


'''simple docstring'''





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


UpperCAmelCase      :						str    =		logging.get_logger(__name__)
UpperCAmelCase      :						Union[str, Any]    =		{"""vocab_file""": """spiece.model"""}

UpperCAmelCase      :						List[Any]    =		{
    """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""",
    },
}

UpperCAmelCase      :						Any    =		{
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

UpperCAmelCase      :						Optional[int]    =		"""▁"""







class 					__lowercase					(       _SCREAMING_SNAKE_CASE     ):



		"""simple docstring"""

		UpperCamelCase    :				Union[str, Any] 					=		VOCAB_FILES_NAMES
		UpperCamelCase    :				List[Any] 					=		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase    :				Dict 					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase    :				str 					=		["input_ids", "token_type_ids"]







		def __init__(   self		,			A		,			A=False		,			A=True		,			A=True		,			A="<unk>"		,			A="[SEP]"		,			A="<pad>"		,			A="[CLS]"		,			A="[MASK]"		,			A = None		,			**A		,			)     ->   None:







					'''simple docstring'''







					lowerCamelCase								=     (
					    AddedToken(__A		,			lstrip=__A		,			rstrip=__A		,			normalized=__A  )
					    if isinstance(__A		,			__A  )
					    else mask_token
					)

					lowerCamelCase								=     {} 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		,			)

					lowerCamelCase								=     do_lower_case
					lowerCamelCase								=     remove_space
					lowerCamelCase								=     keep_accents
					lowerCamelCase								=     vocab_file

					lowerCamelCase								=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
					self.sp_model.Load(__A  )







		@property
		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					return len(self.sp_model  )







		def 				__A      (   self  )     ->   int:







					'''simple docstring'''







					lowerCamelCase								=     {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  )     ->   List[Any]:







					'''simple docstring'''







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







		def __setstate__(   self		,			A  )     ->   Any:







					'''simple docstring'''







					lowerCamelCase								=     d

					# for backward compatibility
					if not hasattr(self		,			"""sp_model_kwargs"""  ):
								lowerCamelCase								=     {}

					lowerCamelCase								=     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
					self.sp_model.Load(self.vocab_file  )







		def 				__A      (   self		,			A  )     ->   Optional[Any]:







					'''simple docstring'''







					if self.remove_space:
								lowerCamelCase								=     """ """.join(inputs.strip().split()  )
					else:
								lowerCamelCase								=     inputs
					lowerCamelCase								=     outputs.replace("""``"""		,			"""\""""  ).replace("""\'\'"""		,			"""\""""  )

					if not self.keep_accents:
								lowerCamelCase								=     unicodedata.normalize("""NFKD"""		,			__A  )
								lowerCamelCase								=     """""".join([c for c in outputs if not unicodedata.combining(__A  )]  )
					if self.do_lower_case:
								lowerCamelCase								=     outputs.lower()

					return outputs







		def 				__A      (   self		,			A  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     self.preprocess_text(__A  )
					lowerCamelCase								=     self.sp_model.encode(__A		,			out_type=__A  )
					lowerCamelCase								=     []
					for piece in pieces:
								if len(__A  ) > 1 and piece[-1] == str(""","""  ) and piece[-2].isdigit():
											lowerCamelCase								=     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:
																	lowerCamelCase								=     cur_pieces[1:]
														else:
																	lowerCamelCase								=     cur_pieces[0][1:]
											cur_pieces.append(piece[-1]  )
											new_pieces.extend(__A  )
								else:
											new_pieces.append(__A  )

					return new_pieces







		def 				__A      (   self		,			A  )     ->   Dict:







					'''simple docstring'''







					return self.sp_model.PieceToId(__A  )







		def 				__A      (   self		,			A  )     ->   Dict:







					'''simple docstring'''







					return self.sp_model.IdToPiece(__A  )







		def 				__A      (   self		,			A  )     ->   int:







					'''simple docstring'''







					lowerCamelCase								=     []
					lowerCamelCase								=     """"""
					lowerCamelCase								=     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
											lowerCamelCase								=     True
											lowerCamelCase								=     []
								else:
											current_sub_tokens.append(__A  )
											lowerCamelCase								=     False
					out_string += self.sp_model.decode(__A  )
					return out_string.strip()







		def 				__A      (   self		,			A		,			A = False		,			A = None		,			A = True		,			**A		,			)     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     kwargs.pop("""use_source_tokenizer"""		,			__A  )

					lowerCamelCase								=     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
					lowerCamelCase								=     []
					lowerCamelCase								=     []
					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  )  )
														lowerCamelCase								=     []
											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:
								lowerCamelCase								=     re.sub(r"""(<unk>) """		,			r"""\1"""		,			""" """.join(__A  )  )
					else:
								lowerCamelCase								=     """""".join(__A  )

					lowerCamelCase								=     (
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
								lowerCamelCase								=     self.clean_up_tokenization(__A  )
								return clean_text
					else:
								return text







		def 				__A      (   self		,			A		,			A = None  )     ->   List[int]:







					'''simple docstring'''







					lowerCamelCase								=     [self.sep_token_id]
					lowerCamelCase								=     [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 				__A      (   self		,			A		,			A = None		,			A = 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 				__A      (   self		,			A		,			A = None  )     ->   List[int]:







					'''simple docstring'''







					lowerCamelCase								=     [self.sep_token_id]
					lowerCamelCase								=     [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		,			A		,			A = None  )     ->   Tuple[str]:







					'''simple docstring'''







					if not os.path.isdir(__A  ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'  )
								return
					lowerCamelCase								=     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:
											lowerCamelCase								=     self.sp_model.serialized_model_proto()
											fi.write(__A  )

					return (out_vocab_file,)





import warnings

from .generation import FlaxGenerationMixin







class 					__lowercase					(       a_     ):



		"""simple docstring"""

		warnings.warn(
		    "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
		    "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead." ,       a_ ,       )

