






'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {'vocab_file': 'spiece.model'}

__lowerCAmelCase									=   {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

__lowerCAmelCase									=   {'bert_for_seq_generation': 512}





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					VOCAB_FILES_NAMES
			snake_case__	=					PRETRAINED_VOCAB_FILES_MAP
			snake_case__	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__	=					[]
			snake_case__	=					["input_ids", "attention_mask"]






			def __init__(			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      int="<s>"	,    __SCREAMING_SNAKE_CASE   :      List[Any]="</s>"	,    __SCREAMING_SNAKE_CASE   :      Any="<unk>"	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]="<pad>"	,    __SCREAMING_SNAKE_CASE   :      List[Any]="<::::>"	,    __SCREAMING_SNAKE_CASE   :      Optional[Dict[str, Any]] = None	,    **__SCREAMING_SNAKE_CASE   :      Dict	,    )      ->      None:
							a_		:	List[str]									=   {} if sp_model_kwargs is None else sp_model_kwargs

							# Add extra_ids to the special token list
							super().__init__(
							    bos_token=__SCREAMING_SNAKE_CASE	,    eos_token=__SCREAMING_SNAKE_CASE	,    unk_token=__SCREAMING_SNAKE_CASE	,    pad_token=__SCREAMING_SNAKE_CASE	,    sep_token=__SCREAMING_SNAKE_CASE	,    sp_model_kwargs=self.sp_model_kwargs	,    **__SCREAMING_SNAKE_CASE	,    )

							a_		:	List[str]									=   vocab_file

							a_		:	Dict									=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(__SCREAMING_SNAKE_CASE       )






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Optional[Any]:
							return self.sp_model.get_piece_size()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Any:
							a_		:	List[Any]									=   {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE       ): i for i in range(self.vocab_size       )}
							vocab.update(self.added_tokens_encoder       )
							return vocab






			def __getstate__(			self   :      Optional[Any]       )      ->      Union[str, Any]:
							a_		:	Union[str, Any]									=   self.__dict__.copy()
							a_		:	str									=   None
							return state






			def __setstate__(			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      List[Any]       )      ->      str:
							a_		:	Dict									=   d

							# for backward compatibility
							if not hasattr(self	,    '''sp_model_kwargs'''       ):
											a_		:	Optional[Any]									=   {}

							a_		:	int									=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
							self.sp_model.Load(self.vocab_file       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      List[str]:
							return self.sp_model.encode(__SCREAMING_SNAKE_CASE	,    out_type=__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      str:
							return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      Union[str, Any]:
							a_		:	Union[str, Any]									=   self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE       )
							return token






			def       SCREAMING_SNAKE_CASE   (			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      Optional[Any]:
							a_		:	Any									=   []
							a_		:	Optional[int]									=   ''''''
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE       ) + token
															a_		:	Optional[Any]									=   []
											else:
															current_sub_tokens.append(__SCREAMING_SNAKE_CASE       )
							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE       )
							return out_string.strip()







			def       SCREAMING_SNAKE_CASE   (			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Optional[str] = None       )      ->      Tuple[str]:
							if not os.path.isdir(__SCREAMING_SNAKE_CASE       ):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
											return
							a_		:	List[str]									=   os.path.join(
							    __SCREAMING_SNAKE_CASE	,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(__SCREAMING_SNAKE_CASE       ) and os.path.isfile(self.vocab_file       ):
											copyfile(self.vocab_file	,    __SCREAMING_SNAKE_CASE       )
							elif not os.path.isfile(self.vocab_file       ):
											with open(__SCREAMING_SNAKE_CASE	,    '''wb'''       ) as fi:
															a_		:	Tuple									=   self.sp_model.serialized_model_proto()
															fi.write(__SCREAMING_SNAKE_CASE       )

							return (out_vocab_file,)












'''simple docstring'''







import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__lowerCAmelCase									=   object()

# For specifying empty leaf dict `{}`
__lowerCAmelCase									=   object()





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Tuple   ):
				a_		:	List[Any]									=   tuple((re.compile(x + '''$'''   ) for x in qs)   )
				for i in range(len(__A   ) - len(__A   ) + 1   ):
								a_		:	Union[str, Any]									=   [x.match(__A   ) for x, y in zip(__A    ,		ks[i:]   )]
								if matches and all(__A   ):
												return True
				return False





def 		_UpperCAmelCase (				__A				: List[str]   ):
				def replace(__A				: int    ,		__A				: Union[str, Any]   ):
								for rule, replacement in rules:
												if _match(__A    ,		__A   ):
																return replacement
								return val

				return replace





def 		_UpperCAmelCase (				):
				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P('''mp'''    ,		__A   )),
				    (("transformer", "wte", "embedding"), P('''mp'''    ,		__A   )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__A    ,		'''mp'''   )),
				    (("attention", "out_proj", "kernel"), P('''mp'''    ,		__A   )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(__A    ,		'''mp'''   )),
				    (("mlp", "c_fc", "bias"), P('''mp'''   )),
				    (("mlp", "c_proj", "kernel"), P('''mp'''    ,		__A   )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]





def 		_UpperCAmelCase (				__A				: Union[str, Any]   ):
				a_		:	Tuple									=   _get_partition_rules()
				a_		:	Tuple									=   _replacement_rules(__A   )
				a_		:	Optional[Any]									=   {k: _unmatched for k in flatten_dict(__A   )}
				a_		:	Optional[Any]									=   {k: replace(__A    ,		__A   ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(__A   )   )




