

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


SCREAMING_SNAKE_CASE		:Optional[int]				  =    logging.get_logger(__name__)
SCREAMING_SNAKE_CASE		:Optional[int]				  =    {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE		:int				  =    {
    """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""",
    },
}

SCREAMING_SNAKE_CASE		:int				  =    {
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

SCREAMING_SNAKE_CASE		:Tuple				  =    """▁"""






class 	__magic_name__				(       snake_case							):
     UpperCamelCase_  :Tuple								=			VOCAB_FILES_NAMES
     UpperCamelCase_  :Union[str, Any]								=			PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_  :Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_  :str								=			["""input_ids""", """token_type_ids"""]






     def __init__(							self		,	_lowercase		,	_lowercase=False		,	_lowercase=True		,	_lowercase=True		,	_lowercase="<unk>"		,	_lowercase="[SEP]"		,	_lowercase="<pad>"		,	_lowercase="[CLS]"		,	_lowercase="[MASK]"		,	_lowercase = None		,	**_lowercase		,	)->    None:
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.
         UpperCamelCase_      =	(
             AddedToken(_lowercase		,	lstrip=_lowercase		,	rstrip=_lowercase		,	normalized=_lowercase  )
             if isinstance(_lowercase		,	_lowercase  )
             else mask_token
         )

         UpperCamelCase_      =	{} if sp_model_kwargs is None else sp_model_kwargs

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

         UpperCamelCase_      =	do_lower_case
         UpperCamelCase_      =	remove_space
         UpperCamelCase_      =	keep_accents
         UpperCamelCase_      =	vocab_file

         UpperCamelCase_      =	spm.SentencePieceProcessor(**self.sp_model_kwargs  )
         self.sp_model.Load(_lowercase  )






     @property
     def   UpperCAmelCase_							(							self  )->    Optional[int]:
         return len(self.sp_model  )






     def   UpperCAmelCase_							(							self  )->    List[str]:
         UpperCamelCase_      =	{self.convert_ids_to_tokens(_lowercase  ): i for i in range(self.vocab_size  )}
         vocab.update(self.added_tokens_encoder  )
         return vocab






     def __getstate__(							self  )->    List[str]:
         UpperCamelCase_      =	self.__dict__.copy()
         UpperCamelCase_      =	None
         return state






     def __setstate__(							self		,	_lowercase  )->    Dict:
         UpperCamelCase_      =	d

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

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






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    Union[str, Any]:
         if self.remove_space:
             UpperCamelCase_      =	" ".join(inputs.strip().split()  )
         else:
             UpperCamelCase_      =	inputs
         UpperCamelCase_      =	outputs.replace("``"		,	"\""  ).replace("''"		,	"\""  )

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

         return outputs






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    List[str]:
         UpperCamelCase_      =	self.preprocess_text(_lowercase  )
         UpperCamelCase_      =	self.sp_model.encode(_lowercase		,	out_type=_lowercase  )
         UpperCamelCase_      =	[]
         for piece in pieces:
             if len(_lowercase  ) > 1 and piece[-1] == str(","  ) and piece[-2].isdigit():
                 UpperCamelCase_      =	self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowercase		,	""  )  )
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                     if len(cur_pieces[0]  ) == 1:
                         UpperCamelCase_      =	cur_pieces[1:]
                     else:
                         UpperCamelCase_      =	cur_pieces[0][1:]
                 cur_pieces.append(piece[-1]  )
                 new_pieces.extend(_lowercase  )
             else:
                 new_pieces.append(_lowercase  )

         return new_pieces






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    List[Any]:
         return self.sp_model.PieceToId(_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    Tuple:
         return self.sp_model.IdToPiece(_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase  )->    Any:
         UpperCamelCase_      =	[]
         UpperCamelCase_      =	""
         UpperCamelCase_      =	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(_lowercase  ) + token
                 UpperCamelCase_      =	True
                 UpperCamelCase_      =	[]
             else:
                 current_sub_tokens.append(_lowercase  )
                 UpperCamelCase_      =	False
         out_string += self.sp_model.decode(_lowercase  )
         return out_string.strip()






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = False		,	_lowercase = None		,	_lowercase = True		,	**_lowercase		,	)->    str:
         UpperCamelCase_      =	kwargs.pop("use_source_tokenizer"		,	_lowercase  )

         UpperCamelCase_      =	self.convert_ids_to_tokens(_lowercase		,	skip_special_tokens=_lowercase  )

         # 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
         UpperCamelCase_      =	[]
         UpperCamelCase_      =	[]
         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(_lowercase  )  )
                     UpperCamelCase_      =	[]
                 sub_texts.append(_lowercase  )
             else:
                 current_sub_text.append(_lowercase  )
         if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(_lowercase  )  )

         # Mimic the behavior of the Rust tokenizer:
         # No space after <unk>
         if spaces_between_special_tokens:
             UpperCamelCase_      =	re.sub(r"(<unk>) "		,	r"\1"		,	" ".join(_lowercase  )  )
         else:
             UpperCamelCase_      =	"".join(_lowercase  )

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






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = None  )->    List[int]:
         UpperCamelCase_      =	[self.sep_token_id]
         UpperCamelCase_      =	[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		,	_lowercase		,	_lowercase = None		,	_lowercase = False  )->    List[int]:

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

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






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase = None  )->    List[int]:
         UpperCamelCase_      =	[self.sep_token_id]
         UpperCamelCase_      =	[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		,	_lowercase		,	_lowercase = None  )->    Tuple[str]:
         if not os.path.isdir(_lowercase  ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
             return
         UpperCamelCase_      =	os.path.join(
             _lowercase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

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

         return (out_vocab_file,)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict				  =    ["""T5Tokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str				  =    ["""T5TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[Any]				  =    [
     """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """T5EncoderModel""",
     """T5ForConditionalGeneration""",
     """T5Model""",
     """T5PreTrainedModel""",
     """load_tf_weights_in_t5""",
     """T5ForQuestionAnswering""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:int				  =    [
     """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFT5EncoderModel""",
     """TFT5ForConditionalGeneration""",
     """TFT5Model""",
     """TFT5PreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[str]				  =    [
     """FlaxT5EncoderModel""",
     """FlaxT5ForConditionalGeneration""",
     """FlaxT5Model""",
     """FlaxT5PreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta import TaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta_fast import TaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ta import (
      T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TaEncoderModel,
      TaForConditionalGeneration,
      TaForQuestionAnswering,
      TaModel,
      TaPreTrainedModel,
      load_tf_weights_in_ta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ta import (
      TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTaEncoderModel,
      TFTaForConditionalGeneration,
      TFTaModel,
      TFTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_ta import (
      FlaxTaEncoderModel,
      FlaxTaForConditionalGeneration,
      FlaxTaModel,
      FlaxTaPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]				  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
