
"""simple docstring"""

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

import sentencepiece as spm

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


UpperCamelCase			:       Optional[int]									=				logging.get_logger(__name__)

UpperCamelCase			:       Optional[Any]									=				"▁"

UpperCamelCase			:       Optional[int]									=				{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase			:       List[str]									=				{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

UpperCamelCase			:       Optional[Any]									=				{
    "facebook/xglm-564M": 2_0_4_8,
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		VOCAB_FILES_NAMES
     lowercase		     =		PRETRAINED_VOCAB_FILES_MAP
     lowercase		     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase		     =		["input_ids", "attention_mask"]





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

      '''simple docstring'''




      __UpperCamelCase       						= {} if sp_model_kwargs is None else sp_model_kwargs

      # Compatibility with the original tokenizer
      __UpperCamelCase       						= 7
      __UpperCamelCase       						= [F'<madeupword{i}>' for i in range(self.num_madeup_words   )]

      __UpperCamelCase       						= kwargs.get('additional_special_tokens'			,  []   )
      kwargs["additional_special_tokens"] += [
          word for word in madeup_words if word not in kwargs["additional_special_tokens"]
      ]

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

      __UpperCamelCase       						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(str(__UpperCAmelCase   )   )
      __UpperCamelCase       						= 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'

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

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

      __UpperCamelCase       						= len(self.sp_model   )
      __UpperCamelCase       						= {F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words   )}
      self.fairseq_tokens_to_ids.update(__UpperCAmelCase   )

      __UpperCamelCase       						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}





     def __getstate__(			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.__dict__.copy()
      __UpperCamelCase       						= None
      __UpperCamelCase       						= self.sp_model.serialized_model_proto()
      return state





     def __setstate__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= d

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

      __UpperCamelCase       						= spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None			,  __UpperCAmelCase = 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 [1] + ([0] * len(__UpperCAmelCase   ))
      return [1] + ([0] * len(__UpperCAmelCase   )) + [1, 1] + ([0] * len(__UpperCAmelCase   ))





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




      __UpperCamelCase       						= [self.sep_token_id]

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





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return len(self.sp_model   ) + self.fairseq_offset + self.num_madeup_words





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= {self.convert_ids_to_tokens(__UpperCAmelCase   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      __UpperCamelCase       						= 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 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''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 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= ''.join(__UpperCAmelCase   ).replace(__UpperCAmelCase			,  ' '   ).strip()
      return out_string







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = None   ):

      '''simple docstring'''




      if not os.path.isdir(__UpperCAmelCase   ):
       logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
       return
      __UpperCamelCase       						= 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       						= self.sp_model.serialized_model_proto()
        fi.write(__UpperCAmelCase   )

      return (out_vocab_file,)





"""simple docstring"""

import re

from filelock import FileLock


try:
      import nltk

      UpperCamelCase			:       int									=				True
except (ImportError, ModuleNotFoundError):
      UpperCamelCase			:       List[str]									=				False

if NLTK_AVAILABLE:
      with FileLock(".lock") as lock:
            nltk.download("punkt", quiet=True)






def 							A		(       snake_case   :str	)   ->			str:
 re.sub('<n>'       ,      ''       ,      snake_case	)  # remove pegasus newline char
 assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
 return "\n".join(nltk.sent_tokenize(snake_case	)	)



