


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						snake_case							:	Tuple					      =				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''')
						snake_case							:	List[str]					      =				parser.parse_args()

						if args.model_type == "bert":
												snake_case							:	Optional[int]					      =				BertForMaskedLM.from_pretrained(args.model_name)
												snake_case							:	List[Any]					      =				'''bert'''
						else:
												raise ValueError('''args.model_type should be "bert".''')

						snake_case							:	Any					      =				model.state_dict()
						snake_case							:	str					      =				{}

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

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

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

																		snake_case							:	Optional[Any]					      =				state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
																		]
																		snake_case							:	Dict					      =				state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
																		]
																		snake_case							:	List[str]					      =				state_dict[
																		    f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
																		]
												std_idx += 1

						snake_case							:	Any					      =				state_dict['''cls.predictions.decoder.weight''']
						snake_case							:	int					      =				state_dict['''cls.predictions.bias''']
						if args.vocab_transform:
												for w in ["weight", "bias"]:
																		snake_case							:	Optional[int]					      =				state_dict[f"""cls.predictions.transform.dense.{w}"""]
																		snake_case							:	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)








from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




