"""simple docstring"""






import argparse

import torch

from transformers import BertForMaskedLM


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

						if args.model_type == "bert":
												lowerCAmelCase__						:						Union[str, Any]		  =			BertForMaskedLM.from_pretrained(args.model_name)
												lowerCAmelCase__						:						Optional[Any]		  =			'bert'
						else:
												raise ValueError('args.model_type should be "bert".')

						lowerCAmelCase__						:						Dict		  =			model.state_dict()
						lowerCAmelCase__						:						Any		  =			{}

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

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

																		lowerCAmelCase__						:						Dict		  =			state_dict[
																		    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
																		]
																		lowerCAmelCase__						:						List[str]		  =			state_dict[
																		    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
																		]

																		lowerCAmelCase__						:						int		  =			state_dict[
																		    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
																		]
																		lowerCAmelCase__						:						Dict		  =			state_dict[
																		    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
																		]
																		lowerCAmelCase__						:						str		  =			state_dict[
																		    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
																		]
												std_idx += 1

						lowerCAmelCase__						:						Any		  =			state_dict['cls.predictions.decoder.weight']
						lowerCAmelCase__						:						List[str]		  =			state_dict['cls.predictions.bias']
						if args.vocab_transform:
												for w in ["weight", "bias"]:
																		lowerCAmelCase__						:						List[Any]		  =			state_dict[F"""cls.predictions.transform.dense.{w}"""]
																		lowerCAmelCase__						:						Dict		  =			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 warnings
from functools import wraps
from typing import Callable



def 						a_		(      lowerCamelCase ):

				@wraps(lowerCamelCase )
				def _inner_fn(*lowerCamelCase      ,    **lowerCamelCase ):
								warnings.warn(
								    (f'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')      ,    lowerCamelCase      ,    )
								return fn(*lowerCamelCase      ,    **lowerCamelCase )

				return _inner_fn



