'''simple docstring'''

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  __a:					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:					int			  =  parser.parse_args()

  if args.model_type == "bert":
    __a:					Optional[int]			  =  BertForMaskedLM.from_pretrained(args.model_name)
    __a:					Dict			  =  """bert"""
  else:
    raise ValueError("""args.model_type should be \"bert\".""")

  __a:					Tuple			  =  model.state_dict()
  __a:					Union[str, Any]			  =  {}

  for w in ["word_embeddings", "position_embeddings"]:
    __a:					Optional[Any]			  =  state_dict[F'{prefix}.embeddings.{w}.weight']
  for w in ["weight", "bias"]:
    __a:					List[Any]			  =  state_dict[F'{prefix}.embeddings.LayerNorm.{w}']

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

      __a:					Union[str, 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:					Tuple			  =  state_dict[
          F'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
      ]
      __a:					str			  =  state_dict[
          F'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
      ]
      __a:					int			  =  state_dict[
          F'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
      ]
    std_idx += 1

  __a:					Optional[int]			  =  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:					Any			  =  state_dict[F'cls.predictions.transform.dense.{w}']
      __a:					Any			  =  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"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a								=						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								=						parser.parse_args()

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

				_a								=						model.state_dict()
				_a								=						{}

				for w in ["word_embeddings", "position_embeddings"]:
								_a								=						state_dict[F"""{prefix}.embeddings.{w}.weight"""]
				for w in ["weight", "bias"]:
								_a								=						state_dict[F"""{prefix}.embeddings.LayerNorm.{w}"""]

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

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a								=						state_dict["""cls.predictions.decoder.weight"""]
				_a								=						state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a								=						state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a								=						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)






