
'''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


__UpperCAmelCase												=   logging.get_logger(__name__)

__UpperCAmelCase												=   {
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__UpperCAmelCase												=   {
    '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'
        )
    },
}

__UpperCAmelCase												=   {'Helsinki-NLP/opus-mt-en-de': 512}
__UpperCAmelCase												=   {}


class 		_a     (							__A  ):







   """simple docstring"""




   A		= VOCAB_FILES_NAMES
   A		= PRETRAINED_VOCAB_FILES_MAP
   A		= PRETRAINED_INIT_CONFIGURATION
   A		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A		= ["input_ids", "attention_mask"]
   A		= re.compile('>>.+<<'  )  # type: re.Pattern






   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE="<unk>"    ,__SCREAMING_SNAKE_CASE="</s>"    ,__SCREAMING_SNAKE_CASE="<pad>"    ,__SCREAMING_SNAKE_CASE=512    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE=False    ,**__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  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=lowercase_    ,target_lang=lowercase_    ,unk_token=lowercase_    ,eos_token=lowercase_    ,pad_token=lowercase_    ,model_max_length=lowercase_    ,sp_model_kwargs=self.sp_model_kwargs    ,target_vocab_file=lowercase_    ,separate_vocabs=lowercase_    ,**lowercase_    ,)
    assert Path(lowercase_      ).exists(), f"""cannot find spm source {source_spm}"""

    SCREAMING_SNAKE_CASE  :  List[str]								=		separate_vocabs
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		load_json(lowercase_      )
    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:
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		load_json(lowercase_      )
     SCREAMING_SNAKE_CASE  :  List[Any]								=		{v: k for k, v in self.target_encoder.items()}
     SCREAMING_SNAKE_CASE  :  Dict								=		[]
    else:
     SCREAMING_SNAKE_CASE  :  int								=		{v: k for k, v in self.encoder.items()}
     SCREAMING_SNAKE_CASE  :  list								=		[k for k in self.encoder if k.startswith('>>'      ) and k.endswith('<<'      )]

    SCREAMING_SNAKE_CASE  :  int								=		source_lang
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		target_lang
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		[source_spm, target_spm]

    # load SentencePiece model for pre-processing
    SCREAMING_SNAKE_CASE  :  str								=		load_spm(lowercase_    ,self.sp_model_kwargs      )
    SCREAMING_SNAKE_CASE  :  str								=		load_spm(lowercase_    ,self.sp_model_kwargs      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.spm_source
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.encoder

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

    self._setup_normalizer()






   def 					__a			(       self      ):
    try:
     from sacremoses import MosesPunctNormalizer

     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		MosesPunctNormalizer(self.source_lang      ).normalize
    except (ImportError, FileNotFoundError):
     warnings.warn('Recommended: pip install sacremoses.'      )
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		lambda __SCREAMING_SNAKE_CASE      : x






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    return self.punc_normalizer(lowercase_      ) if x else ""






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    return self.current_encoder.get(lowercase_    ,self.current_encoder[self.unk_token]      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.language_code_re.match(lowercase_      )
    SCREAMING_SNAKE_CASE  :  list								=		[match.group(0      )] if match else []
    return code, self.language_code_re.sub(''    ,lowercase_      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.remove_language_code(lowercase_      )
    SCREAMING_SNAKE_CASE  :  int								=		self.current_spm.encode(lowercase_    ,out_type=lowercase_      )
    return code + pieces






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    return self.decoder.get(lowercase_    ,self.unk_token      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    return super().batch_decode(lowercase_    ,**lowercase_      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    return super().decode(lowercase_    ,**lowercase_      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		self.spm_source if self._decode_use_source_tokenizer else self.spm_target
    SCREAMING_SNAKE_CASE  :  str								=		[]
    SCREAMING_SNAKE_CASE  :  List[Any]								=		""
    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(lowercase_      ) + token + " "
      SCREAMING_SNAKE_CASE  :  Tuple								=		[]
     else:
      current_sub_tokens.append(lowercase_      )
    out_string += sp_model.decode_pieces(lowercase_      )
    return out_string.strip()






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		self.spm_source
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.encoder






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.spm_target
    if self.separate_vocabs:
     SCREAMING_SNAKE_CASE  :  Tuple								=		self.target_encoder






   @property
   def 					__a			(       self      ):
    return len(self.encoder      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = None      ):
    if not os.path.isdir(lowercase_      ):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
     return
    SCREAMING_SNAKE_CASE  :  Tuple								=		[]

    if self.separate_vocabs:
     SCREAMING_SNAKE_CASE  :  Any								=		os.path.join(
         lowercase_    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']    ,)
     SCREAMING_SNAKE_CASE  :  Dict								=		os.path.join(
         lowercase_    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']    ,)
     save_json(self.encoder    ,lowercase_      )
     save_json(self.target_encoder    ,lowercase_      )
     saved_files.append(lowercase_      )
     saved_files.append(lowercase_      )
    else:
     SCREAMING_SNAKE_CASE  :  int								=		os.path.join(
         lowercase_    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']      )
     save_json(self.encoder    ,lowercase_      )
     saved_files.append(lowercase_      )

    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]    ,):
     SCREAMING_SNAKE_CASE  :  int								=		os.path.join(
         lowercase_    ,(filename_prefix + '-' if filename_prefix else '') + spm_save_filename      )
     if os.path.abspath(lowercase_      ) != os.path.abspath(lowercase_      ) and os.path.isfile(lowercase_      ):
      copyfile(lowercase_    ,lowercase_      )
      saved_files.append(lowercase_      )
     elif not os.path.isfile(lowercase_      ):
      with open(lowercase_    ,'wb'      ) as fi:
       SCREAMING_SNAKE_CASE  :  List[str]								=		spm_model.serialized_model_proto()
       fi.write(lowercase_      )
      saved_files.append(lowercase_      )

    return tuple(lowercase_      )






   def 					__a			(       self      ):
    return self.get_src_vocab()






   def 					__a			(       self      ):
    return dict(self.encoder    ,**self.added_tokens_encoder      )






   def 					__a			(       self      ):
    return dict(self.target_encoder    ,**self.added_tokens_decoder      )






   def __getstate__(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, 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    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		d

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

    SCREAMING_SNAKE_CASE  :  Dict								=		(load_spm(lowercase_    ,self.sp_model_kwargs      ) for f in self.spm_files)
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.spm_source
    self._setup_normalizer()






   def 					__a			(       self    ,*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    return 1






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		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 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = False      ):
    if already_has_special_tokens:
     return self._special_token_mask(lowercase_      )
    elif token_ids_a is None:
     return self._special_token_mask(lowercase_      ) + [1]
    else:
     return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	Dict[str, Any]							)       ->	Optional[Any]:
 SCREAMING_SNAKE_CASE  :  Tuple								=		sentencepiece.SentencePieceProcessor(**_lowerCamelCase							)
 spm.Load(_lowerCamelCase							)
 return spm



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[int]   ,     snake_case_				:	str							)       ->	List[Any]:
 with open(_lowerCamelCase   ,     'w'							) as f:
  json.dump(_lowerCamelCase   ,     _lowerCamelCase   ,     indent=2							)





def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	str:
 with open(_lowerCamelCase   ,     'r'							) as f:
  return json.load(_lowerCamelCase							)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase    				=		{'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase    				=		[
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase    				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



