


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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase : Union[str, Any] 			=				logging.get_logger(__name__)

__UpperCAmelCase : Dict 			=				'▁'

__UpperCAmelCase : Union[str, Any] 			=				{'vocab_file': 'prophetnet.tokenizer'}

__UpperCAmelCase : Tuple 			=				{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

__UpperCAmelCase : int 			=				{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

__UpperCAmelCase : List[str] 			=				{
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		collections.OrderedDict()
       with open(UpperCAmelCase__							,			'''r'''							,			encoding='''utf-8'''       ) as reader:
              UpperCamelCase    :	Dict   	=		reader.readlines()
       for index, token in enumerate(UpperCAmelCase__       ):
              UpperCamelCase    :	int   	=		token.rstrip('''\n'''       )
              UpperCamelCase    :	Dict   	=		index
       return vocab



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Union[str, Any]							=					VOCAB_FILES_NAMES
       __UpperCamelCase      :     str							=					PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase      :     List[Any]							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase      :     str							=					["input_ids", "attention_mask"]






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE="[SEP]" ,					__SCREAMING_SNAKE_CASE="[SEP]" ,					__SCREAMING_SNAKE_CASE="[SEP]" ,					__SCREAMING_SNAKE_CASE="[UNK]" ,					__SCREAMING_SNAKE_CASE="[PAD]" ,					__SCREAMING_SNAKE_CASE="[CLS]" ,					__SCREAMING_SNAKE_CASE="[MASK]" ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		{} if sp_model_kwargs is None else sp_model_kwargs

              super().__init__(
                  bos_token=__UpperCamelCase ,					eos_token=__UpperCamelCase ,					sep_token=__UpperCamelCase ,					unk_token=__UpperCamelCase ,					pad_token=__UpperCamelCase ,					cls_token=__UpperCamelCase ,					mask_token=__UpperCamelCase ,					sp_model_kwargs=self.sp_model_kwargs ,					**__UpperCamelCase ,					)

              try:
                     import sentencepiece as spm
              except ImportError:
                     logger.warning(
                         '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                         ''' pip install sentencepiece'''						)
                     raise

              UpperCamelCase    :	Any   	=		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
              self.sp_model.Load(str(__UpperCamelCase						)						)
              UpperCamelCase    :	Union[str, Any]   	=		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'

              # put special tokens and [unused] tokens into the vocab
              UpperCamelCase    :	Any   	=		{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

              for i in range(10						):
                     UpperCamelCase    :	List[str]   	=		f"""[unused{i}]"""
                     UpperCamelCase    :	str   	=		5 + i

              # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
              UpperCamelCase    :	Tuple   	=		12
              UpperCamelCase    :	Optional[int]   	=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
              for k in self.fairseq_tokens_to_ids.keys():
                     self.unique_no_split_tokens.append(__UpperCamelCase						)






       def __getstate__(     self						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		self.__dict__.copy()
              UpperCamelCase    :	Any   	=		None
              return state






       def __setstate__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		d
              try:
                     import sentencepiece as spm
              except ImportError:
                     logger.warning(
                         '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                         ''' pip install sentencepiece'''						)
                     raise

              # for backward compatibility
              if not hasattr(self ,					'''sp_model_kwargs'''						):
                     UpperCamelCase    :	Dict   	=		{}

              UpperCamelCase    :	List[Any]   	=		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
              self.sp_model.Load(self.vocab_file						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = False						):




              """simple docstring"""




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

              if token_ids_a is None:
                     return ([0] * len(__UpperCamelCase						)) + [1]
              return ([0] * len(__UpperCamelCase						)) + [1] + ([0] * len(__UpperCamelCase						)) + [1]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		[self.sep_token_id]

              if token_ids_a is None:
                     return len(token_ids_a + sep						) * [0]
              return len(token_ids_a + sep + sep + token_ids_a + sep						) * [0]






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return len(self.sp_model						) + self.fairseq_offset






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		{self.convert_ids_to_tokens(__UpperCamelCase						): i for i in range(self.vocab_size						)}
              vocab.update(self.added_tokens_encoder						)
              return vocab






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return self.sp_model.encode(__UpperCamelCase ,					out_type=__UpperCamelCase						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              UpperCamelCase    :	Optional[int]   	=		self.sp_model.PieceToId(__UpperCamelCase						)

              # 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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		''''''.join(__UpperCamelCase						).replace(__UpperCamelCase ,					''' '''						).strip()
              return out_string






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              if not os.path.isdir(__UpperCamelCase						):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
                     return
              UpperCamelCase    :	Dict   	=		os.path.join(
                  __UpperCamelCase ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

              if os.path.abspath(self.vocab_file						) != os.path.abspath(__UpperCamelCase						) and os.path.isfile(self.vocab_file						):
                     copyfile(self.vocab_file ,					__UpperCamelCase						)
              elif not os.path.isfile(self.vocab_file						):
                     with open(__UpperCamelCase ,					'''wb'''						) as fi:
                            UpperCamelCase    :	Dict   	=		self.sp_model.serialized_model_proto()
                            fi.write(__UpperCamelCase						)

              return (out_vocab_file,)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              if token_ids_a is None:
                     return token_ids_a + [self.sep_token_id]
              UpperCamelCase    :	Dict   	=		[self.sep_token_id]
              return token_ids_a + sep + token_ids_a + sep





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_mbart import MBartTokenizer
else:
  __UpperCAmelCase : Optional[int] 			=				None


__UpperCAmelCase : int 			=				logging.get_logger(__name__)


__UpperCAmelCase : List[Any] 			=				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase : str 			=				{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

__UpperCAmelCase : Union[str, Any] 			=				{
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
__UpperCAmelCase : Any 			=				["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"]



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     List[str]							=					VOCAB_FILES_NAMES
       __UpperCamelCase      :     str							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase      :     Tuple							=					PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase      :     Union[str, Any]							=					["input_ids", "attention_mask"]
       __UpperCamelCase      :     Any							=					MBartTokenizer

       __UpperCamelCase      :     List[int]							=					[]
       __UpperCamelCase      :     List[int]							=					[]






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="<unk>" ,					__SCREAMING_SNAKE_CASE="<pad>" ,					__SCREAMING_SNAKE_CASE="<mask>" ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		AddedToken(__SCREAMING_SNAKE_CASE ,					lstrip=__SCREAMING_SNAKE_CASE ,					rstrip=__SCREAMING_SNAKE_CASE						) if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						) else mask_token

              super().__init__(
                  vocab_file=__SCREAMING_SNAKE_CASE ,					tokenizer_file=__SCREAMING_SNAKE_CASE ,					bos_token=__SCREAMING_SNAKE_CASE ,					eos_token=__SCREAMING_SNAKE_CASE ,					sep_token=__SCREAMING_SNAKE_CASE ,					cls_token=__SCREAMING_SNAKE_CASE ,					unk_token=__SCREAMING_SNAKE_CASE ,					pad_token=__SCREAMING_SNAKE_CASE ,					mask_token=__SCREAMING_SNAKE_CASE ,					src_lang=__SCREAMING_SNAKE_CASE ,					tgt_lang=__SCREAMING_SNAKE_CASE ,					additional_special_tokens=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)

              UpperCamelCase    :	Dict   	=		vocab_file
              UpperCamelCase    :	List[str]   	=		False if not self.vocab_file else True

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

              UpperCamelCase    :	Dict   	=		src_lang if src_lang is not None else '''en_XX'''
              UpperCamelCase    :	List[Any]   	=		self.convert_tokens_to_ids(self._src_lang						)
              UpperCamelCase    :	str   	=		tgt_lang
              self.set_src_lang_special_tokens(self._src_lang						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self._src_lang






       @src_lang.setter
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		new_src_lang
              self.set_src_lang_special_tokens(self._src_lang						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              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    :	List[str]   	=		src_lang
              UpperCamelCase    :	Dict   	=		self(__SCREAMING_SNAKE_CASE ,					add_special_tokens=__SCREAMING_SNAKE_CASE ,					return_tensors=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[Any]   	=		self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Any   	=		tgt_lang_id
              return inputs






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = "en_XX" ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = "ro_RO" ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		src_lang
              UpperCamelCase    :	Optional[int]   	=		tgt_lang
              return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              return self.set_src_lang_special_tokens(self.src_lang						)






       def  _lowercase   (     self						):




              """simple docstring"""




              return self.set_tgt_lang_special_tokens(self.tgt_lang						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Any   	=		[]
              UpperCamelCase    :	Dict   	=		[self.eos_token_id, self.cur_lang_code]

              UpperCamelCase    :	Union[str, Any]   	=		self.convert_ids_to_tokens(self.prefix_tokens						)
              UpperCamelCase    :	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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		[]
              UpperCamelCase    :	Optional[Any]   	=		[self.eos_token_id, self.cur_lang_code]

              UpperCamelCase    :	Optional[Any]   	=		self.convert_ids_to_tokens(self.prefix_tokens						)
              UpperCamelCase    :	List[str]   	=		self.convert_ids_to_tokens(self.suffix_tokens						)

              UpperCamelCase    :	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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




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

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

              return (out_vocab_file,)

