

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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a_         =      logging.get_logger(__name__)
a_         =      {'vocab_file': 'spiece.model'}

a_         =      {
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
}

a_         =      {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

a_         =      '▁'






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          =VOCAB_FILES_NAMES
			lowerCamelCase__          =PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__          =['input_ids', 'token_type_ids']
			def __init__(     self							:	List[str] , a							:	Dict , a							:	List[Any]=False , a							:	Dict=True , a							:	int=True , a							:	str="<unk>" , a							:	Optional[Any]="[SEP]" , a							:	int="<pad>" , a							:	Optional[int]="[CLS]" , a							:	Union[str, Any]="[MASK]" , a							:	Optional[Dict[str, Any]] = None , **a							:	List[str] , )							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    (
				    AddedToken(a , lstrip=a , rstrip=a , normalized=a		)
				    if isinstance(a , a		)
				    else mask_token
				)

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=a , remove_space=a , keep_accents=a , unk_token=a , sep_token=a , pad_token=a , cls_token=a , mask_token=a , sp_model_kwargs=self.sp_model_kwargs , **a , )

				SCREAMING_SNAKE_CASE				: Dict			       =    do_lower_case
				SCREAMING_SNAKE_CASE				: Dict			       =    remove_space
				SCREAMING_SNAKE_CASE				: Any			       =    keep_accents
				SCREAMING_SNAKE_CASE				: str			       =    vocab_file

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(a		)
			@property
			def 							__UpperCamelCase	(     self							:	Any		)							->    Dict:

				"""simple docstring"""

				return len(self.sp_model		)
			def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    {self.convert_ids_to_tokens(a		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab
			def __getstate__(     self							:	Tuple		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.__dict__.copy()
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    None
				return state
			def __setstate__(     self							:	int , a							:	List[str]		)							->    Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    d

				# for backward compatibility
				if not hasattr(self , "sp_model_kwargs"		):
					SCREAMING_SNAKE_CASE				: List[Any]			       =    {}

				SCREAMING_SNAKE_CASE				: List[str]			       =    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(self.vocab_file		)
			def 							__UpperCamelCase	(     self							:	str , a							:	str		)							->    Any:

				"""simple docstring"""

				if self.remove_space:
					SCREAMING_SNAKE_CASE				: List[str]			       =    " ".join(inputs.strip().split()		)
				else:
					SCREAMING_SNAKE_CASE				: List[str]			       =    inputs
				SCREAMING_SNAKE_CASE				: str			       =    outputs.replace("``" , "\""		).replace("''" , "\""		)

				if not self.keep_accents:
					SCREAMING_SNAKE_CASE				: int			       =    unicodedata.normalize("NFKD" , a		)
					SCREAMING_SNAKE_CASE				: List[str]			       =    "".join([c for c in outputs if not unicodedata.combining(a		)]		)
				if self.do_lower_case:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    outputs.lower()

				return outputs
			def 							__UpperCamelCase	(     self							:	str , a							:	str		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.preprocess_text(a		)
				SCREAMING_SNAKE_CASE				: Tuple			       =    self.sp_model.encode(a , out_type=a		)
				SCREAMING_SNAKE_CASE				: Any			       =    []
				for piece in pieces:
					if len(a		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
						SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.sp_model.EncodeAsPieces(piece[:-1].replace(a , ""		)		)
						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
							if len(cur_pieces[0]		) == 1:
								SCREAMING_SNAKE_CASE				: Any			       =    cur_pieces[1:]
							else:
								SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    cur_pieces[0][1:]
						cur_pieces.append(piece[-1]		)
						new_pieces.extend(a		)
					else:
						new_pieces.append(a		)

				return new_pieces
			def 							__UpperCamelCase	(     self							:	Optional[Any] , a							:	Optional[Any]		)							->    Tuple:

				"""simple docstring"""

				return self.sp_model.PieceToId(a		)
			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	Optional[Any]		)							->    Any:

				"""simple docstring"""

				return self.sp_model.IdToPiece(a		)
			def 							__UpperCamelCase	(     self							:	int , a							:	Any		)							->    Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    []
				SCREAMING_SNAKE_CASE				: Any			       =    ""
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    False
				for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
						if not prev_is_special:
							out_string += " "
						out_string += self.sp_model.decode(a		) + token
						SCREAMING_SNAKE_CASE				: List[Any]			       =    True
						SCREAMING_SNAKE_CASE				: List[Any]			       =    []
					else:
						current_sub_tokens.append(a		)
						SCREAMING_SNAKE_CASE				: List[str]			       =    False
				out_string += self.sp_model.decode(a		)
				return out_string.strip()
			def 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	List[int] , a							:	bool = False , a							:	bool = None , a							:	bool = True , **a							:	Any , )							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    kwargs.pop("use_source_tokenizer" , a		)

				SCREAMING_SNAKE_CASE				: str			       =    self.convert_ids_to_tokens(a , skip_special_tokens=a		)

				# To avoid mixing byte-level and unicode for byte-level BPT
				# we need to build string separately for added tokens and byte-level tokens
				# cf. https://github.com/huggingface/transformers/issues/1133
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    []
				SCREAMING_SNAKE_CASE				: List[Any]			       =    []
				for token in filtered_tokens:
					if skip_special_tokens and token in self.all_special_ids:
						continue
					if token in self.added_tokens_encoder:
						if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(a		)		)
							SCREAMING_SNAKE_CASE				: List[str]			       =    []
						sub_texts.append(a		)
					else:
						current_sub_text.append(a		)
				if current_sub_text:
					sub_texts.append(self.convert_tokens_to_string(a		)		)

				# Mimic the behavior of the Rust tokenizer:
				# No space after <unk>
				if spaces_between_special_tokens:
					SCREAMING_SNAKE_CASE				: List[Any]			       =    re.sub(R"(<unk>) " , R"\1" , " ".join(a		)		)
				else:
					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    "".join(a		)

				SCREAMING_SNAKE_CASE				: int			       =    (
				    clean_up_tokenization_spaces
				    if clean_up_tokenization_spaces is not None
				    else self.clean_up_tokenization_spaces
				)
				if clean_up_tokenization_spaces:
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.clean_up_tokenization(a		)
					return clean_text
				else:
					return text
			def 							__UpperCamelCase	(     self							:	int , a							:	List[int] , a							:	Optional[List[int]] = None		)							->    List[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    [self.sep_token_id]
				SCREAMING_SNAKE_CASE				: Tuple			       =    [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 							__UpperCamelCase	(     self							:	List[Any] , a							:	List[int] , a							:	Optional[List[int]] = None , a							:	bool = False		)							->    List[int]:

				"""simple docstring"""

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

				if token_ids_a is not None:
					return [1] + ([0] * len(a		)) + [1] + ([0] * len(a		)) + [1]
				return [1] + ([0] * len(a		)) + [1]
			def 							__UpperCamelCase	(     self							:	Optional[Any] , a							:	List[int] , a							:	Optional[List[int]] = None		)							->    List[int]:

				"""simple docstring"""

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

				"""simple docstring"""

				if not os.path.isdir(a		):
					logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
					return
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    os.path.join(
				    a , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(a		) and os.path.isfile(self.vocab_file		):
					copyfile(self.vocab_file , a		)
				elif not os.path.isfile(self.vocab_file		):
					with open(a , "wb"		) as fi:
						SCREAMING_SNAKE_CASE				: str			       =    self.sp_model.serialized_model_proto()
						fi.write(a		)

				return (out_vocab_file,)


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


a_         =      logging.get_logger(__name__)

a_         =      {
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          ='mpnet'
			def __init__(     self							:	Optional[int] , a							:	Dict=3_0527 , a							:	Optional[int]=768 , a							:	List[str]=12 , a							:	List[str]=12 , a							:	str=3072 , a							:	Optional[int]="gelu" , a							:	Tuple=0.1 , a							:	Optional[int]=0.1 , a							:	Optional[Any]=512 , a							:	List[Any]=0.02 , a							:	str=1e-12 , a							:	Optional[Any]=32 , a							:	int=1 , a							:	Union[str, Any]=0 , a							:	List[str]=2 , **a							:	Optional[int] , )							->    Union[str, Any]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE				: Tuple			       =    vocab_size
				SCREAMING_SNAKE_CASE				: Any			       =    hidden_size
				SCREAMING_SNAKE_CASE				: int			       =    num_hidden_layers
				SCREAMING_SNAKE_CASE				: Any			       =    num_attention_heads
				SCREAMING_SNAKE_CASE				: Tuple			       =    hidden_act
				SCREAMING_SNAKE_CASE				: Tuple			       =    intermediate_size
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    hidden_dropout_prob
				SCREAMING_SNAKE_CASE				: List[str]			       =    attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE				: Dict			       =    max_position_embeddings
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    initializer_range
				SCREAMING_SNAKE_CASE				: Any			       =    layer_norm_eps
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    relative_attention_num_buckets