'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_fnet import FNetTokenizer
else:
  a=  None

a=  logging.get_logger(__name__)
a=  {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

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''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

a=  {
    '''google/fnet-base''': 5_1_2,
    '''google/fnet-large''': 5_1_2,
}

a=  '''▁'''






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''token_type_ids''']
     SCREAMING_SNAKE_CASE__		   =						FNetTokenizer







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

         super().__init__(
             _lowerCamelCase     ,			tokenizer_file=_lowerCamelCase     ,			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     ,			**_lowerCamelCase     ,			)

         __UpperCamelCase	:	Optional[int]		    =  do_lower_case
         __UpperCamelCase	:	int		    =  remove_space
         __UpperCamelCase	:	Dict		    =  keep_accents
         __UpperCamelCase	:	List[Any]		    =  vocab_file
         __UpperCamelCase	:	Optional[Any]		    =  False if not self.vocab_file else True







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  [self.sep_token_id]
         __UpperCamelCase	:	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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	Tuple		    =  [self.sep_token_id]
         __UpperCamelCase	:	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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         if not os.path.isdir(_lowerCamelCase    ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
             return
         __UpperCamelCase	:	List[str]		    =  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    ):
             copyfile(self.vocab_file     ,			_lowerCamelCase    )

         return (out_vocab_file,)




'''simple docstring'''
import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
  import bsa
  from bsa import BeautifulSoup


a=  logging.get_logger(__name__)






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     def __init__(		self     ,			**_lowerCamelCase    ):
         requires_backends(self     ,			['bs4']    )
         super().__init__(**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	str		    =  []
         __UpperCamelCase	:	Dict		    =  []
         __UpperCamelCase	:	Union[str, Any]		    =  element if element.name else element.parent
         for parent in child.parents:  # type: bs4.element.Tag
             __UpperCamelCase	:	str		    =  parent.find_all(child.name     ,			recursive=_lowerCamelCase    )
             xpath_tags.append(child.name    )
             xpath_subscripts.append(
                 0 if 1 == len(_lowerCamelCase    ) else next(i for i, s in enumerate(_lowerCamelCase     ,			1    ) if s is child    )    )
             __UpperCamelCase	:	Union[str, Any]		    =  parent
         xpath_tags.reverse()
         xpath_subscripts.reverse()
         return xpath_tags, xpath_subscripts







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  BeautifulSoup(_lowerCamelCase     ,			'html.parser'    )

         __UpperCamelCase	:	int		    =  []
         __UpperCamelCase	:	List[Any]		    =  []
         __UpperCamelCase	:	int		    =  []

         for element in html_code.descendants:
             if type(_lowerCamelCase    ) == bsa.element.NavigableString:
                 if type(element.parent    ) != bsa.element.Tag:
                     continue

                 __UpperCamelCase	:	Tuple		    =  html.unescape(_lowerCamelCase    ).strip()
                 if not text_in_this_tag:
                     continue

                 all_doc_strings.append(_lowerCamelCase    )

                 __UpperCamelCase    ,					__UpperCamelCase	:	Union[str, Any]		    =  self.xpath_soup(_lowerCamelCase    )
                 stringaxtag_seq.append(_lowerCamelCase    )
                 stringaxsubs_seq.append(_lowerCamelCase    )

         if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
             raise ValueError('Number of doc strings and xtags does not correspond'    )
         if len(_lowerCamelCase    ) != len(_lowerCamelCase    ):
             raise ValueError('Number of doc strings and xsubs does not correspond'    )

         return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  ''
         for tagname, subs in zip(_lowerCamelCase     ,			_lowerCamelCase    ):
             xpath += f"""/{tagname}"""
             if subs != 0:
                 xpath += f"""[{subs}]"""
         return xpath







     def __call__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[Any]		    =  False

         # Check that strings has a valid type
         if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	List[str]		    =  True
         elif isinstance(_lowerCamelCase     ,			(list, tuple)    ):
             if len(_lowerCamelCase    ) == 0 or isinstance(html_strings[0]     ,			_lowerCamelCase    ):
                 __UpperCamelCase	:	Optional[int]		    =  True

         if not valid_strings:
             raise ValueError(
                 'HTML strings must of type `str`, `List[str]` (batch of examples), '
                 f"""but is of type {type(_lowerCamelCase    )}."""    )

         __UpperCamelCase	:	Dict		    =  bool(isinstance(_lowerCamelCase     ,			(list, tuple)    ) and (isinstance(html_strings[0]     ,			_lowerCamelCase    ))    )

         if not is_batched:
             __UpperCamelCase	:	Tuple		    =  [html_strings]

         # Get nodes + xpaths
         __UpperCamelCase	:	Tuple		    =  []
         __UpperCamelCase	:	Tuple		    =  []
         for html_string in html_strings:
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	str		    =  self.get_three_from_single(_lowerCamelCase    )
             nodes.append(_lowerCamelCase    )
             __UpperCamelCase	:	Union[str, Any]		    =  []
             for node, tag_list, sub_list in zip(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
                 __UpperCamelCase	:	Optional[Any]		    =  self.construct_xpath(_lowerCamelCase     ,			_lowerCamelCase    )
                 xpath_strings.append(_lowerCamelCase    )
             xpaths.append(_lowerCamelCase    )

         # return as Dict
         __UpperCamelCase	:	Optional[int]		    =  {'nodes': nodes, 'xpaths': xpaths}
         __UpperCamelCase	:	Union[str, Any]		    =  BatchFeature(data=_lowerCamelCase     ,			tensor_type=_lowerCamelCase    )

         return encoded_inputs



