


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

import sentencepiece as spm

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


A							:					Optional[int]     						=     logging.get_logger(__name__)

A							:					str     						=     {'vocab_file': 'sentencepiece.model'}

A							:					Tuple     						=     {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

A							:					Union[str, Any]     						=     {
    'google/rembert': 256,
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				int										=	VOCAB_FILES_NAMES
					snake_case_		:				List[str]										=	PRETRAINED_VOCAB_FILES_MAP
					snake_case_		:				Any										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


					def __init__(						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=False     ,    SCREAMING_SNAKE_CASE	:		List[str]=True     ,    SCREAMING_SNAKE_CASE	:		Tuple=True     ,    SCREAMING_SNAKE_CASE	:		Optional[int]="[CLS]"     ,    SCREAMING_SNAKE_CASE	:		str="[SEP]"     ,    SCREAMING_SNAKE_CASE	:		str="[UNK]"     ,    SCREAMING_SNAKE_CASE	:		Any="[SEP]"     ,    SCREAMING_SNAKE_CASE	:		List[str]="[PAD]"     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]="[CLS]"     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="[MASK]"     ,    **SCREAMING_SNAKE_CASE	:		Dict     ,    )			->	Union[str, Any]:




											'''simple docstring'''

											super().__init__(
											    do_lower_case=SCREAMING_SNAKE_CASE     ,    remove_space=SCREAMING_SNAKE_CASE     ,    keep_accents=SCREAMING_SNAKE_CASE     ,    bos_token=SCREAMING_SNAKE_CASE     ,    eos_token=SCREAMING_SNAKE_CASE     ,    unk_token=SCREAMING_SNAKE_CASE     ,    sep_token=SCREAMING_SNAKE_CASE     ,    pad_token=SCREAMING_SNAKE_CASE     ,    cls_token=SCREAMING_SNAKE_CASE     ,    mask_token=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    )

											__snake_case				=		do_lower_case
											__snake_case				=		remove_space
											__snake_case				=		keep_accents
											__snake_case				=		vocab_file

											__snake_case				=		spm.SentencePieceProcessor()
											self.sp_model.Load(SCREAMING_SNAKE_CASE						)


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Any:




											'''simple docstring'''

											return len(self.sp_model						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		{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	:		Any						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.__dict__.copy()
											__snake_case				=		None
											return state


					def __setstate__(						self	:		str     ,    SCREAMING_SNAKE_CASE	:		int						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		d
											__snake_case				=		spm.SentencePieceProcessor()
											self.sp_model.Load(self.vocab_file						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=False						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		self.sp_model.EncodeAsPieces(SCREAMING_SNAKE_CASE						)
											return pieces


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	List[str]:




											'''simple docstring'''

											return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	Optional[int]:




											'''simple docstring'''

											return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.sp_model.decode_pieces(SCREAMING_SNAKE_CASE						)
											return out_string


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		List[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[List[int]] = None						)			->	List[int]:




											'''simple docstring'''

											__snake_case				=		[self.sep_token_id]
											__snake_case				=		[self.cls_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 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		List[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[List[int]] = None     ,    SCREAMING_SNAKE_CASE	:		bool = False						)			->	List[int]:




											'''simple docstring'''

											if already_has_special_tokens:
																	if token_ids_a is not None:
																							raise ValueError(
																							    "You should not supply a second sequence if the provided sequence of "
																							    "ids is already formatted with special tokens for the model."						)
																	return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

											if token_ids_a is not None:
																	return [1] + ([0] * len(SCREAMING_SNAKE_CASE						)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE						)) + [1]
											return [1] + ([0] * len(SCREAMING_SNAKE_CASE						)) + [1]


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		List[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[List[int]] = None						)			->	List[int]:




											'''simple docstring'''

											__snake_case				=		[self.sep_token_id]
											__snake_case				=		[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 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[str] = None						)			->	Tuple[str]:




											'''simple docstring'''

											if not os.path.isdir(SCREAMING_SNAKE_CASE						):
																	logger.error("Vocabulary path ({}) should be a directory".format(SCREAMING_SNAKE_CASE						)						)
																	return
											__snake_case				=		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						):
																	copyfile(self.vocab_file     ,    SCREAMING_SNAKE_CASE						)

											return (out_vocab_file,)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


A							:					str     						=     logging.get_logger(__name__)

A							:					str     						=     {
    'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
    'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""fnet"""


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=3_2_0_0_0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Any=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu_new"     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    **SCREAMING_SNAKE_CASE	:		List[Any]     ,    )			->	List[Any]:




											'''simple docstring'''

											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,    bos_token_id=SCREAMING_SNAKE_CASE     ,    eos_token_id=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		type_vocab_size
											__snake_case				=		layer_norm_eps
											__snake_case				=		use_tpu_fourier_optimizations
											__snake_case				=		tpu_short_seq_length





