



"""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:
			a_			   =					None


a_			   =					logging.get_logger(__name__)


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

a_			   =					{
    '''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'''
        ),
    },
}

a_			   =					{
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
a_			   =					['''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     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Tuple							       =		VOCAB_FILES_NAMES
						_A		:							Optional[Any]							       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_A		:							Optional[Any]							       =		PRETRAINED_VOCAB_FILES_MAP
						_A		:							Union[str, Any]							       =		["""input_ids""", """attention_mask"""]
						_A		:							Optional[int]							       =		NllbTokenizer

						_A		:							List[int]							       =		[]
						_A		:							List[int]							       =		[]







						def __init__(self   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__="<s>"   ,		lowercase__="</s>"   ,		lowercase__="</s>"   ,		lowercase__="<s>"   ,		lowercase__="<unk>"   ,		lowercase__="<pad>"   ,		lowercase__="<mask>"   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=False   ,		**lowercase__   ,		):
											# Mask token behave like a normal word, i.e. include the space before it
											snake_case_					:		int 					=					AddedToken(lowercase__   ,		lstrip=lowercase__   ,		rstrip=lowercase__       ) if isinstance(lowercase__   ,		lowercase__       ) else mask_token
											snake_case_					:		str 					=					legacy_behaviour
											super().__init__(
											    vocab_file=lowercase__   ,		tokenizer_file=lowercase__   ,		bos_token=lowercase__   ,		eos_token=lowercase__   ,		sep_token=lowercase__   ,		cls_token=lowercase__   ,		unk_token=lowercase__   ,		pad_token=lowercase__   ,		mask_token=lowercase__   ,		src_lang=lowercase__   ,		tgt_lang=lowercase__   ,		additional_special_tokens=lowercase__   ,		legacy_behaviour=lowercase__   ,		**lowercase__   ,		)

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

											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}       )
											snake_case_					:		List[Any] 					=					{
											    lang_code: self.convert_tokens_to_ids(lowercase__       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

											snake_case_					:		Optional[Any] 					=					src_lang if src_lang is not None else """eng_Latn"""
											snake_case_					:		Dict 					=					self.convert_tokens_to_ids(self._src_lang       )
											snake_case_					:		Tuple 					=					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   ,		lowercase__       ):
											snake_case_					:		Union[str, Any] 					=					new_src_lang
											self.set_src_lang_special_tokens(self._src_lang       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = 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   ,		lowercase__   ,		lowercase__ = None       ):
											snake_case_					:		int 					=					[self.sep_token_id]
											snake_case_					:		Dict 					=					[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   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		**lowercase__       ):
											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_					:		List[Any] 					=					src_lang
											snake_case_					:		str 					=					self(lowercase__   ,		add_special_tokens=lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__       )
											snake_case_					:		str 					=					self.convert_tokens_to_ids(lowercase__       )
											snake_case_					:		List[str] 					=					tgt_lang_id
											return inputs







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = "eng_Latn"   ,		lowercase__ = None   ,		lowercase__ = "fra_Latn"   ,		**lowercase__   ,		):
											snake_case_					:		List[Any] 					=					src_lang
											snake_case_					:		int 					=					tgt_lang
											return super().prepare_seqaseq_batch(lowercase__   ,		lowercase__   ,		**lowercase__       )







						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   ,		lowercase__       ):
											snake_case_					:		int 					=					self.convert_tokens_to_ids(lowercase__       )

											if self.legacy_behaviour:
																snake_case_					:		Dict 					=					[]
																snake_case_					:		Optional[Any] 					=					[self.eos_token_id, self.cur_lang_code]
											else:
																snake_case_					:		Optional[int] 					=					[self.cur_lang_code]
																snake_case_					:		List[str] 					=					[self.eos_token_id]

											snake_case_					:		List[Any] 					=					self.convert_ids_to_tokens(self.prefix_tokens       )
											snake_case_					:		Optional[int] 					=					self.convert_ids_to_tokens(self.suffix_tokens       )

											snake_case_					:		Optional[Any] 					=					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   ,		lowercase__       ):
											snake_case_					:		str 					=					self.convert_tokens_to_ids(lowercase__       )
											if self.legacy_behaviour:
																snake_case_					:		Tuple 					=					[]
																snake_case_					:		List[Any] 					=					[self.eos_token_id, self.cur_lang_code]
											else:
																snake_case_					:		Any 					=					[self.cur_lang_code]
																snake_case_					:		Union[str, Any] 					=					[self.eos_token_id]

											snake_case_					:		List[Any] 					=					self.convert_ids_to_tokens(self.prefix_tokens       )
											snake_case_					:		Any 					=					self.convert_ids_to_tokens(self.suffix_tokens       )

											snake_case_					:		str 					=					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   ,		lowercase__   ,		lowercase__ = 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(lowercase__       ):
																logger.error(f'Vocabulary path ({save_directory}) should be a directory.'       )
																return
											snake_case_					:		str 					=					os.path.join(
											    lowercase__   ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

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

											return (out_vocab_file,)









"""simple docstring"""

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					assert isinstance(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 1
					assert dataset.column_names == ["text"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("""keep_in_memory"""				,    [False, True]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Union[str, Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					tmp_path / """cache"""
					snake_case_					:		Optional[int] 					=					{"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_					:		Tuple 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__				,    keep_in_memory=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize(
    """features"""				,    [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,    )
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					tmp_path / """cache"""
					snake_case_					:		int 					=					{"""text""": """string"""}
					snake_case_					:		Any 					=					features.copy() if features else default_expected_features
					snake_case_					:		List[Any] 					=					(
					    Features({feature: Value(SCREAMING_SNAKE_CASE__			) for feature, dtype in features.items()}			) if features is not None else None
					)
					snake_case_					:		Dict 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    features=SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize("""split"""				,    [None, NamedSplit("""train"""			), """train""", """test"""]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  str			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					tmp_path / """cache"""
					snake_case_					:		Optional[Any] 					=					{"""text""": """string"""}
					snake_case_					:		Optional[int] 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__				,    split=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					assert dataset.split == split if split else "train"







@pytest.mark.parametrize("""path_type"""				,    [str, list]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					if issubclass(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		List[str] 					=					text_path
					elif issubclass(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										snake_case_					:		str 					=					[text_path]
					snake_case_					:		List[str] 					=					tmp_path / """cache"""
					snake_case_					:		List[str] 					=					{"""text""": """string"""}
					snake_case_					:		Dict 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_dataset(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  List[str]=("train",)			):



					"""simple docstring"""





					assert isinstance(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					for split in splits:
										snake_case_					:		Dict 					=					dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 1
										assert dataset.column_names == ["text"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("""keep_in_memory"""				,    [False, True]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					snake_case_					:		int 					=					tmp_path / """cache"""
					snake_case_					:		List[str] 					=					{"""text""": """string"""}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_					:		Optional[Any] 					=					TextDatasetReader({"""train""": text_path}				,    cache_dir=SCREAMING_SNAKE_CASE__				,    keep_in_memory=SCREAMING_SNAKE_CASE__			).read()
					_check_text_datasetdict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize(
    """features"""				,    [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]				,    )
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					tmp_path / """cache"""
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					snake_case_					:		List[str] 					=					{"""text""": """string"""}
					snake_case_					:		int 					=					features.copy() if features else default_expected_features
					snake_case_					:		Tuple 					=					(
					    Features({feature: Value(SCREAMING_SNAKE_CASE__			) for feature, dtype in features.items()}			) if features is not None else None
					)
					snake_case_					:		str 					=					TextDatasetReader({"""train""": text_path}				,    features=SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_datasetdict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)







@pytest.mark.parametrize("""split"""				,    [None, NamedSplit("""train"""			), """train""", """test"""]			)
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					if split:
										snake_case_					:		Union[str, Any] 					=					{split: text_path}
					else:
										snake_case_					:		Union[str, Any] 					=					"""train"""
										snake_case_					:		int 					=					{"""train""": text_path, """test""": text_path}
					snake_case_					:		List[Any] 					=					tmp_path / """cache"""
					snake_case_					:		Tuple 					=					{"""text""": """string"""}
					snake_case_					:		int 					=					TextDatasetReader(SCREAMING_SNAKE_CASE__				,    cache_dir=SCREAMING_SNAKE_CASE__			).read()
					_check_text_datasetdict(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    splits=list(path.keys()			)			)
					assert all(dataset[split].split == split for split in path.keys()			)




