
"""simple docstring"""
import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_a   :      List[Any]       =				logging.get_logger(__name__)

_a   :      Tuple       =				{
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_a   :      List[Any]       =				{
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

_a   :      Tuple       =				{"""Helsinki-NLP/opus-mt-en-de""": 512}
_a   :      Any       =				{}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Optional[Any]	=       VOCAB_FILES_NAMES
    __lowercase				:			str	=       PRETRAINED_VOCAB_FILES_MAP
    __lowercase				:			Dict	=       PRETRAINED_INIT_CONFIGURATION
    __lowercase				:			Optional[Any]	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowercase				:			Optional[int]	=       ["""input_ids""", """attention_mask"""]
    __lowercase				:			List[str]	=       re.compile(""">>.+<<""")  # type: re.Pattern
    def __init__(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_=None    ,     snake_case_=None    ,     snake_case_=None    ,     snake_case_="<unk>"    ,     snake_case_="</s>"    ,     snake_case_="<pad>"    ,     snake_case_=5_12    ,     snake_case_ = None    ,     snake_case_=False    ,     **snake_case_    ,     ):
      _snake_case  :    Optional[int]            = {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
          source_lang=snake_case_    ,     target_lang=snake_case_    ,     unk_token=snake_case_    ,     eos_token=snake_case_    ,     pad_token=snake_case_    ,     model_max_length=snake_case_    ,     sp_model_kwargs=self.sp_model_kwargs    ,     target_vocab_file=snake_case_    ,     separate_vocabs=snake_case_    ,     **snake_case_    ,     )
      assert Path(snake_case_  ).exists(), F'cannot find spm source {source_spm}'

      _snake_case  :    Optional[Any]            = separate_vocabs
      _snake_case  :    Union[str, Any]            = load_json(snake_case_  )
      if self.unk_token not in self.encoder:
        raise KeyError("<unk> token must be in vocab"  )
      assert self.pad_token in self.encoder

      if separate_vocabs:
        _snake_case  :    List[str]            = load_json(snake_case_  )
        _snake_case  :    Tuple            = {v: k for k, v in self.target_encoder.items()}
        _snake_case  :    List[str]            = []
      else:
        _snake_case  :    Dict            = {v: k for k, v in self.encoder.items()}
        _snake_case  :    list            = [k for k in self.encoder if k.startswith(">>"  ) and k.endswith("<<"  )]

      _snake_case  :    Union[str, Any]            = source_lang
      _snake_case  :    Optional[Any]            = target_lang
      _snake_case  :    str            = [source_spm, target_spm]

      # load SentencePiece model for pre-processing
      _snake_case  :    Union[str, Any]            = load_spm(snake_case_    ,     self.sp_model_kwargs  )
      _snake_case  :    Optional[Any]            = load_spm(snake_case_    ,     self.sp_model_kwargs  )
      _snake_case  :    List[str]            = self.spm_source
      _snake_case  :    Dict            = self.encoder

      # Multilingual target side: default to using first supported language code.

      self._setup_normalizer()
    def        lowerCamelCase__						(     self  ):
      try:
        from sacremoses import MosesPunctNormalizer

        _snake_case  :    List[str]            = MosesPunctNormalizer(self.source_lang  ).normalize
      except (ImportError, FileNotFoundError):
        warnings.warn("Recommended: pip install sacremoses."  )
        _snake_case  :    List[str]            = lambda snake_case_  : x
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      return self.punc_normalizer(snake_case_  ) if x else ""
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      return self.current_encoder.get(snake_case_    ,     self.current_encoder[self.unk_token]  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    str            = self.language_code_re.match(snake_case_  )
      _snake_case  :    list            = [match.group(0  )] if match else []
      return code, self.language_code_re.sub(""    ,     snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case      ,       _snake_case  :    Union[str, Any]            = self.remove_language_code(snake_case_  )
      _snake_case  :    Any            = self.current_spm.encode(snake_case_    ,     out_type=snake_case_  )
      return code + pieces
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      return self.decoder.get(snake_case_    ,     self.unk_token  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     **snake_case_  ):
      return super().batch_decode(snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     **snake_case_  ):
      return super().decode(snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    str            = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
      _snake_case  :    List[Any]            = []
      _snake_case  :    str            = ""
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          out_string += sp_model.decode_pieces(snake_case_  ) + token + " "
          _snake_case  :    Union[str, Any]            = []
        else:
          current_sub_tokens.append(snake_case_  )
      out_string += sp_model.decode_pieces(snake_case_  )
      return out_string.strip()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=None  ):
      if token_ids_a is None:
        return token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return token_ids_a + token_ids_a + [self.eos_token_id]
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = self.spm_source
      _snake_case  :    Tuple            = self.encoder
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = self.spm_target
      if self.separate_vocabs:
        _snake_case  :    Dict            = self.target_encoder
    @property
    def        lowerCamelCase__						(     self  ):
      return len(self.encoder  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None  ):
      if not os.path.isdir(snake_case_  ):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'  )
        return
      _snake_case  :    int            = []

      if self.separate_vocabs:
        _snake_case  :    List[str]            = os.path.join(
            snake_case_    ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]    ,     )
        _snake_case  :    Optional[Any]            = os.path.join(
            snake_case_    ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]    ,     )
        save_json(self.encoder    ,     snake_case_  )
        save_json(self.target_encoder    ,     snake_case_  )
        saved_files.append(snake_case_  )
        saved_files.append(snake_case_  )
      else:
        _snake_case  :    Any            = os.path.join(
            snake_case_    ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]  )
        save_json(self.encoder    ,     snake_case_  )
        saved_files.append(snake_case_  )

      for spm_save_filename, spm_orig_path, spm_model in zip(
          [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]    ,     self.spm_files    ,     [self.spm_source, self.spm_target]    ,     ):
        _snake_case  :    List[str]            = os.path.join(
            snake_case_    ,     (filename_prefix + "-" if filename_prefix else "") + spm_save_filename  )
        if os.path.abspath(snake_case_  ) != os.path.abspath(snake_case_  ) and os.path.isfile(snake_case_  ):
          copyfile(snake_case_    ,     snake_case_  )
          saved_files.append(snake_case_  )
        elif not os.path.isfile(snake_case_  ):
          with open(snake_case_    ,     "wb"  ) as fi:
            _snake_case  :    str            = spm_model.serialized_model_proto()
            fi.write(snake_case_  )
          saved_files.append(snake_case_  )

      return tuple(snake_case_  )
    def        lowerCamelCase__						(     self  ):
      return self.get_src_vocab()
    def        lowerCamelCase__						(     self  ):
      return dict(self.encoder    ,     **self.added_tokens_encoder  )
    def        lowerCamelCase__						(     self  ):
      return dict(self.target_encoder    ,     **self.added_tokens_decoder  )
    def __getstate__(     self  ):
      _snake_case  :    Optional[Any]            = self.__dict__.copy()
      state.update(
          {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}  )
      return state
    def __setstate__(     self    ,     snake_case_  ):
      _snake_case  :    int            = d

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

      _snake_case      ,       _snake_case  :    str            = (load_spm(snake_case_    ,     self.sp_model_kwargs  ) for f in self.spm_files)
      _snake_case  :    Optional[int]            = self.spm_source
      self._setup_normalizer()
    def        lowerCamelCase__						(     self    ,     *snake_case_    ,     **snake_case_  ):
      return 1
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    List[Any]            = set(self.all_special_ids  )  # call it once instead of inside list comp
      all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special
      return [1 if x in all_special_ids else 0 for x in seq]


    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None    ,     snake_case_ = False  ):
      if already_has_special_tokens:
        return self._special_token_mask(snake_case_  )
      elif token_ids_a is None:
        return self._special_token_mask(snake_case_  ) + [1]
      else:
        return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]


def 					a__ ( a :					str							,  a :					Dict[str, Any]		):

  """simple docstring"""







  _snake_case  :    Tuple            = sentencepiece.SentencePieceProcessor(**a		)
  spm.Load(a		)
  return spm
def 					a__ ( a :					Any							,  a :					str		):

  """simple docstring"""







  with open(a							,  "w"		) as f:
    json.dump(a							,  a							,  indent=2		)



def 					a__ ( a :					str		):

  """simple docstring"""







  with open(a							,  "r"		) as f:
    return json.load(a		)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Dict				      =     ['SplinterTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
