





"""simple docstring"""






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






       def __init__( self     ,	a__     ,	a__     ,	a__     ,	a__=None     ,	a__=None     ,	a__=None     ,	a__="<unk>"     ,	a__="</s>"     ,	a__="<pad>"     ,	a__=5_12     ,	a__ = None     ,	a__=False     ,	**a__     ,	):
              _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=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}"""

              _UpperCAmelCase			     =  separate_vocabs
              _UpperCAmelCase			     =  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:
                     _UpperCAmelCase			     =  load_json(a__  )
                     _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(a__     ,	self.sp_model_kwargs  )
              _UpperCAmelCase			     =  load_spm(a__     ,	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 				__A			( self  ):
              try:
                     from sacremoses import MosesPunctNormalizer

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






       def 				__A			( self     ,	a__  ):
              return self.punc_normalizer(a__  ) if x else ""






       def 				__A			( self     ,	a__  ):
              return self.current_encoder.get(a__     ,	self.current_encoder[self.unk_token]  )






       def 				__A			( self     ,	a__  ):
              _UpperCAmelCase			     =  self.language_code_re.match(a__  )
              _UpperCAmelCase			     =  [match.group(0  )] if match else []
              return code, self.language_code_re.sub(''     ,	a__  )






       def 				__A			( self     ,	a__  ):
              _UpperCAmelCase     ,				_UpperCAmelCase			     =  self.remove_language_code(a__  )
              _UpperCAmelCase			     =  self.current_spm.encode(a__     ,	out_type=a__  )
              return code + pieces






       def 				__A			( self     ,	a__  ):
              return self.decoder.get(a__     ,	self.unk_token  )






       def 				__A			( self     ,	a__     ,	**a__  ):
              return super().batch_decode(a__     ,	**a__  )






       def 				__A			( self     ,	a__     ,	**a__  ):
              return super().decode(a__     ,	**a__  )






       def 				__A			( self     ,	a__  ):
              _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(a__  ) + token + " "
                            _UpperCAmelCase			     =  []
                     else:
                            current_sub_tokens.append(a__  )
              out_string += sp_model.decode_pieces(a__  )
              return out_string.strip()






       def 				__A			( self     ,	a__     ,	a__=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 				__A			( self  ):
              _UpperCAmelCase			     =  self.spm_source
              _UpperCAmelCase			     =  self.encoder






       def 				__A			( self  ):
              _UpperCAmelCase			     =  self.spm_target
              if self.separate_vocabs:
                     _UpperCAmelCase			     =  self.target_encoder






       @property
       def 				__A			( self  ):
              return len(self.encoder  )






       def 				__A			( self     ,	a__     ,	a__ = None  ):
              if not os.path.isdir(a__  ):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
                     return
              _UpperCAmelCase			     =  []

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

              return tuple(a__  )






       def 				__A			( self  ):
              return self.get_src_vocab()






       def 				__A			( self  ):
              return dict(self.encoder     ,	**self.added_tokens_encoder  )






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






       def __getstate__( self  ):
              _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     ,	a__  ):
              _UpperCAmelCase			     =  d

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

              _UpperCAmelCase     ,				_UpperCAmelCase			     =  (load_spm(a__     ,	self.sp_model_kwargs  ) for f in self.spm_files)
              _UpperCAmelCase			     =  self.spm_source
              self._setup_normalizer()






       def 				__A			( self     ,	*a__     ,	**a__  ):
              return 1






       def 				__A			( self     ,	a__  ):
              _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 				__A			( self     ,	a__     ,	a__ = None     ,	a__ = False  ):
              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    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							sentencepiece.SentencePieceProcessor:






       """simple docstring"""




       _UpperCAmelCase			     =  sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE				)
       spm.Load(SCREAMING_SNAKE_CASE				)
       return spm

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				)		->							None:






       """simple docstring"""




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


def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Union[Dict, List]:






       """simple docstring"""




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













"""simple docstring"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							bool:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       if len(SCREAMING_SNAKE_CASE				) == 1:
              return True
       try:
              _UpperCAmelCase			     =  series[1] / series[0]
              for index in range(len(SCREAMING_SNAKE_CASE				) - 1				):
                     if series[index + 1] / series[index] != common_ratio:
                            return False
       except ZeroDivisionError:
              return False
       return True

def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							float:






       """simple docstring"""




       if not isinstance(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE				):
              raise ValueError('Input series is not valid, valid series - [2, 4, 8]'				)
       if len(SCREAMING_SNAKE_CASE				) == 0:
              raise ValueError('Input list must be a non empty list'				)
       _UpperCAmelCase			     =  1
       for value in series:
              answer *= value
       return pow(SCREAMING_SNAKE_CASE,1 / len(SCREAMING_SNAKE_CASE				)				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()






