

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def 		_lowercase (     lowercase__						):





      if "model" in orig_key:
            __lowerCAmelCase					:						List[Any]					=					orig_key.replace('''model.'''   ,	''''''						)
      if "norm1" in orig_key:
            __lowerCAmelCase					:						Any					=					orig_key.replace('''norm1'''   ,	'''attention.output.LayerNorm'''						)
      if "norm2" in orig_key:
            __lowerCAmelCase					:						List[str]					=					orig_key.replace('''norm2'''   ,	'''output.LayerNorm'''						)
      if "norm" in orig_key:
            __lowerCAmelCase					:						str					=					orig_key.replace('''norm'''   ,	'''LayerNorm'''						)
      if "transformer" in orig_key:
            __lowerCAmelCase					:						List[str]					=					orig_key.split('''.'''						)[0].split('''_'''						)[-1]
            __lowerCAmelCase					:						Optional[Any]					=					orig_key.replace(f"""transformer_{layer_num}"""   ,	f"""encoder.layer.{layer_num}"""						)
      if "mha.attn" in orig_key:
            __lowerCAmelCase					:						Tuple					=					orig_key.replace('''mha.attn'''   ,	'''attention.self'''						)
      if "mha" in orig_key:
            __lowerCAmelCase					:						Optional[int]					=					orig_key.replace('''mha'''   ,	'''attention'''						)
      if "W_q" in orig_key:
            __lowerCAmelCase					:						Optional[int]					=					orig_key.replace('''W_q'''   ,	'''self.query'''						)
      if "W_k" in orig_key:
            __lowerCAmelCase					:						Optional[int]					=					orig_key.replace('''W_k'''   ,	'''self.key'''						)
      if "W_v" in orig_key:
            __lowerCAmelCase					:						Union[str, Any]					=					orig_key.replace('''W_v'''   ,	'''self.value'''						)
      if "ff1" in orig_key:
            __lowerCAmelCase					:						Optional[Any]					=					orig_key.replace('''ff1'''   ,	'''intermediate.dense'''						)
      if "ff2" in orig_key:
            __lowerCAmelCase					:						Optional[Any]					=					orig_key.replace('''ff2'''   ,	'''output.dense'''						)
      if "ff" in orig_key:
            __lowerCAmelCase					:						List[str]					=					orig_key.replace('''ff'''   ,	'''output.dense'''						)
      if "mlm_class" in orig_key:
            __lowerCAmelCase					:						Optional[Any]					=					orig_key.replace('''mlm.mlm_class'''   ,	'''cls.predictions.decoder'''						)
      if "mlm" in orig_key:
            __lowerCAmelCase					:						Tuple					=					orig_key.replace('''mlm'''   ,	'''cls.predictions.transform'''						)
      if "cls" not in orig_key:
            __lowerCAmelCase					:						List[str]					=					"""yoso.""" + orig_key

      return orig_key







def 		_lowercase (     lowercase__   ,	lowercase__						):





      for key in orig_state_dict.copy().keys():
            __lowerCAmelCase					:						Optional[Any]					=					orig_state_dict.pop(SCREAMING_SNAKE_CASE__						)

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

      __lowerCAmelCase					:						Tuple					=					orig_state_dict["""cls.predictions.decoder.bias"""]
      __lowerCAmelCase					:						List[Any]					=					torch.arange(SCREAMING_SNAKE_CASE__						).expand((1, -1)						) + 2

      return orig_state_dict







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						str					=					torch.load(SCREAMING_SNAKE_CASE__   ,	map_location='''cpu'''						)["""model_state_dict"""]
      __lowerCAmelCase					:						List[str]					=					YosoConfig.from_json_file(SCREAMING_SNAKE_CASE__						)
      __lowerCAmelCase					:						Any					=					YosoForMaskedLM(SCREAMING_SNAKE_CASE__						)

      __lowerCAmelCase					:						List[Any]					=					convert_checkpoint_helper(config.max_position_embeddings   ,	SCREAMING_SNAKE_CASE__						)

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

      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 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."
 )
 _UpperCamelCase 			=		parser.parse_args()
 convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''


import unittest

from knapsack import greedy_knapsack as kp





class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      [10, 20, 30, 40, 50, 60]
											_SCREAMING_SNAKE_CASE :       List[str]     		=      [2, 4, 6, 8, 10, 12]
											_SCREAMING_SNAKE_CASE :       str     		=      100
											self.assertEqual(kp.calc_profit(__snake_case    ,					__snake_case    ,					__snake_case  )    ,					210  )






							def UpperCAmelCase_	(    self  ):
											self.assertRaisesRegex(__snake_case    ,					"""max_weight must greater than zero."""  )






							def UpperCAmelCase_	(    self  ):
											self.assertRaisesRegex(__snake_case    ,					"""Weight can not be negative."""  )






							def UpperCAmelCase_	(    self  ):
											self.assertRaisesRegex(__snake_case    ,					"""Profit can not be negative."""  )






							def UpperCAmelCase_	(    self  ):
											self.assertRaisesRegex(__snake_case    ,					"""max_weight must greater than zero."""  )






							def UpperCAmelCase_	(    self  ):
											self.assertRaisesRegex(
											    __snake_case    ,					"""The length of profit and weight must be same."""  )


if __name__ == "__main__":
				unittest.main()




