForPreTraining,
				    SplinterForQuestionAnswering,
				    SplinterLayer,
				    SplinterModel,
				    SplinterPreTrainedModel,
				)


else:
		import sys

		__UpperCAmelCase						  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





