



'''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     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		VOCAB_FILES_NAMES
    lowercase					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase					=		PRETRAINED_VOCAB_FILES_MAP
    lowercase					=		["input_ids", "attention_mask"]
    lowercase					=		NllbTokenizer

    lowercase					=		[]
    lowercase					=		[]




    def __init__(    self					:     Tuple ,     snake_case_					:     Optional[int]=None ,     snake_case_					:     Union[str, Any]=None ,     snake_case_					:     List[Any]="<s>" ,     snake_case_					:     int="</s>" ,     snake_case_					:     Optional[Any]="</s>" ,     snake_case_					:     List[str]="<s>" ,     snake_case_					:     Optional[int]="<unk>" ,     snake_case_					:     int="<pad>" ,     snake_case_					:     Any="<mask>" ,     snake_case_					:     Optional[Any]=None ,     snake_case_					:     Optional[Any]=None ,     snake_case_					:     Optional[Any]=None ,     snake_case_					:     Union[str, Any]=False ,     **snake_case_					:     Any ,     ):
          # Mask token behave like a normal word, i.e. include the space before it
          snake_case__  :    Any    						=      AddedToken(snake_case_ ,     lstrip=snake_case_ ,     rstrip=snake_case_						) if isinstance(snake_case_ ,     snake_case_						) else mask_token
          snake_case__  :    Union[str, Any]    						=      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__  :    Union[str, Any]    						=      vocab_file
          snake_case__  :    Optional[int]    						=      False if not self.vocab_file else True

          snake_case__  :    Tuple    						=      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__  :    Optional[Any]    						=      {
              lang_code: self.convert_tokens_to_ids(snake_case_						) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

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




    @property
    def lowerCamelCase       (    self					:     int						):
          return self._src_lang




    @src_lang.setter
    def lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     str						):
          snake_case__  :    int    						=      new_src_lang
          self.set_src_lang_special_tokens(self._src_lang						)




    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     List[int] ,     snake_case_					:     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 lowerCamelCase       (    self					:     List[str] ,     snake_case_					:     List[int] ,     snake_case_					:     Optional[List[int]] = None						):
          snake_case__  :    Tuple    						=      [self.sep_token_id]
          snake_case__  :    Optional[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 lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     str ,     snake_case_					:     Optional[str] ,     snake_case_					:     Optional[str] ,     **snake_case_					:     int						):
          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__  :    Dict    						=      src_lang
          snake_case__  :    int    						=      self(snake_case_ ,     add_special_tokens=snake_case_ ,     return_tensors=snake_case_ ,     **snake_case_						)
          snake_case__  :    int    						=      self.convert_tokens_to_ids(snake_case_						)
          snake_case__  :    Dict    						=      tgt_lang_id
          return inputs




    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     List[str] ,     snake_case_					:     str = "eng_Latn" ,     snake_case_					:     Optional[List[str]] = None ,     snake_case_					:     str = "fra_Latn" ,     **snake_case_					:     Optional[int] ,     ):
          snake_case__  :    Dict    						=      src_lang
          snake_case__  :    Dict    						=      tgt_lang
          return super().prepare_seqaseq_batch(snake_case_ ,     snake_case_ ,     **snake_case_						)




    def lowerCamelCase       (    self					:     List[Any]						):
          return self.set_src_lang_special_tokens(self.src_lang						)




    def lowerCamelCase       (    self					:     List[Any]						):
          return self.set_tgt_lang_special_tokens(self.tgt_lang						)




    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     Optional[Any]						):
          snake_case__  :    List[Any]    						=      self.convert_tokens_to_ids(snake_case_						)

          if self.legacy_behaviour:
                snake_case__  :    Any    						=      []
                snake_case__  :    Optional[int]    						=      [self.eos_token_id, self.cur_lang_code]
          else:
                snake_case__  :    Optional[int]    						=      [self.cur_lang_code]
                snake_case__  :    Optional[Any]    						=      [self.eos_token_id]

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

          snake_case__  :    Dict    						=      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 lowerCamelCase       (    self					:     int ,     snake_case_					:     str						):
          snake_case__  :    str    						=      self.convert_tokens_to_ids(snake_case_						)
          if self.legacy_behaviour:
                snake_case__  :    Union[str, Any]    						=      []
                snake_case__  :    int    						=      [self.eos_token_id, self.cur_lang_code]
          else:
                snake_case__  :    Dict    						=      [self.cur_lang_code]
                snake_case__  :    Union[str, Any]    						=      [self.eos_token_id]

          snake_case__  :    Any    						=      self.convert_ids_to_tokens(self.prefix_tokens						)
          snake_case__  :    Tuple    						=      self.convert_ids_to_tokens(self.suffix_tokens						)

          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 lowerCamelCase       (    self					:     List[str] ,     snake_case_					:     str ,     snake_case_					:     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(snake_case_						):
                logger.error(f"Vocabulary path ({save_directory}) should be a directory."						)
                return
          snake_case__  :    List[Any]    						=      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'''





import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__a          =		logging.getLogger(__name__)







@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		field(default=_a		,		metadata={"help": "Whether to use SortishSampler or not."}	)
    lowercase					=		field(
        default=_a		,		metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}	)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `max_length` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": (
                "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
                "to the `num_beams` value of the model configuration."
            )
        }		,		)
    lowercase					=		field(
        default=_a		,		metadata={
            "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
        }		,		)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    int    						=      super().to_dict()
          for k, v in d.items():
                if isinstance(snake_case_ ,     snake_case_						):
                      snake_case__  :    Optional[int]    						=      v.to_dict()
          return d
