

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						:   Tuple										=    logging.get_logger(__name__)

lowerCAmelCase						:   List[Any]										=    {
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase						:   Optional[Any]										=    {
    '''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						:   List[Any]										=    {'''Helsinki-NLP/opus-mt-en-de''': 512}
lowerCAmelCase						:   List[str]										=    {}
class     SCREAMING_SNAKE_CASE__     (						__a		):





       '''simple docstring'''




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

       def __init__(						self       :						str    ,		lowerCAmelCase__       :						List[str]    ,		lowerCAmelCase__       :						int    ,		lowerCAmelCase__       :						Optional[int]    ,		lowerCAmelCase__       :						int=None    ,		lowerCAmelCase__       :						Any=None    ,		lowerCAmelCase__       :						int=None    ,		lowerCAmelCase__       :						List[str]="<unk>"    ,		lowerCAmelCase__       :						int="</s>"    ,		lowerCAmelCase__       :						Union[str, Any]="<pad>"    ,		lowerCAmelCase__       :						Any=512    ,		lowerCAmelCase__       :						Optional[Dict[str, Any]] = None    ,		lowerCAmelCase__       :						Dict=False    ,		**lowerCAmelCase__       :						Union[str, Any]    ,		)			->    None:
             snake_case__					=   {} 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}'''

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

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

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

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

             self._setup_normalizer()

       def UpperCAmelCase_						(						self       :						Any							)			->    Any:
             try:
                   from sacremoses import MosesPunctNormalizer

                   snake_case__					=   MosesPunctNormalizer(self.source_lang							).normalize
             except (ImportError, FileNotFoundError):
                   warnings.warn("""Recommended: pip install sacremoses."""							)
                   snake_case__					=   lambda lowerCAmelCase__							: x

       def UpperCAmelCase_						(						self       :						Tuple    ,		lowerCAmelCase__       :						str							)			->    str:
             return self.punc_normalizer(lowerCAmelCase__							) if x else ""

       def UpperCAmelCase_						(						self       :						str    ,		lowerCAmelCase__       :						Any							)			->    Any:
             return self.current_encoder.get(lowerCAmelCase__    ,		self.current_encoder[self.unk_token]							)

       def UpperCAmelCase_						(						self       :						Optional[int]    ,		lowerCAmelCase__       :						str							)			->    Union[str, Any]:
             snake_case__					=   self.language_code_re.match(lowerCAmelCase__							)
             snake_case__					=   [match.group(0							)] if match else []
             return code, self.language_code_re.sub(""""""    ,		lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						List[str]    ,		lowerCAmelCase__       :						str							)			->    List[str]:
             snake_case__	,			snake_case__					=   self.remove_language_code(lowerCAmelCase__							)
             snake_case__					=   self.current_spm.encode(lowerCAmelCase__    ,		out_type=lowerCAmelCase__							)
             return code + pieces

       def UpperCAmelCase_						(						self       :						List[Any]    ,		lowerCAmelCase__       :						int							)			->    str:
             return self.decoder.get(lowerCAmelCase__    ,		self.unk_token							)

       def UpperCAmelCase_						(						self       :						str    ,		lowerCAmelCase__       :						Any    ,		**lowerCAmelCase__       :						Any							)			->    str:
             return super().batch_decode(lowerCAmelCase__    ,		**lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						List[str]    ,		lowerCAmelCase__       :						Optional[int]    ,		**lowerCAmelCase__       :						Optional[int]							)			->    int:
             return super().decode(lowerCAmelCase__    ,		**lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Optional[Any]    ,		lowerCAmelCase__       :						List[str]							)			->    str:
             snake_case__					=   self.spm_source if self._decode_use_source_tokenizer else self.spm_target
             snake_case__					=   []
             snake_case__					=   """"""
             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 + " "
                         snake_case__					=   []
                   else:
                         current_sub_tokens.append(lowerCAmelCase__							)
             out_string += sp_model.decode_pieces(lowerCAmelCase__							)
             return out_string.strip()

       def UpperCAmelCase_						(						self       :						Optional[int]    ,		lowerCAmelCase__       :						List[str]    ,		lowerCAmelCase__       :						Union[str, Any]=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       :						int							)			->    int:
             snake_case__					=   self.spm_source
             snake_case__					=   self.encoder

       def UpperCAmelCase_						(						self       :						List[str]							)			->    Dict:
             snake_case__					=   self.spm_target
             if self.separate_vocabs:
                   snake_case__					=   self.target_encoder

       @property
       def UpperCAmelCase_						(						self       :						Dict							)			->    int:
             return len(self.encoder							)

       def UpperCAmelCase_						(						self       :						Optional[Any]    ,		lowerCAmelCase__       :						str    ,		lowerCAmelCase__       :						Optional[str] = None							)			->    Tuple[str]:
             if not os.path.isdir(lowerCAmelCase__							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                   return
             snake_case__					=   []

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

             return tuple(lowerCAmelCase__							)

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

       def UpperCAmelCase_						(						self       :						Optional[int]							)			->    List[str]:
             return dict(self.encoder    ,		**self.added_tokens_encoder							)

       def UpperCAmelCase_						(						self       :						int							)			->    Dict:
             return dict(self.target_encoder    ,		**self.added_tokens_decoder							)

       def __getstate__(						self       :						str							)			->    Dict:
             snake_case__					=   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]    ,		lowerCAmelCase__       :						Dict							)			->    None:
             snake_case__					=   d

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

             snake_case__	,			snake_case__					=   (load_spm(lowerCAmelCase__    ,		self.sp_model_kwargs							) for f in self.spm_files)
             snake_case__					=   self.spm_source
             self._setup_normalizer()

       def UpperCAmelCase_						(						self       :						Union[str, Any]    ,		*lowerCAmelCase__       :						Optional[int]    ,		**lowerCAmelCase__       :						Union[str, Any]							)			->    str:
             return 1

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





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





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









import sys





def 			_lowercase    (      __UpperCamelCase     :		int   ,       __UpperCamelCase     :		list[int] ):
      if not numbers:
            return 0
      if target == 0 and target in numbers:
            return 0
      if not isinstance(__UpperCamelCase   ,       (list, tuple) ) or not all(
          isinstance(__UpperCamelCase   ,       __UpperCamelCase ) for number in numbers ):
            raise ValueError("""numbers must be an iterable of integers""" )

      snake_case__					=   snake_case__					=   snake_case__					=   0
      snake_case__					=   sys.maxsize

      while right < len(__UpperCamelCase ):
            curr_sum += numbers[right]
            while curr_sum >= target and left <= right:
                  snake_case__					=   min(__UpperCamelCase   ,       right - left + 1 )
                  curr_sum -= numbers[left]
                  left += 1
            right += 1

      return 0 if min_len == sys.maxsize else min_len






