






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
 _a     : Optional[Any]   	=      argparse.ArgumentParser(
     description=(
         """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
         """ Distillation"""
     )
 )
 parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
 parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
 parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
 parser.add_argument("""--vocab_transform""", action="""store_true""")
 _a     : List[Any]   	=      parser.parse_args()

 if args.model_type == "bert":
  _a     : int   	=      BertForMaskedLM.from_pretrained(args.model_name)
  _a     : Tuple   	=      'bert'
 else:
  raise ValueError("""args.model_type should be \"bert\".""")

 _a     : List[Any]   	=      model.state_dict()
 _a     : Any   	=      {}

 for w in ["word_embeddings", "position_embeddings"]:
  _a     : List[str]   	=      state_dict[f"""{prefix}.embeddings.{w}.weight"""]
 for w in ["weight", "bias"]:
  _a     : Optional[int]   	=      state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

 _a     : List[str]   	=      0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  for w in ["weight", "bias"]:
   _a     : str   	=      state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
   ]
   _a     : Tuple   	=      state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
   ]
   _a     : str   	=      state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
   ]

   _a     : Any   	=      state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
   ]
   _a     : Union[str, Any]   	=      state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
   ]

   _a     : Union[str, Any]   	=      state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
   ]
   _a     : Any   	=      state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
   ]
   _a     : Optional[int]   	=      state_dict[
       f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
   ]
  std_idx += 1

 _a     : Union[str, Any]   	=      state_dict['cls.predictions.decoder.weight']
 _a     : Union[str, Any]   	=      state_dict['cls.predictions.bias']
 if args.vocab_transform:
  for w in ["weight", "bias"]:
   _a     : Union[str, Any]   	=      state_dict[f"""cls.predictions.transform.dense.{w}"""]
   _a     : List[str]   	=      state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

 print(f"""N layers selected for distillation: {std_idx}""")
 print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

 print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
 torch.save(compressed_sd, args.dump_checkpoint)








"""simple docstring"""






def      _lowercase							(    __snake_case   ,__snake_case      )		->  Tuple:
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			__lowerCAmelCase :    Optional[Any]				=			(boundary[1] - boundary[0]) / steps
			__lowerCAmelCase :    List[Any]				=			boundary[0]
			__lowerCAmelCase :    Any				=			boundary[1]
			__lowerCAmelCase :    Dict				=			make_points(__snake_case   ,__snake_case   ,__snake_case      )
			__lowerCAmelCase :    List[str]				=			0.0
			y += (h / 2.0) * f(__snake_case      )
			for i in x_i:
						# print(i)
						y += h * f(__snake_case      )
			y += (h / 2.0) * f(__snake_case      )
			return y


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case      )		->  Union[str, Any]:
			__lowerCAmelCase :    int				=			a + h
			while x < (b - h):
						yield x
						__lowerCAmelCase :    List[str]				=			x + h


def      _lowercase							(    __snake_case      )		->  Tuple:  # enter your function here
			__lowerCAmelCase :    int				=			(x - 0) * (x - 0)
			return y


def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Tuple				=			0.0  # Lower bound of integration
			__lowerCAmelCase :    Union[str, Any]				=			1.0  # Upper bound of integration
			__lowerCAmelCase :    Dict				=			10.0  # define number of steps or resolution
			__lowerCAmelCase :    Optional[Any]				=			[a, b]  # define boundary of integration
			__lowerCAmelCase :    Optional[int]				=			method_a(__snake_case   ,__snake_case      )
			print(F"""y = {y}"""      )


if __name__ == "__main__":
	main()