






import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
							__UpperCamelCase					:		Dict	  =						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""")
							__UpperCamelCase					:		Optional[int]	  =						parser.parse_args()

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

							__UpperCamelCase					:		Optional[int]	  =						model.state_dict()
							__UpperCamelCase					:		Union[str, Any]	  =						{}

							for w in ["word_embeddings", "position_embeddings"]:
														__UpperCamelCase					:		Tuple	  =						state_dict[f'''{prefix}.embeddings.{w}.weight''']
							for w in ["weight", "bias"]:
														__UpperCamelCase					:		List[str]	  =						state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

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

																					__UpperCamelCase					:		str	  =						state_dict[
																					    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
																					]
																					__UpperCamelCase					:		List[Any]	  =						state_dict[
																					    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
																					]

																					__UpperCamelCase					:		List[Any]	  =						state_dict[
																					    f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
																					]
																					__UpperCamelCase					:		Any	  =						state_dict[
																					    f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
																					]
																					__UpperCamelCase					:		Optional[int]	  =						state_dict[
																					    f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
																					]
														std_idx += 1

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCamelCase					:		str	  =						{}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""BartphoTokenizer"""]

if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bartpho import BartphoTokenizer

else:
							import sys

							__UpperCamelCase					:		Any	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




