




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


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

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

snake_case_					:					int       						=			{
    "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"
        )
    },
}

snake_case_					:					Dict       						=			{"Helsinki-NLP/opus-mt-en-de": 512}
snake_case_					:					Union[str, Any]       						=			{}







class __snake_case     (     a						):
   UpperCAmelCase__    :       Dict		=    VOCAB_FILES_NAMES
   UpperCAmelCase__    :       str		=    PRETRAINED_VOCAB_FILES_MAP
   UpperCAmelCase__    :       Optional[int]		=    PRETRAINED_INIT_CONFIGURATION
   UpperCAmelCase__    :       Union[str, Any]		=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCAmelCase__    :       Dict		=    ['''input_ids''', '''attention_mask''']
   UpperCAmelCase__    :       Tuple		=    re.compile('''>>.+<<'''						)  # type: re.Pattern







   def __init__( self						:		Tuple   ,		_snake_case						:		Optional[int]   ,		_snake_case						:		List[str]   ,		_snake_case						:		Dict   ,		_snake_case						:		Any=None   ,		_snake_case						:		Tuple=None   ,		_snake_case						:		List[str]=None   ,		_snake_case						:		List[Any]="<unk>"   ,		_snake_case						:		List[Any]="</s>"   ,		_snake_case						:		Optional[Any]="<pad>"   ,		_snake_case						:		Union[str, Any]=512   ,		_snake_case						:		Optional[Dict[str, Any]] = None   ,		_snake_case						:		Any=False   ,		**_snake_case						:		List[str]   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				{} 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}"""

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

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

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

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

        self._setup_normalizer()







   def lowerCamelCase		( self						:		List[Any]):

        """simple docstring"""


        try:
             from sacremoses import MosesPunctNormalizer

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







   def lowerCamelCase		( self						:		int   ,		_snake_case						:		str):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		Optional[int]   ,		_snake_case						:		Dict):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		Union[str, Any]   ,		_snake_case						:		str):

        """simple docstring"""


        UpperCAmelCase_		=				self.language_code_re.match(_snake_case)
        UpperCAmelCase_		=				[match.group(0)] if match else []
        return code, self.language_code_re.sub(''''''   ,		_snake_case)







   def lowerCamelCase		( self						:		Tuple   ,		_snake_case						:		str):

        """simple docstring"""


        UpperCAmelCase_	,			UpperCAmelCase_		=				self.remove_language_code(_snake_case)
        UpperCAmelCase_		=				self.current_spm.encode(_snake_case   ,		out_type=_snake_case)
        return code + pieces







   def lowerCamelCase		( self						:		str   ,		_snake_case						:		int):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		str   ,		_snake_case						:		Dict   ,		**_snake_case						:		Optional[Any]):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		List[Any]   ,		_snake_case						:		Optional[Any]   ,		**_snake_case						:		Tuple):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		str   ,		_snake_case						:		List[str]):

        """simple docstring"""


        UpperCAmelCase_		=				self.spm_source if self._decode_use_source_tokenizer else self.spm_target
        UpperCAmelCase_		=				[]
        UpperCAmelCase_		=				''''''
        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 + " "
                  UpperCAmelCase_		=				[]
             else:
                  current_sub_tokens.append(_snake_case)
        out_string += sp_model.decode_pieces(_snake_case)
        return out_string.strip()







   def lowerCamelCase		( self						:		List[Any]   ,		_snake_case						:		Union[str, Any]   ,		_snake_case						:		List[Any]=None):

        """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 lowerCamelCase		( self						:		int):

        """simple docstring"""


        UpperCAmelCase_		=				self.spm_source
        UpperCAmelCase_		=				self.encoder







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        UpperCAmelCase_		=				self.spm_target
        if self.separate_vocabs:
             UpperCAmelCase_		=				self.target_encoder







   @property
   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        return len(self.encoder)







   def lowerCamelCase		( self						:		Optional[int]   ,		_snake_case						:		str   ,		_snake_case						:		Optional[str] = None):

        """simple docstring"""


        if not os.path.isdir(_snake_case):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
             return
        UpperCAmelCase_		=				[]

        if self.separate_vocabs:
             UpperCAmelCase_		=				os.path.join(
                 _snake_case   ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']   ,		)
             UpperCAmelCase_		=				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:
             UpperCAmelCase_		=				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]   ,		):
             UpperCAmelCase_		=				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:
                       UpperCAmelCase_		=				spm_model.serialized_model_proto()
                       fi.write(_snake_case)
                  saved_files.append(_snake_case)

        return tuple(_snake_case)







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        return self.get_src_vocab()







   def lowerCamelCase		( self						:		Any):

        """simple docstring"""


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







   def lowerCamelCase		( self						:		List[str]):

        """simple docstring"""


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







   def __getstate__( self						:		int):

        """simple docstring"""


        UpperCAmelCase_		=				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]   ,		_snake_case						:		Dict):

        """simple docstring"""


        UpperCAmelCase_		=				d

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

        UpperCAmelCase_	,			UpperCAmelCase_		=				(load_spm(_snake_case   ,		self.sp_model_kwargs) for f in self.spm_files)
        UpperCAmelCase_		=				self.spm_source
        self._setup_normalizer()







   def lowerCamelCase		( self						:		List[str]   ,		*_snake_case						:		Dict   ,		**_snake_case						:		List[str]):

        """simple docstring"""


        return 1







   def lowerCamelCase		( self						:		Optional[Any]   ,		_snake_case						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				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						:		Dict   ,		_snake_case						:		List   ,		_snake_case						:		Optional[List] = None   ,		_snake_case						:		bool = False):

        """simple docstring"""


        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]			)  -> sentencepiece.SentencePieceProcessor:







     """simple docstring"""


     UpperCAmelCase_		=				sentencepiece.SentencePieceProcessor(**__A			)
     spm.Load(__A			)
     return spm


def   A		(__A  :       Dict  ,							__A  :       str			)  -> None:







     """simple docstring"""


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


def   A		(__A  :       str			)  -> Union[Dict, List]:







     """simple docstring"""


     with open(__A  ,							'''r'''			) as f:
          return json.load(__A			)












import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


snake_case_					:					Dict       						=			logging.get_logger(__name__)







class __snake_case     (     a						):







   def __init__( self						:		Union[str, Any]   ,		*_snake_case						:		int   ,		**_snake_case						:		List[str]):

        """simple docstring"""


        warnings.warn(
            '''The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
            ''' use LevitImageProcessor instead.'''   ,		_snake_case   ,		)
        super().__init__(*_snake_case   ,		**_snake_case)






