



"""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:
 _lowerCAmelCase							:   Union[str, Any]				=				None


_lowerCAmelCase							:   Any				=				logging.get_logger(__name__)


_lowerCAmelCase							:   Dict				=				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase							:   List[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"
        ),
    },
}

_lowerCAmelCase							:   List[str]				=				{
    "facebook/nllb-large-en-ro": 10_24,
    "facebook/nllb-200-distilled-600M": 10_24,
}

# fmt: off
_lowerCAmelCase							:   List[Any]				=				["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       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:  Tuple						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:  Tuple						=					PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE		:  str						=					NllbTokenizer

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



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

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

           _UpperCAmelCase     :      Optional[Any]														=       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}		)
           _UpperCAmelCase     :      Optional[int]														=       {
               lang_code: self.convert_tokens_to_ids(A		) for lang_code in FAIRSEQ_LANGUAGE_CODES
           }

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



      @property
      def 		snake_case_   (	self	:      Optional[int]		):
           return self._src_lang



      @src_lang.setter
      def 		snake_case_   (	self	:      Dict	,      A	:      str		):
           _UpperCAmelCase     :      str														=       new_src_lang
           self.set_src_lang_special_tokens(self._src_lang		)



      def 		snake_case_   (	self	:      int	,      A	:      List[int]	,      A	:      Optional[List[int]] = 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 		snake_case_   (	self	:      List[Any]	,      A	:      List[int]	,      A	:      Optional[List[int]] = None		):
           _UpperCAmelCase     :      str														=       [self.sep_token_id]
           _UpperCAmelCase     :      Union[str, Any]														=       [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 		snake_case_   (	self	:      Optional[int]	,      A	:      Dict	,      A	:      str	,      A	:      Optional[str]	,      A	:      Optional[str]	,      **A	:      List[Any]		):
           if src_lang is None or tgt_lang is None:
                raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"		)
           _UpperCAmelCase     :      Union[str, Any]														=       src_lang
           _UpperCAmelCase     :      Optional[int]														=       self(A	,      add_special_tokens=A	,      return_tensors=A	,      **A		)
           _UpperCAmelCase     :      Dict														=       self.convert_tokens_to_ids(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       tgt_lang_id
           return inputs



      def 		snake_case_   (	self	:      Tuple	,      A	:      List[str]	,      A	:      str = "eng_Latn"	,      A	:      Optional[List[str]] = None	,      A	:      str = "fra_Latn"	,      **A	:      List[Any]	,      ):
           _UpperCAmelCase     :      List[Any]														=       src_lang
           _UpperCAmelCase     :      Dict														=       tgt_lang
           return super().prepare_seqaseq_batch(A	,      A	,      **A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           return self.set_src_lang_special_tokens(self.src_lang		)



      def 		snake_case_   (	self	:      Any		):
           return self.set_tgt_lang_special_tokens(self.tgt_lang		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Dict		):
           _UpperCAmelCase     :      Dict														=       self.convert_tokens_to_ids(A		)

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

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

           _UpperCAmelCase     :      Tuple														=       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 		snake_case_   (	self	:      List[Any]	,      A	:      str		):
           _UpperCAmelCase     :      Dict														=       self.convert_tokens_to_ids(A		)
           if self.legacy_behaviour:
                _UpperCAmelCase     :      List[str]														=       []
                _UpperCAmelCase     :      Optional[Any]														=       [self.eos_token_id, self.cur_lang_code]
           else:
                _UpperCAmelCase     :      Optional[Any]														=       [self.cur_lang_code]
                _UpperCAmelCase     :      int														=       [self.eos_token_id]

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

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

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

           return (out_vocab_file,)





"""simple docstring"""




import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       tmp_path / "file.csv"
     _UpperCAmelCase     :      Union[str, Any]														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       tmp_path / "malformed_file.csv"
     _UpperCAmelCase     :      Dict														=       textwrap.dedent(
         "\\n        header1,header2\n        1,2\n        10,20,\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       tmp_path / "csv_with_image.csv"
     _UpperCAmelCase     :      List[str]														=       textwrap.dedent(
         f'\\n        image\n        {image_file}\n        '							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  str:





     '''simple docstring'''

     _UpperCAmelCase     :      Dict														=       tmp_path / "csv_with_label.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        label\n        good\n        bad\n        good\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

@pytest.fixture
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       tmp_path / "csv_with_int_list.csv"
     _UpperCAmelCase     :      Any														=       textwrap.dedent(
         "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "							)
     with open(SCREAMING_SNAKE_CASE__		,    "w"							) as f:
          f.write(SCREAMING_SNAKE_CASE__							)
     return str(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Dict							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       Csv()
     _UpperCAmelCase     :      Optional[int]														=       csv._generate_tables([[csv_file, malformed_csv_file]]							)
     with pytest.raises(SCREAMING_SNAKE_CASE__		,    match="Error tokenizing data"							):
          for _ in generator:
               pass
     assert any(
         record.levelname == "ERROR"
         and "Failed to read file" in record.message
         and os.path.basename(SCREAMING_SNAKE_CASE__							) in record.message
         for record in caplog.records							)

@require_pil
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Union[str, Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Union[str, Any]														=       f.read().splitlines()[1]
     _UpperCAmelCase     :      Tuple														=       Csv(encoding="utf-8"		,    features=Features({"image": Image()}							)							)
     _UpperCAmelCase     :      Tuple														=       csv._generate_tables([[csv_file_with_image]]							)
     _UpperCAmelCase     :      List[str]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("image"							).type == Image()()
     _UpperCAmelCase     :      Optional[Any]														=       pa_table.to_pydict()["image"]
     assert generated_content == [{"path": image_file, "bytes": None}]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  Optional[Any]:





     '''simple docstring'''

     with open(SCREAMING_SNAKE_CASE__		,    encoding="utf-8"							) as f:
          _UpperCAmelCase     :      Tuple														=       f.read().splitlines()[1:]
     _UpperCAmelCase     :      Optional[Any]														=       Csv(encoding="utf-8"		,    features=Features({"label": ClassLabel(names=["good", "bad"]							)}							)							)
     _UpperCAmelCase     :      int														=       csv._generate_tables([[csv_file_with_label]]							)
     _UpperCAmelCase     :      Dict														=       pa.concat_tables([table for _, table in generator]							)
     assert pa_table.schema.field("label"							).type == ClassLabel(names=["good", "bad"]							)()
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["label"]
     assert generated_content == [ClassLabel(names=["good", "bad"]							).straint(SCREAMING_SNAKE_CASE__							) for label in labels]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      int														=       Csv(encoding="utf-8"		,    sep=","		,    converters={"int_list": lambda SCREAMING_SNAKE_CASE__							: [int(SCREAMING_SNAKE_CASE__							) for i in x.split()]}							)
     _UpperCAmelCase     :      Dict														=       csv._generate_tables([[csv_file_with_int_list]]							)
     _UpperCAmelCase     :      List[Any]														=       pa.concat_tables([table for _, table in generator]							)
     assert pa.types.is_list(pa_table.schema.field("int_list"							).type							)
     _UpperCAmelCase     :      Dict														=       pa_table.to_pydict()["int_list"]
     assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
