





"""simple docstring"""




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


A   : str			=				logging.get_logger(__name__)

A   : int			=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

A   : str			=				{
    """vocab_file""": {
        """allegro/herbert-base-cased""": """https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """allegro/herbert-base-cased""": """https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"""
    },
}

A   : Tuple			=				{"""allegro/herbert-base-cased""": 5_1_4}
A   : int			=				{}


class   A      (lowerCAmelCase_   ):
			'''simple docstring'''




			__lowerCamelCase     :	Optional[int]          =   VOCAB_FILES_NAMES
			__lowerCamelCase     :	Any          =   PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase     :	Tuple          =   PRETRAINED_INIT_CONFIGURATION
			__lowerCamelCase     :	Optional[Any]          =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase     :	List[Any]          =   HerbertTokenizer






			def __init__(      self       :   str							,     __lowerCAmelCase       :   Union[str, Any]=None							,     __lowerCAmelCase       :   List[Any]=None							,     __lowerCAmelCase       :   int=None							,     __lowerCAmelCase       :   Union[str, Any]="<s>"							,     __lowerCAmelCase       :   Any="<unk>"							,     __lowerCAmelCase       :   str="<pad>"							,     __lowerCAmelCase       :   str="<mask>"							,     __lowerCAmelCase       :   Any="</s>"							,     **__lowerCAmelCase       :   Tuple							,     )     ->     Union[str, Any]:







					"""simple docstring"""






					super().__init__(
					    __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     tokenizer_file=__SCREAMING_SNAKE_CASE							,     cls_token=__SCREAMING_SNAKE_CASE							,     unk_token=__SCREAMING_SNAKE_CASE							,     pad_token=__SCREAMING_SNAKE_CASE							,     mask_token=__SCREAMING_SNAKE_CASE							,     sep_token=__SCREAMING_SNAKE_CASE							,     **__SCREAMING_SNAKE_CASE							,     )






			def        a_	(      self       :   Dict							,     __lowerCAmelCase       :   List[str]							,     __lowerCAmelCase       :   List[Any] = None       )     ->     Union[str, Any]:







					"""simple docstring"""






					A__  				=       [self.cls_token_id]
					A__  				=       [self.sep_token_id]
					if token_ids_a is None:
							return cls + token_ids_a + sep

					return cls + token_ids_a + sep + token_ids_a + sep






			def        a_	(      self       :   str							,     __lowerCAmelCase       :   Dict							,     __lowerCAmelCase       :   Any = None							,     __lowerCAmelCase       :   List[Any] = False       )     ->     Union[str, Any]:







					"""simple docstring"""






					if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__SCREAMING_SNAKE_CASE							,     token_ids_a=__SCREAMING_SNAKE_CASE							,     already_has_special_tokens=__SCREAMING_SNAKE_CASE       )

					if token_ids_a is None:
							return [1] + ([0] * len(__SCREAMING_SNAKE_CASE       )) + [1]
					return [1] + ([0] * len(__SCREAMING_SNAKE_CASE       )) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE       )) + [1]






			def        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   Optional[Any] = None       )     ->     str:







					"""simple docstring"""






					A__  				=       [self.sep_token_id]
					A__  				=       [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        a_	(      self       :   str							,     __lowerCAmelCase       :   Any							,     __lowerCAmelCase       :   Any = None       )     ->     int:







					"""simple docstring"""






					A__  				=       self._tokenizer.model.save(__SCREAMING_SNAKE_CASE							,     name=__SCREAMING_SNAKE_CASE       )
					return tuple(__SCREAMING_SNAKE_CASE       )










import argparse

import torch

from transformers import BertForMaskedLM


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

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

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

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

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

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



