


'''simple docstring'''






import argparse

import torch

from transformers import MraConfig, MraForMaskedLM






def 						_A					(lowerCAmelCase__	:Tuple		) ->	List[Any]:




     '''simple docstring'''



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

     return orig_key






def 						_A					(lowerCAmelCase__	:Dict					,    lowerCAmelCase__	:List[str]		) ->	List[Any]:




     '''simple docstring'''



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

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

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

     return orig_state_dict






def 						_A					(lowerCAmelCase__	:Optional[int]					,    lowerCAmelCase__	:int					,    lowerCAmelCase__	:str		) ->	Optional[int]:




     '''simple docstring'''



     _a         =				torch.load(A_					,    map_location='cpu'		)['model_state_dict']
     _a         =				MraConfig.from_json_file(A_		)
     _a         =				MraForMaskedLM(A_		)

     _a         =				convert_checkpoint_helper(config.max_position_embeddings					,    A_		)

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

     print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'		)


if __name__ == "__main__":
 a_       :     List[str] 							=     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_       :     Tuple 							=     parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









"""simple docstring"""


import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info




def      snake_case    (A_ :Union[str, Any] ):







  '''simple docstring'''

  class 		snake_case    :





      def __init__(				self      :   Tuple							,						A      :   Dict     ):


        '''simple docstring'''




        assert isinstance(A							,						(bytes, str)     )
        a	:     Union[str, Any]							=  content





      def 							lowerCamelCase__ (				self      :   str     ):


        '''simple docstring'''




        return json.loads(self.content     )

  def mock_response(*A_ :Union[str, Any]      ,     **A_ :Tuple ):
    assert args[0] == AUTHENTICATED_USER_ENDPOINT
    assert "Authorization" in kwargs["headers"]
    assert kwargs["headers"]["Authorization"].startswith('token ' )
    assert "Accept" in kwargs["headers"]
    return FakeResponse(B'{"login":"test","id":1}' )

  monkeypatch.setattr(A_      ,     'get'      ,     A_ )
  a	:     List[str]							=  fetch_github_info('token' )
  assert result["login"] == "test"
  assert result["id"] == 1




