


'''simple docstring'''


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_		:   Optional[int]							   =		None


UpperCAmelCase_		:   str							   =		logging.get_logger(__name__)


UpperCAmelCase_		:   Union[str, Any]							   =		{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_		:   Any							   =		{
    '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_		:   Union[str, Any]							   =		{
    'facebook/nllb-large-en-ro': 1024,
    'facebook/nllb-200-distilled-600M': 1024,
}

# fmt: off
UpperCAmelCase_		:   List[str]							   =		['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__  (						_snake_case					):





							'''simple docstring'''


							A_							:      List[Any]      	=  VOCAB_FILES_NAMES
							A_							:      Optional[int]      	=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_							:      Optional[int]      	=  PRETRAINED_VOCAB_FILES_MAP
							A_							:      Any      	=  ["""input_ids""", """attention_mask"""]
							A_							:      Tuple      	=  NllbTokenizer

							A_							:      List[int]      	=  []
							A_							:      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
											_SCREAMING_SNAKE_CASE :       int     		=      AddedToken(__snake_case    ,					lstrip=__snake_case    ,					rstrip=__snake_case  ) if isinstance(__snake_case    ,					__snake_case  ) else mask_token
											_SCREAMING_SNAKE_CASE :       List[str]     		=      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    ,					)

											_SCREAMING_SNAKE_CASE :       int     		=      vocab_file
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      False if not self.vocab_file else True

											_SCREAMING_SNAKE_CASE :       str     		=      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}  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      {
											    lang_code: self.convert_tokens_to_ids(__snake_case  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      src_lang if src_lang is not None else """eng_Latn"""
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.convert_tokens_to_ids(self._src_lang  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      tgt_lang
											self.set_src_lang_special_tokens(self._src_lang  )






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






							@src_lang.setter
							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      new_src_lang
											self.set_src_lang_special_tokens(self._src_lang  )






							def UpperCAmelCase_	(    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 UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = None  ):
											_SCREAMING_SNAKE_CASE :       int     		=      [self.sep_token_id]
											_SCREAMING_SNAKE_CASE :       str     		=      [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 UpperCAmelCase_	(    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"""  )
											_SCREAMING_SNAKE_CASE :       str     		=      src_lang
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self(__snake_case    ,					add_special_tokens=__snake_case    ,					return_tensors=__snake_case    ,					**__snake_case  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.convert_tokens_to_ids(__snake_case  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      tgt_lang_id
											return inputs






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case = "eng_Latn"    ,					__snake_case = None    ,					__snake_case = "fra_Latn"    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Any     		=      src_lang
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tgt_lang
											return super().prepare_seqaseq_batch(__snake_case    ,					__snake_case    ,					**__snake_case  )






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






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






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.convert_tokens_to_ids(__snake_case  )

											if self.legacy_behaviour:
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      []
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      [self.eos_token_id, self.cur_lang_code]
											else:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      [self.cur_lang_code]
															_SCREAMING_SNAKE_CASE :       Tuple     		=      [self.eos_token_id]

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.convert_ids_to_tokens(self.prefix_tokens  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.convert_ids_to_tokens(self.suffix_tokens  )

											_SCREAMING_SNAKE_CASE :       int     		=      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 UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.convert_tokens_to_ids(__snake_case  )
											if self.legacy_behaviour:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      []
															_SCREAMING_SNAKE_CASE :       Dict     		=      [self.eos_token_id, self.cur_lang_code]
											else:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      [self.cur_lang_code]
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      [self.eos_token_id]

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.convert_ids_to_tokens(self.prefix_tokens  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.convert_ids_to_tokens(self.suffix_tokens  )

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      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 UpperCAmelCase_	(    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
											_SCREAMING_SNAKE_CASE :       Tuple     		=      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,)








'''simple docstring'''


UpperCAmelCase_		:   Union[str, Any]							   =		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase_		:   Dict							   =		[{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_		:   Any							   =		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




