


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


__lowerCamelCase   	=  logging.get_logger(__name__)

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

__lowerCamelCase   	=  {
    '''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'''
        )
    },
}

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


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  List[Any]				     =  VOCAB_FILES_NAMES
       lowerCamelCase__:  Optional[int]				     =  PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase__:  Optional[Any]				     =  PRETRAINED_INIT_CONFIGURATION
       lowerCamelCase__:  int				     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase__:  Dict				     =  ["""input_ids""", """attention_mask"""]
       lowerCamelCase__:  Optional[int]				     =  re.compile(""">>.+<<"""     )  # type: re.Pattern

       def __init__( self   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Dict   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int=None   ,				lowerCamelCase_   :  int=None   ,				lowerCamelCase_   :  Optional[Any]=None   ,				lowerCamelCase_   :  List[Any]="<unk>"   ,				lowerCamelCase_   :  Optional[Any]="</s>"   ,				lowerCamelCase_   :  Tuple="<pad>"   ,				lowerCamelCase_   :  Dict=5_1_2   ,				lowerCamelCase_   :  Optional[Dict[str, Any]] = None   ,				lowerCamelCase_   :  List[str]=False   ,				**lowerCamelCase_   :  int   ,				):
            a_     :  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=lowerCamelCase_   ,				target_lang=lowerCamelCase_   ,				unk_token=lowerCamelCase_   ,				eos_token=lowerCamelCase_   ,				pad_token=lowerCamelCase_   ,				model_max_length=lowerCamelCase_   ,				sp_model_kwargs=self.sp_model_kwargs   ,				target_vocab_file=lowerCamelCase_   ,				separate_vocabs=lowerCamelCase_   ,				**lowerCamelCase_   ,				)
            assert Path(lowerCamelCase_     ).exists(), F'''cannot find spm source {source_spm}'''

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

            a_     :  Dict				      =     source_lang
            a_     :  Any				      =     target_lang
            a_     :  str				      =     [source_spm, target_spm]

            # load SentencePiece model for pre-processing
            a_     :  str				      =     load_spm(lowerCamelCase_   ,				self.sp_model_kwargs     )
            a_     :  Any				      =     load_spm(lowerCamelCase_   ,				self.sp_model_kwargs     )
            a_     :  Tuple				      =     self.spm_source
            a_     :  Tuple				      =     self.encoder

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

            self._setup_normalizer()

       def     UpperCAmelCase( self   :  Tuple     ):
            try:
                 from sacremoses import MosesPunctNormalizer

                 a_     :  Union[str, Any]				      =     MosesPunctNormalizer(self.source_lang     ).normalize
            except (ImportError, FileNotFoundError):
                 warnings.warn("""Recommended: pip install sacremoses."""     )
                 a_     :  Any				      =     lambda lowerCamelCase_     : x

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  str     ):
            return self.punc_normalizer(lowerCamelCase_     ) if x else ""

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  Union[str, Any]     ):
            return self.current_encoder.get(lowerCamelCase_   ,				self.current_encoder[self.unk_token]     )

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  str     ):
            a_     :  str				      =     self.language_code_re.match(lowerCamelCase_     )
            a_     :  list				      =     [match.group(0     )] if match else []
            return code, self.language_code_re.sub(""""""   ,				lowerCamelCase_     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  str     ):
            a_      ,      a_     :  str				      =     self.remove_language_code(lowerCamelCase_     )
            a_     :  List[str]				      =     self.current_spm.encode(lowerCamelCase_   ,				out_type=lowerCamelCase_     )
            return code + pieces

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  int     ):
            return self.decoder.get(lowerCamelCase_   ,				self.unk_token     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  int   ,				**lowerCamelCase_   :  Tuple     ):
            return super().batch_decode(lowerCamelCase_   ,				**lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  str   ,				**lowerCamelCase_   :  Any     ):
            return super().decode(lowerCamelCase_   ,				**lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  List[str]     ):
            a_     :  Optional[Any]				      =     self.spm_source if self._decode_use_source_tokenizer else self.spm_target
            a_     :  Optional[Any]				      =     []
            a_     :  str				      =     """"""
            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(lowerCamelCase_     ) + token + " "
                      a_     :  Dict				      =     []
                 else:
                      current_sub_tokens.append(lowerCamelCase_     )
            out_string += sp_model.decode_pieces(lowerCamelCase_     )
            return out_string.strip()

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  Any=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     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  Optional[Any]				      =     self.spm_source
            a_     :  Optional[Any]				      =     self.encoder

       def     UpperCAmelCase( self   :  List[str]     ):
            a_     :  Dict				      =     self.spm_target
            if self.separate_vocabs:
                 a_     :  List[Any]				      =     self.target_encoder

       @property
       def     UpperCAmelCase( self   :  str     ):
            return len(self.encoder     )

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[str] = None     ):
            if not os.path.isdir(lowerCamelCase_     ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
                 return
            a_     :  Tuple				      =     []

            if self.separate_vocabs:
                 a_     :  Optional[Any]				      =     os.path.join(
                     lowerCamelCase_   ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]   ,				)
                 a_     :  List[Any]				      =     os.path.join(
                     lowerCamelCase_   ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]   ,				)
                 save_json(self.encoder   ,				lowerCamelCase_     )
                 save_json(self.target_encoder   ,				lowerCamelCase_     )
                 saved_files.append(lowerCamelCase_     )
                 saved_files.append(lowerCamelCase_     )
            else:
                 a_     :  Optional[Any]				      =     os.path.join(
                     lowerCamelCase_   ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]     )
                 save_json(self.encoder   ,				lowerCamelCase_     )
                 saved_files.append(lowerCamelCase_     )

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

            return tuple(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Tuple     ):
            return self.get_src_vocab()

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            return dict(self.encoder   ,				**self.added_tokens_encoder     )

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

       def __getstate__( self   :  Optional[Any]     ):
            a_     :  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   :  Optional[Any]   ,				lowerCamelCase_   :  Dict     ):
            a_     :  List[str]				      =     d

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

            a_      ,      a_     :  Optional[int]				      =     (load_spm(lowerCamelCase_   ,				self.sp_model_kwargs     ) for f in self.spm_files)
            a_     :  int				      =     self.spm_source
            self._setup_normalizer()

       def     UpperCAmelCase( self   :  Optional[int]   ,				*lowerCamelCase_   :  str   ,				**lowerCamelCase_   :  int     ):
            return 1

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  List[Any]     ):
            a_     :  Tuple				      =     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   :  List[Any]   ,				lowerCamelCase_   :  List   ,				lowerCamelCase_   :  Optional[List] = None   ,				lowerCamelCase_   :  bool = False     ):
            if already_has_special_tokens:
                 return self._special_token_mask(lowerCamelCase_     )
            elif token_ids_a is None:
                 return self._special_token_mask(lowerCamelCase_     ) + [1]
            else:
                 return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Dict				      =     sentencepiece.SentencePieceProcessor(**__UpperCamelCase			)
     spm.Load(__UpperCamelCase			)
     return spm



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     with open(__UpperCamelCase	,	"""w"""			) as f:
          json.dump(__UpperCamelCase	,	__UpperCamelCase	,	indent=2			)



def 			_a      (    __UpperCamelCase			):
     with open(__UpperCamelCase	,	"""r"""			) as f:
          return json.load(__UpperCamelCase			)







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase   	=  {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  [
           '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TvltModel''',
           '''TvltForPreTraining''',
           '''TvltForAudioVisualClassification''',
           '''TvltPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase   	=  ['''TvltImageProcessor''']


if TYPE_CHECKING:
       from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
       from .processing_tvlt import TvltProcessor
       from .feature_extraction_tvlt import TvltFeatureExtractor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tvlt import (
                  TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TvltForAudioVisualClassification,
                  TvltForPreTraining,
                  TvltModel,
                  TvltPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_tvlt import TvltImageProcessor


else:
       import sys

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



