
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": 5_12}
__UpperCAmelCase   						=     {}





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


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



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

          SCREAMING_SNAKE_CASE_	=      separate_vocabs
          SCREAMING_SNAKE_CASE_	=      load_json(_A	)
          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_	=      load_json(_A	)
             SCREAMING_SNAKE_CASE_	=      {v: k for k, v in self.target_encoder.items()}
             SCREAMING_SNAKE_CASE_	=      []
          else:
             SCREAMING_SNAKE_CASE_	=      {v: k for k, v in self.encoder.items()}
             SCREAMING_SNAKE_CASE_	=      [k for k in self.encoder if k.startswith('''>>'''	) and k.endswith('''<<'''	)]

          SCREAMING_SNAKE_CASE_	=      source_lang
          SCREAMING_SNAKE_CASE_	=      target_lang
          SCREAMING_SNAKE_CASE_	=      [source_spm, target_spm]

          # load SentencePiece model for pre-processing
          SCREAMING_SNAKE_CASE_	=      load_spm(_A ,     self.sp_model_kwargs	)
          SCREAMING_SNAKE_CASE_	=      load_spm(_A ,     self.sp_model_kwargs	)
          SCREAMING_SNAKE_CASE_	=      self.spm_source
          SCREAMING_SNAKE_CASE_	=      self.encoder

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

          self._setup_normalizer()



       def     _UpperCamelCase	(							self	)				->				Dict:
          try:
             from sacremoses import MosesPunctNormalizer

             SCREAMING_SNAKE_CASE_	=      MosesPunctNormalizer(self.source_lang	).normalize
          except (ImportError, FileNotFoundError):
             warnings.warn('''Recommended: pip install sacremoses.'''	)
             SCREAMING_SNAKE_CASE_	=      lambda _A	: x



       def     _UpperCamelCase	(							self ,     _A	)				->				str:
          return self.punc_normalizer(_A	) if x else ""



       def     _UpperCamelCase	(							self ,     _A	)				->				List[Any]:
          return self.current_encoder.get(_A ,     self.current_encoder[self.unk_token]	)



       def     _UpperCamelCase	(							self ,     _A	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      self.language_code_re.match(_A	)
          SCREAMING_SNAKE_CASE_	=      [match.group(0	)] if match else []
          return code, self.language_code_re.sub('''''' ,     _A	)



       def     _UpperCamelCase	(							self ,     _A	)				->				List[str]:
          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      self.remove_language_code(_A	)
          SCREAMING_SNAKE_CASE_	=      self.current_spm.encode(_A ,     out_type=_A	)
          return code + pieces



       def     _UpperCamelCase	(							self ,     _A	)				->				str:
          return self.decoder.get(_A ,     self.unk_token	)



       def     _UpperCamelCase	(							self ,     _A ,     **_A	)				->				str:
          return super().batch_decode(_A ,     **_A	)



       def     _UpperCamelCase	(							self ,     _A ,     **_A	)				->				List[Any]:
          return super().decode(_A ,     **_A	)



       def     _UpperCamelCase	(							self ,     _A	)				->				str:
          SCREAMING_SNAKE_CASE_	=      self.spm_source if self._decode_use_source_tokenizer else self.spm_target
          SCREAMING_SNAKE_CASE_	=      []
          SCREAMING_SNAKE_CASE_	=      ''''''
          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(_A	) + token + " "
                SCREAMING_SNAKE_CASE_	=      []
             else:
                current_sub_tokens.append(_A	)
          out_string += sp_model.decode_pieces(_A	)
          return out_string.strip()



       def     _UpperCamelCase	(							self ,     _A ,     _A=None	)				->				List[int]:
          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     _UpperCamelCase	(							self	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      self.spm_source
          SCREAMING_SNAKE_CASE_	=      self.encoder



       def     _UpperCamelCase	(							self	)				->				Optional[int]:
          SCREAMING_SNAKE_CASE_	=      self.spm_target
          if self.separate_vocabs:
             SCREAMING_SNAKE_CASE_	=      self.target_encoder



       @property
       def     _UpperCamelCase	(							self	)				->				int:
          return len(self.encoder	)



       def     _UpperCamelCase	(							self ,     _A ,     _A = None	)				->				Tuple[str]:
          if not os.path.isdir(_A	):
             logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
             return
          SCREAMING_SNAKE_CASE_	=      []

          if self.separate_vocabs:
             SCREAMING_SNAKE_CASE_	=      os.path.join(
                 _A ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab'''] ,     )
             SCREAMING_SNAKE_CASE_	=      os.path.join(
                 _A ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file'''] ,     )
             save_json(self.encoder ,     _A	)
             save_json(self.target_encoder ,     _A	)
             saved_files.append(_A	)
             saved_files.append(_A	)
          else:
             SCREAMING_SNAKE_CASE_	=      os.path.join(
                 _A ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']	)
             save_json(self.encoder ,     _A	)
             saved_files.append(_A	)

          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_	=      os.path.join(
                 _A ,     (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename	)
             if os.path.abspath(_A	) != os.path.abspath(_A	) and os.path.isfile(_A	):
                copyfile(_A ,     _A	)
                saved_files.append(_A	)
             elif not os.path.isfile(_A	):
                with open(_A ,     '''wb'''	) as fi:
                   SCREAMING_SNAKE_CASE_	=      spm_model.serialized_model_proto()
                   fi.write(_A	)
                saved_files.append(_A	)

          return tuple(_A	)



       def     _UpperCamelCase	(							self	)				->				Dict:
          return self.get_src_vocab()



       def     _UpperCamelCase	(							self	)				->				List[Any]:
          return dict(self.encoder ,     **self.added_tokens_encoder	)



       def     _UpperCamelCase	(							self	)				->				Any:
          return dict(self.target_encoder ,     **self.added_tokens_decoder	)



       def __getstate__(							self	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      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 ,     _A	)				->				None:
          SCREAMING_SNAKE_CASE_	=      d

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

          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      (load_spm(_A ,     self.sp_model_kwargs	) for f in self.spm_files)
          SCREAMING_SNAKE_CASE_	=      self.spm_source
          self._setup_normalizer()



       def     _UpperCamelCase	(							self ,     *_A ,     **_A	)				->				Any:
          return 1



       def     _UpperCamelCase	(							self ,     _A	)				->				Tuple:
          SCREAMING_SNAKE_CASE_	=      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     _UpperCamelCase	(							self ,     _A ,     _A = None ,     _A = False	)				->				List[int]:
          if already_has_special_tokens:
             return self._special_token_mask(_A	)
          elif token_ids_a is None:
             return self._special_token_mask(_A	) + [1]
          else:
             return self._special_token_mask(token_ids_a + token_ids_a	) + [1]







def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      sentencepiece.SentencePieceProcessor(**__lowerCamelCase						)
   spm.Load(__lowerCamelCase						)
   return spm


def  A__ (       __lowerCamelCase,  __lowerCamelCase						):
   with open(__lowerCamelCase,  '''w'''						) as f:
      json.dump(__lowerCamelCase,  __lowerCamelCase,  indent=2						)





def  A__ (       __lowerCamelCase						):
   with open(__lowerCamelCase,  '''r'''						) as f:
      return json.load(__lowerCamelCase						)




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__UpperCAmelCase   						=     logging.get_logger(__name__)





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="speech-encoder-decoder"
       UpperCAmelCase_				       =True



       def __init__(							self ,     **_A	)				->				Dict:
          super().__init__(**_A	)
          if "encoder" not in kwargs or "decoder" not in kwargs:
             raise ValueError(
                 F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                 F''' `decoder` sub-configurations are passed, but only {kwargs}'''	)

          SCREAMING_SNAKE_CASE_	=      kwargs.pop('''encoder'''	)
          SCREAMING_SNAKE_CASE_	=      encoder_config.pop('''model_type'''	)
          SCREAMING_SNAKE_CASE_	=      kwargs.pop('''decoder'''	)
          SCREAMING_SNAKE_CASE_	=      decoder_config.pop('''model_type'''	)

          SCREAMING_SNAKE_CASE_	=      AutoConfig.for_model(_A ,     **_A	)
          SCREAMING_SNAKE_CASE_	=      AutoConfig.for_model(_A ,     **_A	)
          SCREAMING_SNAKE_CASE_	=      True



       @classmethod
       def     _UpperCamelCase	(							cls ,     _A ,     _A ,     **_A	)				->				PretrainedConfig:
          logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''	)
          SCREAMING_SNAKE_CASE_	=      True
          SCREAMING_SNAKE_CASE_	=      True

          return cls(encoder=encoder_config.to_dict() ,     decoder=decoder_config.to_dict() ,     **_A	)




       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      copy.deepcopy(self.__dict__	)
          SCREAMING_SNAKE_CASE_	=      self.encoder.to_dict()
          SCREAMING_SNAKE_CASE_	=      self.decoder.to_dict()
          SCREAMING_SNAKE_CASE_	=      self.__class__.model_type
          return output


