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     snake_case_			(						__A			):
   __A      :						Any   =							VOCAB_FILES_NAMES
   __A      :						List[Any]   =							PRETRAINED_VOCAB_FILES_MAP
   __A      :						Tuple   =							PRETRAINED_INIT_CONFIGURATION
   __A      :						Any   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A      :						Dict   =							["input_ids", "attention_mask"]
   __A      :						Optional[Any]   =							re.compile(">>.+<<"			)  # type: re.Pattern






   def __init__( self							:	str      ,		lowercase_							:	Optional[int]      ,		lowercase_							:	Tuple      ,		lowercase_							:	Tuple      ,		lowercase_							:	Optional[int]=None      ,		lowercase_							:	Any=None      ,		lowercase_							:	Optional[Any]=None      ,		lowercase_							:	Any="<unk>"      ,		lowercase_							:	Optional[int]="</s>"      ,		lowercase_							:	str="<pad>"      ,		lowercase_							:	Tuple=5_12      ,		lowercase_							:	Optional[Dict[str, Any]] = None      ,		lowercase_							:	List[Any]=False      ,		**lowercase_							:	List[str]      ,		)   ->		None:
       lowercase__					:  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}'''

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

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

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

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

       self._setup_normalizer()






   def  __UpperCamelCase	( self							:	List[str]    )   ->		int:
       try:
           from sacremoses import MosesPunctNormalizer

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






   def  __UpperCamelCase	( self							:	Optional[int]      ,		lowercase_							:	str    )   ->		str:
       return self.punc_normalizer(lowercase_    ) if x else ""






   def  __UpperCamelCase	( self							:	Tuple      ,		lowercase_							:	List[str]    )   ->		Tuple:
       return self.current_encoder.get(lowercase_      ,		self.current_encoder[self.unk_token]    )






   def  __UpperCamelCase	( self							:	Optional[int]      ,		lowercase_							:	str    )   ->		List[str]:
       lowercase__					:  Optional[int]          =							self.language_code_re.match(lowercase_    )
       lowercase__					:  list          =							[match.group(0    )] if match else []
       return code, self.language_code_re.sub(""      ,		lowercase_    )






   def  __UpperCamelCase	( self							:	str      ,		lowercase_							:	str    )   ->		List[str]:
       lowercase__   ,  lowercase__					:  Union[str, Any]          =							self.remove_language_code(lowercase_    )
       lowercase__					:  int          =							self.current_spm.encode(lowercase_      ,		out_type=lowercase_    )
       return code + pieces






   def  __UpperCamelCase	( self							:	Optional[Any]      ,		lowercase_							:	int    )   ->		str:
       return self.decoder.get(lowercase_      ,		self.unk_token    )






   def  __UpperCamelCase	( self							:	Tuple      ,		lowercase_							:	int      ,		**lowercase_							:	int    )   ->		Optional[int]:
       return super().batch_decode(lowercase_      ,		**lowercase_    )






   def  __UpperCamelCase	( self							:	int      ,		lowercase_							:	Optional[int]      ,		**lowercase_							:	List[Any]    )   ->		List[Any]:
       return super().decode(lowercase_      ,		**lowercase_    )






   def  __UpperCamelCase	( self							:	Union[str, Any]      ,		lowercase_							:	List[str]    )   ->		str:
       lowercase__					:  Dict          =							self.spm_source if self._decode_use_source_tokenizer else self.spm_target
       lowercase__					:  str          =							[]
       lowercase__					:  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 + " "
               lowercase__					:  Tuple          =							[]
           else:
               current_sub_tokens.append(lowercase_    )
       out_string += sp_model.decode_pieces(lowercase_    )
       return out_string.strip()






   def  __UpperCamelCase	( self							:	Union[str, Any]      ,		lowercase_							:	Tuple      ,		lowercase_							:	str=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							:	Optional[Any]    )   ->		str:
       lowercase__					:  Any          =							self.spm_source
       lowercase__					:  List[Any]          =							self.encoder






   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		Dict:
       lowercase__					:  List[str]          =							self.spm_target
       if self.separate_vocabs:
           lowercase__					:  Tuple          =							self.target_encoder






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






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

       if self.separate_vocabs:
           lowercase__					:  Any          =							os.path.join(
               lowercase_      ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]      ,		)
           lowercase__					:  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:
           lowercase__					:  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]      ,		):
           lowercase__					:  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:
                   lowercase__					:  List[str]          =							spm_model.serialized_model_proto()
                   fi.write(lowercase_    )
               saved_files.append(lowercase_    )

       return tuple(lowercase_    )






   def  __UpperCamelCase	( self							:	Optional[Any]    )   ->		Dict:
       return self.get_src_vocab()






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






   def  __UpperCamelCase	( self							:	Union[str, Any]    )   ->		List[Any]:
       return dict(self.target_encoder      ,		**self.added_tokens_decoder    )






   def __getstate__( self							:	Dict    )   ->		Dict:
       lowercase__					:  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							:	Union[str, Any]      ,		lowercase_							:	Dict    )   ->		None:
       lowercase__					:  Optional[Any]          =							d

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

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






   def  __UpperCamelCase	( self							:	Union[str, Any]      ,		*lowercase_							:	Dict      ,		**lowercase_							:	Union[str, Any]    )   ->		Optional[int]:
       return 1






   def  __UpperCamelCase	( self							:	str      ,		lowercase_							:	Optional[int]    )   ->		Optional[int]:
       lowercase__					:  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  __UpperCamelCase	( self							:	int      ,		lowercase_							:	List      ,		lowercase_							:	Optional[List] = None      ,		lowercase_							:	bool = False    )   ->		List[int]:
       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      lowercase_							(     _lowerCamelCase				:			str						,      _lowerCamelCase				:			Dict[str, Any]):
    lowercase__					:  Tuple          =							sentencepiece.SentencePieceProcessor(**_lowerCamelCase)
    spm.Load(_lowerCamelCase)
    return spm

def      lowercase_							(     _lowerCamelCase				:			Optional[int]						,      _lowerCamelCase				:			str):
    with open(_lowerCamelCase						,      "w") as f:
        json.dump(_lowerCamelCase						,      _lowerCamelCase						,      indent=2)

def      lowercase_							(     _lowerCamelCase				:			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__)



