
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_A   =			logging.get_logger(__name__)

_A   =			"▁"

_A   =			{"vocab_file": "sentencepiece.bpe.model"}

_A   =			{
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

_A   =			{
    "facebook/mbart-large-50-one-to-many-mmt": 10_24,
}

# fmt: off
_A   =			["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							List[str]									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							List[str]									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							List[str]									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							List[str]									=		["input_ids", "attention_mask"]

     UpperCAmelCase__    :							str									=		[]
     UpperCAmelCase__    :							Tuple									=		[]





     def __init__(self				:   str    ,   _A				:   str    ,   _A				:   Optional[int]=None    ,   _A				:   Any=None    ,   _A				:   List[Any]="</s>"    ,   _A				:   List[Any]="</s>"    ,   _A				:   Tuple="<s>"    ,   _A				:   Optional[Any]="<unk>"    ,   _A				:   int="<pad>"    ,   _A				:   Union[str, Any]="<mask>"    ,   _A				:   Optional[Dict[str, Any]] = None    ,   **_A				:   Dict    ,   )						->						None:
         snake_case								= AddedToken(lowercase_    ,   lstrip=lowercase_    ,   rstrip=lowercase_       ) if isinstance(lowercase_    ,   lowercase_       ) else mask_token

         snake_case								= {} if sp_model_kwargs is None else sp_model_kwargs

         snake_case								= kwargs.get("additional_special_tokens"    ,   []       )
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             src_lang=lowercase_    ,   tgt_lang=lowercase_    ,   eos_token=lowercase_    ,   unk_token=lowercase_    ,   sep_token=lowercase_    ,   cls_token=lowercase_    ,   pad_token=lowercase_    ,   mask_token=lowercase_    ,   sp_model_kwargs=self.sp_model_kwargs    ,   **lowercase_    ,   )

         snake_case								= spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(str(lowercase_       )       )
         snake_case								= vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         snake_case								= {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         snake_case								= 1

         snake_case								= len(self.sp_model       )
         snake_case								= {
             code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase_       )
         }
         snake_case								= {v: k for k, v in self.lang_code_to_id.items()}
         snake_case								= len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

         self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
         snake_case								= {v: k for k, v in self.fairseq_tokens_to_ids.items()}

         snake_case								= src_lang if src_lang is not None else "en_XX"
         snake_case								= self.lang_code_to_id[self._src_lang]
         snake_case								= tgt_lang
         self.set_src_lang_special_tokens(self._src_lang       )





     @property
     def 		UpperCAmelCase(self				:   int       )						->						int:
         return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token





     @property
     def 		UpperCAmelCase(self				:   Optional[int]       )						->						str:
         return self._src_lang





     @src_lang.setter
     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   str       )						->						None:
         snake_case								= new_src_lang
         self.set_src_lang_special_tokens(self._src_lang       )





     def __getstate__(self				:   List[str]       )						->						Dict:
         snake_case								= self.__dict__.copy()
         snake_case								= None
         return state





     def __setstate__(self				:   List[str]    ,   _A				:   Dict       )						->						None:
         snake_case								= d

         # for backward compatibility
         if not hasattr(self    ,   "sp_model_kwargs"       ):
             snake_case								= {}

         snake_case								= spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(self.vocab_file       )





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Dict:
         snake_case								= {self.convert_ids_to_tokens(lowercase_       ): i for i in range(self.vocab_size       )}
         vocab.update(self.added_tokens_encoder       )
         return vocab





     def 		UpperCAmelCase(self				:   str    ,   _A				:   str       )						->						List[str]:
         return self.sp_model.encode(lowercase_    ,   out_type=lowercase_       )





     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   str       )						->						int:







         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         snake_case								= self.sp_model.PieceToId(lowercase_       )

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   int       )						->						str:







         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset       )





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Optional[int]       )						->						Dict:
         snake_case								= []
         snake_case								= ""
         snake_case								= False
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 if not prev_is_special:
                     out_string += " "
                 out_string += self.sp_model.decode(lowercase_       ) + token
                 snake_case								= True
                 snake_case								= []
             else:
                 current_sub_tokens.append(lowercase_       )
                 snake_case								= False
         out_string += self.sp_model.decode(lowercase_       )
         return out_string.strip()





     def 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple[str]:







         if not os.path.isdir(lowercase_       ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
             return
         snake_case								= 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_       ) and os.path.isfile(self.vocab_file       ):
             copyfile(self.vocab_file    ,   lowercase_       )
         elif not os.path.isfile(self.vocab_file       ):
             with open(lowercase_    ,   "wb"       ) as fi:
                 snake_case								= self.sp_model.serialized_model_proto()
                 fi.write(lowercase_       )

         return (out_vocab_file,)





     def 		UpperCAmelCase(self				:   int    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None    ,   _A				:   bool = False       )						->						List[int]:







         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=lowercase_    ,   token_ids_a=lowercase_    ,   already_has_special_tokens=lowercase_       )

         snake_case								= [1] * len(self.prefix_tokens       )
         snake_case								= [1] * len(self.suffix_tokens       )
         if token_ids_a is None:
             return prefix_ones + ([0] * len(lowercase_       )) + suffix_ones
         return prefix_ones + ([0] * len(lowercase_       )) + ([0] * len(lowercase_       )) + suffix_ones





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:







         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				:   Optional[int]    ,   _A				:   Tuple    ,   _A				:   str    ,   _A				:   Optional[str]    ,   _A				:   Optional[str]    ,   **_A				:   Any       )						->						Dict:







         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								= src_lang
         snake_case								= self(lowercase_    ,   add_special_tokens=lowercase_    ,   return_tensors=lowercase_    ,   **lowercase_       )
         snake_case								= self.convert_tokens_to_ids(lowercase_       )
         snake_case								= tgt_lang_id
         return inputs





     def 		UpperCAmelCase(self				:   Any    ,   _A				:   List[str]    ,   _A				:   str = "en_XX"    ,   _A				:   Optional[List[str]] = None    ,   _A				:   str = "ro_RO"    ,   **_A				:   int    ,   )						->						BatchEncoding:
         snake_case								= src_lang
         snake_case								= tgt_lang
         return super().prepare_seqaseq_batch(lowercase_    ,   lowercase_    ,   **lowercase_       )





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						str:
         return self.set_src_lang_special_tokens(self.src_lang       )





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						List[str]:
         return self.set_tgt_lang_special_tokens(self.tgt_lang       )





     def 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   str       )						->						None:
         snake_case								= self.lang_code_to_id[src_lang]
         snake_case								= [self.cur_lang_code_id]
         snake_case								= [self.eos_token_id]





     def 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   str       )						->						None:
         snake_case								= self.lang_code_to_id[tgt_lang]
         snake_case								= [self.cur_lang_code_id]
         snake_case								= [self.eos_token_id]



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A   =			{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A   =			[
       "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LiltForQuestionAnswering",
       "LiltForSequenceClassification",
       "LiltForTokenClassification",
       "LiltModel",
       "LiltPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_lilt import (
          LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
          LiltForQuestionAnswering,
          LiltForSequenceClassification,
          LiltForTokenClassification,
          LiltModel,
          LiltPreTrainedModel,
      )

else:
   import sys

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

