

"""simple docstring"""






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM
def 		A_	(   __UpperCamelCase     :				Any				):
		if "model" in orig_key:
				lowercase										=							orig_key.replace('''model.'''       ,	''''''				)
		if "norm1" in orig_key:
				lowercase										=							orig_key.replace('''norm1'''       ,	'''attention.output.LayerNorm'''				)
		if "norm2" in orig_key:
				lowercase										=							orig_key.replace('''norm2'''       ,	'''output.LayerNorm'''				)
		if "norm" in orig_key:
				lowercase										=							orig_key.replace('''norm'''       ,	'''LayerNorm'''				)
		if "transformer" in orig_key:
				lowercase										=							orig_key.split('''.'''				)[0].split('''_'''				)[-1]
				lowercase										=							orig_key.replace(f"""transformer_{layer_num}"""       ,	f"""encoder.layer.{layer_num}"""				)
		if "mha.attn" in orig_key:
				lowercase										=							orig_key.replace('''mha.attn'''       ,	'''attention.self'''				)
		if "mha" in orig_key:
				lowercase										=							orig_key.replace('''mha'''       ,	'''attention'''				)
		if "W_q" in orig_key:
				lowercase										=							orig_key.replace('''W_q'''       ,	'''self.query'''				)
		if "W_k" in orig_key:
				lowercase										=							orig_key.replace('''W_k'''       ,	'''self.key'''				)
		if "W_v" in orig_key:
				lowercase										=							orig_key.replace('''W_v'''       ,	'''self.value'''				)
		if "ff.0" in orig_key:
				lowercase										=							orig_key.replace('''ff.0'''       ,	'''intermediate.dense'''				)
		if "ff.2" in orig_key:
				lowercase										=							orig_key.replace('''ff.2'''       ,	'''output.dense'''				)
		if "ff" in orig_key:
				lowercase										=							orig_key.replace('''ff'''       ,	'''output.dense'''				)
		if "mlm_class" in orig_key:
				lowercase										=							orig_key.replace('''mlm.mlm_class'''       ,	'''cls.predictions.decoder'''				)
		if "mlm" in orig_key:
				lowercase										=							orig_key.replace('''mlm'''       ,	'''cls.predictions.transform'''				)
		if "backbone.backbone.encoders" in orig_key:
				lowercase										=							orig_key.replace('''backbone.backbone.encoders'''       ,	'''encoder.layer'''				)
		if "cls" not in orig_key:
				lowercase										=							'mra.' + orig_key

		return orig_key
def 		A_	(   __UpperCamelCase     :				Dict       ,	__UpperCamelCase     :				int				):
		for key in orig_state_dict.copy().keys():
				lowercase										=							orig_state_dict.pop(__UpperCamelCase				)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowercase										=							val

		lowercase										=							orig_state_dict['cls.predictions.decoder.bias']
		lowercase										=							torch.arange(__UpperCamelCase				).expand((1, -1)				) + 2

		return orig_state_dict
def 		A_	(   __UpperCamelCase     :				str       ,	__UpperCamelCase     :				List[str]       ,	__UpperCamelCase     :				Any				):
		lowercase										=							torch.load(__UpperCamelCase       ,	map_location='''cpu'''				)['model_state_dict']
		lowercase										=							MraConfig.from_json_file(__UpperCamelCase				)
		lowercase										=							MraForMaskedLM(__UpperCamelCase				)

		lowercase										=							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)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A     						= {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

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