
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

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


if is_sentencepiece_available():
					from .tokenization_mbart import MBartTokenizer
else:
					A     :	List[str]    = None


A     :	List[str]    = logging.get_logger(__name__)


A     :	int    = {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A     :	Optional[Any]    = {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

A     :	List[str]    = {
    'facebook/mbart-large-en-ro': 1_0_2_4,
    'facebook/mbart-large-cc25': 1_0_2_4,
}

# fmt: off
A     :	Optional[int]    = ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']







class 			__A( a			):
				snake_case_						   =    VOCAB_FILES_NAMES
				snake_case_						   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_						   =    PRETRAINED_VOCAB_FILES_MAP
				snake_case_						   =    ['''input_ids''', '''attention_mask''']
				snake_case_						   =    MBartTokenizer

				snake_case_						   =    []
				snake_case_						   =    []




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case="<s>"		,     _snake_case="</s>"		,     _snake_case="</s>"		,     _snake_case="<s>"		,     _snake_case="<unk>"		,     _snake_case="<pad>"		,     _snake_case="<mask>"		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case		,     )    ->      Tuple:


								'''simple docstring'''




								__a										=    AddedToken(_snake_case		,     lstrip=_snake_case		,     rstrip=_snake_case				) if isinstance(_snake_case		,     _snake_case				) else mask_token

								super().__init__(
								    vocab_file=_snake_case		,     tokenizer_file=_snake_case		,     bos_token=_snake_case		,     eos_token=_snake_case		,     sep_token=_snake_case		,     cls_token=_snake_case		,     unk_token=_snake_case		,     pad_token=_snake_case		,     mask_token=_snake_case		,     src_lang=_snake_case		,     tgt_lang=_snake_case		,     additional_special_tokens=_snake_case		,     **_snake_case		,     )

								__a										=    vocab_file
								__a										=    False if not self.vocab_file else True

								__a										=    FAIRSEQ_LANGUAGE_CODES.copy()

								if additional_special_tokens is not None:
												# Only add those special tokens if they are not already there.
												_additional_special_tokens.extend(
												    [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

								self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}				)
								__a										=    {
								    lang_code: self.convert_tokens_to_ids(_snake_case				) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								__a										=    src_lang if src_lang is not None else '''en_XX'''
								__a										=    self.convert_tokens_to_ids(self._src_lang				)
								__a										=    tgt_lang
								self.set_src_lang_special_tokens(self._src_lang				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								return self._src_lang




				@src_lang.setter
				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    new_src_lang
								self.set_src_lang_special_tokens(self._src_lang				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      List[int]:


								'''simple docstring'''




								if token_ids_a is None:
												return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      List[int]:


								'''simple docstring'''




								__a										=    [self.sep_token_id]
								__a										=    [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 + sep + token_ids_a + sep				) * [0]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case		,     _snake_case		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								if src_lang is None or tgt_lang is None:
												raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''				)
								__a										=    src_lang
								__a										=    self(_snake_case		,     add_special_tokens=_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)
								__a										=    self.convert_tokens_to_ids(_snake_case				)
								__a										=    tgt_lang_id
								return inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = "en_XX"		,     _snake_case = None		,     _snake_case = "ro_RO"		,     **_snake_case		,     )    ->      BatchEncoding:


								'''simple docstring'''




								__a										=    src_lang
								__a										=    tgt_lang
								return super().prepare_seqaseq_batch(_snake_case		,     _snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								return self.set_src_lang_special_tokens(self.src_lang				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								return self.set_tgt_lang_special_tokens(self.tgt_lang				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    self.convert_tokens_to_ids(_snake_case				)
								__a										=    []
								__a										=    [self.eos_token_id, self.cur_lang_code]

								__a										=    self.convert_ids_to_tokens(self.prefix_tokens				)
								__a										=    self.convert_ids_to_tokens(self.suffix_tokens				)

								__a										=    processors.TemplateProcessing(
								    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str		,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str		,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,     self.prefix_tokens + self.suffix_tokens				)				)		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      None:


								'''simple docstring'''




								__a										=    self.convert_tokens_to_ids(_snake_case				)
								__a										=    []
								__a										=    [self.eos_token_id, self.cur_lang_code]

								__a										=    self.convert_ids_to_tokens(self.prefix_tokens				)
								__a										=    self.convert_ids_to_tokens(self.suffix_tokens				)

								__a										=    processors.TemplateProcessing(
								    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str		,     pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str		,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str		,     self.prefix_tokens + self.suffix_tokens				)				)		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case = None				)    ->      Tuple[str]:


								'''simple docstring'''




								if not self.can_save_slow_tokenizer:
												raise ValueError(
												    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
												    '''tokenizer.'''				)

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

								if os.path.abspath(self.vocab_file				) != os.path.abspath(_snake_case				):
												copyfile(self.vocab_file		,     _snake_case				)

								return (out_vocab_file,)

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     :	str    = logging.get_logger(__name__)







class 			__A( a			):




				def __init__(     self		,     **_snake_case				)    ->      List[Any]:


								'''simple docstring'''




								requires_backends(self		,     ['''bs4''']				)
								super().__init__(**_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      int:


								'''simple docstring'''




								__a										=    []
								__a										=    []
								__a										=    element if element.name else element.parent
								for parent in child.parents:  # type: bs4.element.Tag
												__a										=    parent.find_all(child.name		,     recursive=_snake_case				)
												xpath_tags.append(child.name				)
												xpath_subscripts.append(
												    0 if 1 == len(_snake_case				) else next(i for i, s in enumerate(_snake_case		,     1				) if s is child				)				)
												__a										=    parent
								xpath_tags.reverse()
								xpath_subscripts.reverse()
								return xpath_tags, xpath_subscripts




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    BeautifulSoup(_snake_case		,     '''html.parser'''				)

								__a										=    []
								__a										=    []
								__a										=    []

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

																__a										=    html.unescape(_snake_case				).strip()
																if not text_in_this_tag:
																				continue

																all_doc_strings.append(_snake_case				)

																__a							,     __a										=    self.xpath_soup(_snake_case				)
																stringaxtag_seq.append(_snake_case				)
																stringaxsubs_seq.append(_snake_case				)

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

								return all_doc_strings, stringaxtag_seq, stringaxsubs_seq




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    ''''''
								for tagname, subs in zip(_snake_case		,     _snake_case				):
												xpath += F"""/{tagname}"""
												if subs != 0:
																xpath += F"""[{subs}]"""
								return xpath




				def __call__(     self		,     _snake_case				)    ->      BatchFeature:


								'''simple docstring'''




								__a										=    False

								# Check that strings has a valid type
								if isinstance(_snake_case		,     _snake_case				):
												__a										=    True
								elif isinstance(_snake_case		,     (list, tuple)				):
												if len(_snake_case				) == 0 or isinstance(html_strings[0]		,     _snake_case				):
																__a										=    True

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

								__a										=    bool(isinstance(_snake_case		,     (list, tuple)				) and (isinstance(html_strings[0]		,     _snake_case				))				)

								if not is_batched:
												__a										=    [html_strings]

								# Get nodes + xpaths
								__a										=    []
								__a										=    []
								for html_string in html_strings:
												__a							,     __a							,     __a										=    self.get_three_from_single(_snake_case				)
												nodes.append(_snake_case				)
												__a										=    []
												for node, tag_list, sub_list in zip(_snake_case		,     _snake_case		,     _snake_case				):
																__a										=    self.construct_xpath(_snake_case		,     _snake_case				)
																xpath_strings.append(_snake_case				)
												xpaths.append(_snake_case				)

								# return as Dict
								__a										=    {'''nodes''': nodes, '''xpaths''': xpaths}
								__a										=    BatchFeature(data=_snake_case		,     tensor_type=_snake_case				)

								return encoded_inputs