import argparse

import torch

from transformers import BertForMaskedLM


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

		if args.model_type == "bert":
				__lowerCamelCase					:	Any       =  BertForMaskedLM.from_pretrained(args.model_name)
				__lowerCamelCase					:	Any       =  '''bert'''
		else:
				raise ValueError('''args.model_type should be "bert".''')

		__lowerCamelCase					:	Dict       =  model.state_dict()
		__lowerCamelCase					:	str       =  {}

		for w in ["word_embeddings", "position_embeddings"]:
				__lowerCamelCase					:	int       =  state_dict[f'''{prefix}.embeddings.{w}.weight''']
		for w in ["weight", "bias"]:
				__lowerCamelCase					:	Any       =  state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

		__lowerCamelCase					:	Tuple       =  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					:	str       =  state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
						]
						__lowerCamelCase					:	str       =  state_dict[
						    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
						]

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

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

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

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


__lowerCamelCase					:	Optional[Any]       =  logging.get_logger(__name__)

__lowerCamelCase					:	Dict       =  {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase					:	int       =  {
    '''vocab_file''': {
        '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt''',
        '''junnyu/roformer_chinese_char_small''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_chinese_char_base''': (
            '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_discriminator''': (
            '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'''
        ),
        '''junnyu/roformer_small_generator''': (
            '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'''
        ),
    }
}

__lowerCamelCase					:	Any       =  {
    '''junnyu/roformer_chinese_small''': 15_36,
    '''junnyu/roformer_chinese_base''': 15_36,
    '''junnyu/roformer_chinese_char_small''': 5_12,
    '''junnyu/roformer_chinese_char_base''': 5_12,
    '''junnyu/roformer_small_discriminator''': 1_28,
    '''junnyu/roformer_small_generator''': 1_28,
}


__lowerCamelCase					:	Union[str, Any]       =  {
    '''junnyu/roformer_chinese_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_small''': {'''do_lower_case''': True},
    '''junnyu/roformer_chinese_char_base''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_discriminator''': {'''do_lower_case''': True},
    '''junnyu/roformer_small_generator''': {'''do_lower_case''': True},
}






class 		a__						(					A__					):
					A	       =							VOCAB_FILES_NAMES
					A	       =							PRETRAINED_VOCAB_FILES_MAP
					A	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A	       =							PRETRAINED_INIT_CONFIGURATION
					A	       =							RoFormerTokenizer

					def __init__(    self      :       List[str],_A      :       int=None,_A      :       int=None,_A      :       int=True,_A      :       List[Any]="[UNK]",_A      :       Tuple="[SEP]",_A      :       List[Any]="[PAD]",_A      :       Optional[int]="[CLS]",_A      :       Optional[Any]="[MASK]",_A      :       Optional[int]=True,_A      :       List[str]=None,**_A      :       List[Any],):







							"""simple docstring"""






							super().__init__(
							    _A,tokenizer_file=_A,do_lower_case=_A,unk_token=_A,sep_token=_A,pad_token=_A,cls_token=_A,mask_token=_A,tokenize_chinese_chars=_A,strip_accents=_A,**_A,)

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
							if (
							    pre_tok_state.get("lowercase",_A    ) != do_lower_case
							    or pre_tok_state.get("strip_accents",_A    ) != strip_accents
							):
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    getattr(_A,pre_tok_state.pop("type"    )    )
									SCREAMING_SNAKE_CASE_  :		Any				      =    do_lower_case
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    strip_accents
									SCREAMING_SNAKE_CASE_  :		str				      =    pre_tok_class(**_A    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    do_lower_case

					def __getstate__(    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.__dict__.copy()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    BertPreTokenizer()
							return state

					def __setstate__(    self      :       List[Any],_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    d
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.__dict__["_tokenizer"].get_vocab()
							SCREAMING_SNAKE_CASE_  :		Any				      =    PreTokenizer.custom(JiebaPreTokenizer(_A    )    )

					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       List[Any],_A      :       str=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

							return output

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [self.sep_token_id]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [self.cls_token_id]
							if token_ids_a is None:
									return len(cls + token_ids_a + sep    ) * [0]
							return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]

					def 				__UpperCamelCase   (    self      :       int,_A      :       str,_A      :       Optional[str] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self._tokenizer.model.save(_A,name=_A    )
							return tuple(_A    )






					def 				__UpperCamelCase   (    self      :       int,_A      :       Optional[int],_A      :       List[Any]=None,_A      :       Tuple=None,_A      :       str=False,**_A      :       List[Any],):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    BertPreTokenizer()
							return super().save_pretrained(_A,_A,_A,_A,**_A    )
