



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:
   _lowercase						  =		None


_lowercase						  =		logging.get_logger(__name__)


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

_lowercase						  =		{
    "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"
        ),
    },
}

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

# fmt: off
_lowercase						  =		["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     ):
     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				["""input_ids""", """attention_mask"""]
     UpperCamelCase__		    =				NllbTokenizer

     UpperCamelCase__		    =				[]
     UpperCamelCase__		    =				[]




     def __init__(				self ,						a__=None ,						a__=None ,						a__="<s>" ,						a__="</s>" ,						a__="</s>" ,						a__="<s>" ,						a__="<unk>" ,						a__="<pad>" ,						a__="<mask>" ,						a__=None ,						a__=None ,						a__=None ,						a__=False ,						**a__ ,						):
         # Mask token behave like a normal word, i.e. include the space before it
         A__		 =					AddedToken(lowercase__ ,						lstrip=lowercase__ ,						rstrip=lowercase__) if isinstance(lowercase__ ,						lowercase__) else mask_token
         A__		 =					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__ ,						)

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

         A__		 =					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})
         A__		 =					{
             lang_code: self.convert_tokens_to_ids(lowercase__) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

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




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




     @src_lang.setter
     def 		snake_case_		(				self ,						a__):
         A__		 =					new_src_lang
         self.set_src_lang_special_tokens(self._src_lang)




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




     def 		snake_case_		(				self ,						a__ ,						a__ = "eng_Latn" ,						a__ = None ,						a__ = "fra_Latn" ,						**a__ ,						):
         A__		 =					src_lang
         A__		 =					tgt_lang
         return super().prepare_seqaseq_batch(lowercase__ ,						lowercase__ ,						**lowercase__)




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




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




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.convert_tokens_to_ids(lowercase__)

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

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

         A__		 =					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 ,						a__):
         A__		 =					self.convert_tokens_to_ids(lowercase__)
         if self.legacy_behaviour:
             A__		 =					[]
             A__		 =					[self.eos_token_id, self.cur_lang_code]
         else:
             A__		 =					[self.cur_lang_code]
             A__		 =					[self.eos_token_id]

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

         A__		 =					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 ,						a__ ,						a__ = 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
         A__		 =					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,)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_lowercase						  =		{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		["MobileViTFeatureExtractor"]
   _lowercase						  =		["MobileViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MobileViTForImageClassification",
       "MobileViTForSemanticSegmentation",
       "MobileViTModel",
       "MobileViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFMobileViTForImageClassification",
       "TFMobileViTForSemanticSegmentation",
       "TFMobileViTModel",
       "TFMobileViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilevit import MobileViTFeatureExtractor
      from .image_processing_mobilevit import MobileViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilevit import (
          MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileViTForImageClassification,
          MobileViTForSemanticSegmentation,
          MobileViTModel,
          MobileViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mobilevit import (
          TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFMobileViTForImageClassification,
          TFMobileViTForSemanticSegmentation,
          TFMobileViTModel,
          TFMobileViTPreTrainedModel,
      )


else:
   import sys

   _lowercase						  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
