

from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available







@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Union[List[PIL.Image.Image], np.ndarray]
  UpperCAmelCase__   :			Optional[List[bool]]


if is_transformers_available() and is_torch_available():
  from .modeling_roberta_series import RobertaSeriesModelWithTransformation
  from .pipeline_alt_diffusion import AltDiffusionPipeline
  from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline







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

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

UpperCAmelCase__			:  Tuple					     =     {
    """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__			:  int					     =     {"""Helsinki-NLP/opus-mt-en-de""": 512}
UpperCAmelCase__			:  Union[str, Any]					     =     {}







class 							a__							( UpperCAmelCase							):







  """simple docstring"""




  UpperCAmelCase__   :			Union[str, Any]      							=VOCAB_FILES_NAMES
  UpperCAmelCase__   :			List[Any]      							=PRETRAINED_VOCAB_FILES_MAP
  UpperCAmelCase__   :			int      							=PRETRAINED_INIT_CONFIGURATION
  UpperCAmelCase__   :			int      							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCAmelCase__   :			Tuple      							=["""input_ids""", """attention_mask"""]
  UpperCAmelCase__   :			Tuple      							=re.compile(""">>.+<<"""							)  # type: re.Pattern


  def __init__(      self     :			Dict					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Optional[Any]					,					UpperCAmelCase__     :			List[str]					,					UpperCAmelCase__     :			Union[str, Any]=None					,					UpperCAmelCase__     :			Optional[Any]=None					,					UpperCAmelCase__     :			Tuple=None					,					UpperCAmelCase__     :			Optional[Any]="<unk>"					,					UpperCAmelCase__     :			List[str]="</s>"					,					UpperCAmelCase__     :			Any="<pad>"					,					UpperCAmelCase__     :			Union[str, Any]=5_1_2					,					UpperCAmelCase__     :			Optional[Dict[str, Any]] = None					,					UpperCAmelCase__     :			Dict=False					,					**UpperCAmelCase__     :			List[str]					,					)       ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = {} 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=UpperCAmelCase__					,					target_lang=UpperCAmelCase__					,					unk_token=UpperCAmelCase__					,					eos_token=UpperCAmelCase__					,					pad_token=UpperCAmelCase__					,					model_max_length=UpperCAmelCase__					,					sp_model_kwargs=self.sp_model_kwargs					,					target_vocab_file=UpperCAmelCase__					,					separate_vocabs=UpperCAmelCase__					,					**UpperCAmelCase__					,					)
        assert Path(UpperCAmelCase__		).exists(), f"cannot find spm source {source_spm}"

        SCREAMING_SNAKE_CASE			:   Any	  = separate_vocabs
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = load_json(UpperCAmelCase__		)
        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			:   str	  = load_json(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE			:   str	  = {v: k for k, v in self.target_encoder.items()}
              SCREAMING_SNAKE_CASE			:   Tuple	  = []
        else:
              SCREAMING_SNAKE_CASE			:   List[str]	  = {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			:   Dict	  = source_lang
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = target_lang
        SCREAMING_SNAKE_CASE			:   str	  = [source_spm, target_spm]

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

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

        self._setup_normalizer()


  def 			_lowercase   (      self     :			Optional[int]		)       ->Union[str, Any]:
        """simple docstring"""







        try:
              from sacremoses import MosesPunctNormalizer

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


  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str		)       ->str:
        """simple docstring"""







        return self.punc_normalizer(UpperCAmelCase__		) if x else ""


  def 			_lowercase   (      self     :			Union[str, Any]					,					UpperCAmelCase__     :			Tuple		)       ->Dict:
        """simple docstring"""







        return self.current_encoder.get(UpperCAmelCase__					,					self.current_encoder[self.unk_token]		)


  def 			_lowercase   (      self     :			Any					,					UpperCAmelCase__     :			str		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = self.language_code_re.match(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   list	  = [match.group(0		)] if match else []
        return code, self.language_code_re.sub(""""""					,					UpperCAmelCase__		)


  def 			_lowercase   (      self     :			List[str]					,					UpperCAmelCase__     :			str		)       ->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   str	  = self.remove_language_code(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE			:   int	  = self.current_spm.encode(UpperCAmelCase__					,					out_type=UpperCAmelCase__		)
        return code + pieces


  def 			_lowercase   (      self     :			Dict					,					UpperCAmelCase__     :			int		)       ->str:
        """simple docstring"""







        return self.decoder.get(UpperCAmelCase__					,					self.unk_token		)


  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			List[str]					,					**UpperCAmelCase__     :			List[Any]		)       ->Dict:
        """simple docstring"""







        return super().batch_decode(UpperCAmelCase__					,					**UpperCAmelCase__		)


  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str					,					**UpperCAmelCase__     :			List[Any]		)       ->Optional[int]:
        """simple docstring"""







        return super().decode(UpperCAmelCase__					,					**UpperCAmelCase__		)


  def 			_lowercase   (      self     :			int					,					UpperCAmelCase__     :			List[str]		)       ->str:
        """simple docstring"""







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


  def 			_lowercase   (      self     :			int					,					UpperCAmelCase__     :			Tuple					,					UpperCAmelCase__     :			Union[str, Any]=None		)       ->List[int]:
        """simple docstring"""







        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 			_lowercase   (      self     :			Tuple		)       ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   int	  = self.spm_source
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = self.encoder


  def 			_lowercase   (      self     :			Optional[int]		)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[Any]	  = self.spm_target
        if self.separate_vocabs:
              SCREAMING_SNAKE_CASE			:   List[Any]	  = self.target_encoder


  @property
  def 			_lowercase   (      self     :			List[Any]		)       ->int:
        """simple docstring"""







        return len(self.encoder		)


  def 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			str					,					UpperCAmelCase__     :			Optional[str] = None		)       ->Tuple[str]:
        """simple docstring"""







        if not os.path.isdir(UpperCAmelCase__		):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
              return
        SCREAMING_SNAKE_CASE			:   Tuple	  = []

        if self.separate_vocabs:
              SCREAMING_SNAKE_CASE			:   Dict	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]					,					)
              SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]					,					)
              save_json(self.encoder					,					UpperCAmelCase__		)
              save_json(self.target_encoder					,					UpperCAmelCase__		)
              saved_files.append(UpperCAmelCase__		)
              saved_files.append(UpperCAmelCase__		)
        else:
              SCREAMING_SNAKE_CASE			:   Tuple	  = os.path.join(
                  UpperCAmelCase__					,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]		)
              save_json(self.encoder					,					UpperCAmelCase__		)
              saved_files.append(UpperCAmelCase__		)

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

        return tuple(UpperCAmelCase__		)


  def 			_lowercase   (      self     :			int		)       ->Dict:
        """simple docstring"""







        return self.get_src_vocab()


  def 			_lowercase   (      self     :			str		)       ->Union[str, Any]:
        """simple docstring"""







        return dict(self.encoder					,					**self.added_tokens_encoder		)


  def 			_lowercase   (      self     :			str		)       ->Tuple:
        """simple docstring"""







        return dict(self.target_encoder					,					**self.added_tokens_decoder		)


  def __getstate__(      self     :			Optional[int]		)       ->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = 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     :			List[Any]					,					UpperCAmelCase__     :			Dict		)       ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   List[str]	  = d

        # for backward compatibility
        if not hasattr(self					,					"""sp_model_kwargs"""		):
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = {}

        SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   List[Any]	  = (load_spm(UpperCAmelCase__					,					self.sp_model_kwargs		) for f in self.spm_files)
        SCREAMING_SNAKE_CASE			:   Any	  = self.spm_source
        self._setup_normalizer()


  def 			_lowercase   (      self     :			Dict					,					*UpperCAmelCase__     :			str					,					**UpperCAmelCase__     :			List[str]		)       ->Dict:
        """simple docstring"""







        return 1


  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			Dict		)       ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[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 			_lowercase   (      self     :			Tuple					,					UpperCAmelCase__     :			List					,					UpperCAmelCase__     :			Optional[List] = None					,					UpperCAmelCase__     :			bool = False		)       ->List[int]:
        """simple docstring"""







        if already_has_special_tokens:
              return self._special_token_mask(UpperCAmelCase__		)
        elif token_ids_a is None:
              return self._special_token_mask(UpperCAmelCase__		) + [1]
        else:
              return self._special_token_mask(token_ids_a + token_ids_a		) + [1]






def        __lowercase   (  _A    ,     _A      )  ->    sentencepiece.SentencePieceProcessor:
      SCREAMING_SNAKE_CASE			:   List[str]	  = sentencepiece.SentencePieceProcessor(**_A      )
      spm.Load(_A      )
      return spm






def        __lowercase   (  _A    ,     _A      )  ->    None:
      with open(_A    ,     """w"""      ) as f:
            json.dump(_A    ,     _A    ,     indent=2      )







def        __lowercase   (  _A      )  ->    Union[Dict, List]:
      with open(_A    ,     """r"""      ) as f:
            return json.load(_A      )




