


'''simple docstring'''






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 		_A			(lowerCAmelCase__					:Optional[int]						)				->					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 "ff1" in orig_key:
          _a      =							orig_key.replace('ff1'			,  'intermediate.dense'						)
     if "ff2" in orig_key:
          _a      =							orig_key.replace('ff2'			,  '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 "cls" not in orig_key:
          _a      =							'yoso.' + orig_key

     return orig_key






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




     '''simple docstring'''



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

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

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

     return orig_state_dict






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




     '''simple docstring'''



     _a      =							torch.load(lowerCAmelCase__			,  map_location='cpu'						)['model_state_dict']
     _a      =							YosoConfig.from_json_file(lowerCAmelCase__						)
     _a      =							YosoForMaskedLM(lowerCAmelCase__						)

     _a      =							convert_checkpoint_helper(config.max_position_embeddings			,  lowerCAmelCase__						)

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

     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 YOSO pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for YOSO model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_   :      List[Any]				    =      parser.parse_args()
 convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list[int]			,  lowerCAmelCase__					:list[int]						)				->					None:




     '''simple docstring'''



     _a      =							len(lowerCAmelCase__						)
     print('The following activities are selected:'						)

     # The first activity is always selected
     _a      =							0
     print(lowerCAmelCase__			,  end=','						)

     # Consider rest of the activities
     for j in range(lowerCAmelCase__						):
          # If this activity has start time greater than
          # or equal to the finish time of previously
          # selected activity, then select it
          if start[j] >= finish[i]:
               print(lowerCAmelCase__			,  end=','						)
               _a      =							j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a_   :      List[str]				    =      [1, 3, 0, 5, 8, 5]
 a_   :      str				    =      [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)

