






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM






def    UpperCamelCase__  (   UpperCAmelCase     )     ->		Any:







							"""simple docstring"""





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

							return orig_key






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		Dict:







							"""simple docstring"""





							for key in orig_state_dict.copy().keys():
														_a  :    str			=						orig_state_dict.pop(UpperCAmelCase     )

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

							_a  :    str			=						orig_state_dict['''cls.predictions.decoder.bias''']
							_a  :    List[Any]			=						torch.arange(UpperCAmelCase     ).expand((1, -1)     ) + 2

							return orig_state_dict






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		List[str]:







							"""simple docstring"""





							_a  :    Dict			=						torch.load(UpperCAmelCase    ,	map_location='''cpu'''     )['''model_state_dict''']
							_a  :    Optional[Any]			=						MraConfig.from_json_file(UpperCAmelCase     )
							_a  :    Tuple			=						MraForMaskedLM(UpperCAmelCase     )

							_a  :    Optional[Any]			=						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__":
						__lowerCamelCase						= 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.'
						)
						__lowerCamelCase						= 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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase						= {
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BartForCausalLM',
						    'BartForConditionalGeneration',
						    'BartForQuestionAnswering',
						    'BartForSequenceClassification',
						    'BartModel',
						    'BartPretrainedModel',
						    'PretrainedBartModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'TFBartForConditionalGeneration',
						    'TFBartForSequenceClassification',
						    'TFBartModel',
						    'TFBartPretrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase						= [
						    'FlaxBartDecoderPreTrainedModel',
						    'FlaxBartForCausalLM',
						    'FlaxBartForConditionalGeneration',
						    'FlaxBartForQuestionAnswering',
						    'FlaxBartForSequenceClassification',
						    'FlaxBartModel',
						    'FlaxBartPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

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