
'''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


lowerCAmelCase_			:			Dict			   =     logging.get_logger(__name__)
lowerCAmelCase_			:			Optional[Any]			   =     {'''vocab_file''': '''spiece.model'''}

lowerCAmelCase_			:			Tuple			   =     {
    '''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''',
    },
}

lowerCAmelCase_			:			Union[str, Any]			   =     {
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

lowerCAmelCase_			:			List[str]			   =     '''▁'''







class 	__lowerCAmelCase    (  __a				):
     snake_case   :    List[str]							      =      VOCAB_FILES_NAMES
     snake_case   :    int							      =      PRETRAINED_VOCAB_FILES_MAP
     snake_case   :    Dict							      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case   :    int							      =      ["""input_ids""", """token_type_ids"""]



     def __init__(self						,	lowerCAmelCase__						,	lowerCAmelCase__=False						,	lowerCAmelCase__=True						,	lowerCAmelCase__=True						,	lowerCAmelCase__="<unk>"						,	lowerCAmelCase__="[SEP]"						,	lowerCAmelCase__="<pad>"						,	lowerCAmelCase__="[CLS]"						,	lowerCAmelCase__="[MASK]"						,	lowerCAmelCase__ = None						,	**lowerCAmelCase__						,	):
          # 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					:  str         =    (
              AddedToken(lowerCAmelCase__						,	lstrip=lowerCAmelCase__						,	rstrip=lowerCAmelCase__						,	normalized=lowerCAmelCase__	)
              if isinstance(lowerCAmelCase__						,	lowerCAmelCase__	)
              else mask_token
          )

          _UpperCAmelCase					:  Tuple         =    {} if sp_model_kwargs is None else sp_model_kwargs

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

          _UpperCAmelCase					:  int         =    do_lower_case
          _UpperCAmelCase					:  Optional[int]         =    remove_space
          _UpperCAmelCase					:  Optional[Any]         =    keep_accents
          _UpperCAmelCase					:  List[str]         =    vocab_file

          _UpperCAmelCase					:  Any         =    spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(lowerCAmelCase__	)



     @property
     def     snake_case_ (self	):
          return len(self.sp_model	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[str]         =    {self.convert_ids_to_tokens(lowerCAmelCase__	): i for i in range(self.vocab_size	)}
          vocab.update(self.added_tokens_encoder	)
          return vocab



     def __getstate__(self	):
          _UpperCAmelCase					:  Any         =    self.__dict__.copy()
          _UpperCAmelCase					:  str         =    None
          return state



     def __setstate__(self						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Union[str, Any]         =    d

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

          _UpperCAmelCase					:  Any         =    spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(self.vocab_file	)



     def     snake_case_ (self						,	lowerCAmelCase__	):
          if self.remove_space:
               _UpperCAmelCase					:  Tuple         =    """ """.join(inputs.strip().split()	)
          else:
               _UpperCAmelCase					:  int         =    inputs
          _UpperCAmelCase					:  Optional[int]         =    outputs.replace("""``"""						,	"""\""""	).replace("""''"""						,	"""\""""	)

          if not self.keep_accents:
               _UpperCAmelCase					:  Any         =    unicodedata.normalize("""NFKD"""						,	lowerCAmelCase__	)
               _UpperCAmelCase					:  str         =    """""".join([c for c in outputs if not unicodedata.combining(lowerCAmelCase__	)]	)
          if self.do_lower_case:
               _UpperCAmelCase					:  Dict         =    outputs.lower()

          return outputs



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

          return new_pieces



     def     snake_case_ (self						,	lowerCAmelCase__	):
          return self.sp_model.PieceToId(lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__	):
          return self.sp_model.IdToPiece(lowerCAmelCase__	)



     def     snake_case_ (self						,	lowerCAmelCase__	):
          _UpperCAmelCase					:  Optional[int]         =    []
          _UpperCAmelCase					:  Dict         =    """"""
          _UpperCAmelCase					:  Dict         =    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(lowerCAmelCase__	) + token
                    _UpperCAmelCase					:  str         =    True
                    _UpperCAmelCase					:  List[Any]         =    []
               else:
                    current_sub_tokens.append(lowerCAmelCase__	)
                    _UpperCAmelCase					:  Union[str, Any]         =    False
          out_string += self.sp_model.decode(lowerCAmelCase__	)
          return out_string.strip()



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = False						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = True						,	**lowerCAmelCase__						,	):
          _UpperCAmelCase					:  List[Any]         =    kwargs.pop("""use_source_tokenizer"""						,	lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[int]         =    self.convert_ids_to_tokens(lowerCAmelCase__						,	skip_special_tokens=lowerCAmelCase__	)

          # 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					:  Optional[Any]         =    []
          _UpperCAmelCase					:  Optional[int]         =    []
          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(lowerCAmelCase__	)	)
                         _UpperCAmelCase					:  int         =    []
                    sub_texts.append(lowerCAmelCase__	)
               else:
                    current_sub_text.append(lowerCAmelCase__	)
          if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__	)	)

          # Mimic the behavior of the Rust tokenizer:
          # No space after <unk>
          if spaces_between_special_tokens:
               _UpperCAmelCase					:  Union[str, Any]         =    re.sub(r"""(<unk>) """						,	r"""\1"""						,	""" """.join(lowerCAmelCase__	)	)
          else:
               _UpperCAmelCase					:  Optional[Any]         =    """""".join(lowerCAmelCase__	)

          _UpperCAmelCase					:  Optional[int]         =    (
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
               _UpperCAmelCase					:  int         =    self.clean_up_tokenization(lowerCAmelCase__	)
               return clean_text
          else:
               return text



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None	):
          _UpperCAmelCase					:  Optional[Any]         =    [self.sep_token_id]
          _UpperCAmelCase					:  Any         =    [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     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None						,	lowerCAmelCase__ = False	):

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

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



     def     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None	):
          _UpperCAmelCase					:  Dict         =    [self.sep_token_id]
          _UpperCAmelCase					:  Union[str, Any]         =    [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     snake_case_ (self						,	lowerCAmelCase__						,	lowerCAmelCase__ = None	):
          if not os.path.isdir(lowerCAmelCase__	):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
               return
          _UpperCAmelCase					:  Dict         =    os.path.join(
              lowerCAmelCase__						,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

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

          return (out_vocab_file,)




'''simple docstring'''

import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
   import torch







class 	__lowerCAmelCase    (  unittest.TestCase       ,					__a				):



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Tuple         =    load_tool("""speech-to-text"""	)
          self.tool.setup()



     def     snake_case_ (self	):
          _UpperCAmelCase					:  str         =    self.tool(torch.ones(3_0_0_0	)	)
          self.assertEqual(lowerCAmelCase__						,	""" you"""	)



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Union[str, Any]         =    self.tool(audio=torch.ones(3_0_0_0	)	)
          self.assertEqual(lowerCAmelCase__						,	""" you"""	)


