





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_nllb import NllbTokenizer
else:
					_UpperCAmelCase		:       str						    =	None


_UpperCAmelCase		:       Optional[int]						    =	logging.get_logger(__name__)


_UpperCAmelCase		:       List[Any]						    =	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase		:       Dict						    =	{
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCAmelCase		:       Any						    =	{
    """facebook/nllb-large-en-ro""": 1024,
    """facebook/nllb-200-distilled-600M""": 1024,
}

# fmt: off
_UpperCAmelCase		:       Dict						    =	["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			Union[str, Any]	      =      VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE :			Tuple	      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE :			str	      =      PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      ['''input_ids''', '''attention_mask''']
				__SCREAMING_SNAKE_CASE :			Tuple	      =      NllbTokenizer

				__SCREAMING_SNAKE_CASE :			List[int]	      =      []
				__SCREAMING_SNAKE_CASE :			List[int]	      =      []




				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=False				,       **snake_case				,       ):
											# Mask token behave like a normal word, i.e. include the space before it
											snake_case_          =   AddedToken(snake_case				,       lstrip=snake_case				,       rstrip=snake_case	) if isinstance(snake_case				,       snake_case	) else mask_token
											snake_case_          =   legacy_behaviour
											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				,       legacy_behaviour=snake_case				,       **snake_case				,       )

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

											snake_case_          =   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}	)
											snake_case_          =   {
											    lang_code: self.convert_tokens_to_ids(snake_case	) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

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




				@property
				def    a							( self	):
											return self._src_lang




				@src_lang.setter
				def    a							( self				,       snake_case	):
											snake_case_          =   new_src_lang
											self.set_src_lang_special_tokens(self._src_lang	)




				def    a							( self				,       snake_case				,       snake_case = None	):
											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    a							( self				,       snake_case				,       snake_case = None	):
											snake_case_          =   [self.sep_token_id]
											snake_case_          =   [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    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       **snake_case	):
											if src_lang is None or tgt_lang is None:
																		raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'	)
											snake_case_          =   src_lang
											snake_case_          =   self(snake_case				,       add_special_tokens=snake_case				,       return_tensors=snake_case				,       **snake_case	)
											snake_case_          =   self.convert_tokens_to_ids(snake_case	)
											snake_case_          =   tgt_lang_id
											return inputs




				def    a							( self				,       snake_case				,       snake_case = "eng_Latn"				,       snake_case = None				,       snake_case = "fra_Latn"				,       **snake_case				,       ):
											snake_case_          =   src_lang
											snake_case_          =   tgt_lang
											return super().prepare_seqaseq_batch(snake_case				,       snake_case				,       **snake_case	)




				def    a							( self	):
											return self.set_src_lang_special_tokens(self.src_lang	)




				def    a							( self	):
											return self.set_tgt_lang_special_tokens(self.tgt_lang	)




				def    a							( self				,       snake_case	):
											snake_case_          =   self.convert_tokens_to_ids(snake_case	)

											if self.legacy_behaviour:
																		snake_case_          =   []
																		snake_case_          =   [self.eos_token_id, self.cur_lang_code]
											else:
																		snake_case_          =   [self.cur_lang_code]
																		snake_case_          =   [self.eos_token_id]

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

											snake_case_          =   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    a							( self				,       snake_case	):
											snake_case_          =   self.convert_tokens_to_ids(snake_case	)
											if self.legacy_behaviour:
																		snake_case_          =   []
																		snake_case_          =   [self.eos_token_id, self.cur_lang_code]
											else:
																		snake_case_          =   [self.cur_lang_code]
																		snake_case_          =   [self.eos_token_id]

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

											snake_case_          =   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    a							( self				,       snake_case				,       snake_case = None	):
											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
											snake_case_          =   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 requests

_UpperCAmelCase		:       Union[str, Any]						    =	"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   requests.get(_NEWS_API + bbc_news_api_key				).json()
							# each article in the list is a dict
							for i, article in enumerate(bbc_news_page['articles']       ,							1				):
														print(F'''{i}.) {article["title"]}'''				)


if __name__ == "__main__":
					fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")




