


"""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


__SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE					=						{
    """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"""
        )
    },
}

__SCREAMING_SNAKE_CASE					=						{"""Helsinki-NLP/opus-mt-en-de""": 5_12}
__SCREAMING_SNAKE_CASE					=						{}





class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




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




   def __init__(		self							:Optional[int]				,  UpperCamelCase__							:List[Any]				,  UpperCamelCase__							:int				,  UpperCamelCase__							:List[Any]				,  UpperCamelCase__							:Dict=None				,  UpperCamelCase__							:List[Any]=None				,  UpperCamelCase__							:Any=None				,  UpperCamelCase__							:int="<unk>"				,  UpperCamelCase__							:List[Any]="</s>"				,  UpperCamelCase__							:int="<pad>"				,  UpperCamelCase__							:str=512				,  UpperCamelCase__							:Union[str, Any] = None				,  UpperCamelCase__							:int=False				,  **UpperCamelCase__							:List[Any]				,  ):
          _a          =				{} 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=UpperCamelCase__				,  target_lang=UpperCamelCase__				,  unk_token=UpperCamelCase__				,  eos_token=UpperCamelCase__				,  pad_token=UpperCamelCase__				,  model_max_length=UpperCamelCase__				,  sp_model_kwargs=self.sp_model_kwargs				,  target_vocab_file=UpperCamelCase__				,  separate_vocabs=UpperCamelCase__				,  **UpperCamelCase__				,  )
          assert Path(UpperCamelCase__       ).exists(), f'cannot find spm source {source_spm}'

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

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

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

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

          self._setup_normalizer()




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]       ):







          try:
                 from sacremoses import MosesPunctNormalizer

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




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Dict       ):
          return self.punc_normalizer(UpperCamelCase__       ) if x else ""




   def SCREAMING_SNAKE_CASE_      (		self							:Dict				,  UpperCamelCase__							:Union[str, Any]       ):
          return self.current_encoder.get(UpperCamelCase__				,  self.current_encoder[self.unk_token]       )




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:List[str]       ):
          _a          =				self.language_code_re.match(UpperCamelCase__       )
          _a          =				[match.group(0       )] if match else []
          return code, self.language_code_re.sub(""				,  UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:Optional[Any]       ):
          _a   , _a          =				self.remove_language_code(UpperCamelCase__       )
          _a          =				self.current_spm.encode(UpperCamelCase__				,  out_type=UpperCamelCase__       )
          return code + pieces




   def SCREAMING_SNAKE_CASE_      (		self							:int				,  UpperCamelCase__							:List[Any]       ):
          return self.decoder.get(UpperCamelCase__				,  self.unk_token       )




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:int				,  **UpperCamelCase__							:int       ):
          return super().batch_decode(UpperCamelCase__				,  **UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str				,  **UpperCamelCase__							:List[str]       ):
          return super().decode(UpperCamelCase__				,  **UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Any				,  UpperCamelCase__							:List[Any]       ):
          _a          =				self.spm_source if self._decode_use_source_tokenizer else self.spm_target
          _a          =				[]
          _a          =				""
          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(UpperCamelCase__       ) + token + " "
                        _a          =				[]
                 else:
                        current_sub_tokens.append(UpperCamelCase__       )
          out_string += sp_model.decode_pieces(UpperCamelCase__       )
          return out_string.strip()




   def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  UpperCamelCase__							:Optional[Any]				,  UpperCamelCase__							:List[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 SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
          _a          =				self.spm_source
          _a          =				self.encoder




   def SCREAMING_SNAKE_CASE_      (		self							:str       ):
          _a          =				self.spm_target
          if self.separate_vocabs:
                 _a          =				self.target_encoder




   @property
   def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
          return len(self.encoder       )




   def SCREAMING_SNAKE_CASE_      (		self							:int				,  UpperCamelCase__							:Any				,  UpperCamelCase__							:Any = None       ):







          if not os.path.isdir(UpperCamelCase__       ):
                 logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
                 return
          _a          =				[]

          if self.separate_vocabs:
                 _a          =				os.path.join(
                     UpperCamelCase__				,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]				,  )
                 _a          =				os.path.join(
                     UpperCamelCase__				,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]				,  )
                 save_json(self.encoder				,  UpperCamelCase__       )
                 save_json(self.target_encoder				,  UpperCamelCase__       )
                 saved_files.append(UpperCamelCase__       )
                 saved_files.append(UpperCamelCase__       )
          else:
                 _a          =				os.path.join(
                     UpperCamelCase__				,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]       )
                 save_json(self.encoder				,  UpperCamelCase__       )
                 saved_files.append(UpperCamelCase__       )

          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          =				os.path.join(
                     UpperCamelCase__				,  (filename_prefix + "-" if filename_prefix else "") + spm_save_filename       )
                 if os.path.abspath(UpperCamelCase__       ) != os.path.abspath(UpperCamelCase__       ) and os.path.isfile(UpperCamelCase__       ):
                        copyfile(UpperCamelCase__				,  UpperCamelCase__       )
                        saved_files.append(UpperCamelCase__       )
                 elif not os.path.isfile(UpperCamelCase__       ):
                        with open(UpperCamelCase__				,  "wb"       ) as fi:
                               _a          =				spm_model.serialized_model_proto()
                               fi.write(UpperCamelCase__       )
                        saved_files.append(UpperCamelCase__       )

          return tuple(UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
          return self.get_src_vocab()




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




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




   def __getstate__(		self							:int       ):
          _a          =				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]				,  UpperCamelCase__							:Tuple       ):
          _a          =				d

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

          _a   , _a          =				(load_spm(UpperCamelCase__				,  self.sp_model_kwargs       ) for f in self.spm_files)
          _a          =				self.spm_source
          self._setup_normalizer()




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  *UpperCamelCase__							:Union[str, Any]				,  **UpperCamelCase__							:int       ):
          return 1




   def SCREAMING_SNAKE_CASE_      (		self							:Any				,  UpperCamelCase__							:Optional[int]       ):
          _a          =				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 SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:List[str]				,  UpperCamelCase__							:Dict = None				,  UpperCamelCase__							:str = False       ):







          if already_has_special_tokens:
                 return self._special_token_mask(UpperCamelCase__       )
          elif token_ids_a is None:
                 return self._special_token_mask(UpperCamelCase__       ) + [1]
          else:
                 return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]






def 						__a			(  a,		a					):





       """simple docstring"""







       _a          =				sentencepiece.SentencePieceProcessor(**__snake_case					)
       spm.Load(__snake_case					)
       return spm



def 						__a			(  a,		a					):





       """simple docstring"""







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



def 						__a			(  a					):





       """simple docstring"""







       with open(__snake_case,		"r"					) as f:
              return json.load(__snake_case					)




"""simple docstring"""




from __future__ import annotations

__SCREAMING_SNAKE_CASE					=						[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class __snake_case		:







   """simple docstring"""




   def __init__(		self							:Optional[Any]				,  UpperCamelCase__							:int       ):
          _a          =				-1
          _a          =				-1
          _a          =				[-1] * length
          _a          =				None
          _a          =				None




   def __repr__(		self							:Optional[Any]       ):
          return f'Node(min_value={self.minn} max_value={self.maxx})'






def 						__a      (  a					):





       """simple docstring"""







       _a          =				Node(len(a					)					)
       _a   , _a          =				min(a					), max(a					)
       # Leaf node case where the node contains only one unique value
       if root.minn == root.maxx:
              return root
       _a          =				(root.minn + root.maxx) // 2

       _a          =				[]
       _a          =				[]

       for index, num in enumerate(a					):
              if num <= pivot:
                     left_arr.append(a					)
              else:
                     right_arr.append(a					)
              _a          =				len(a					)
       _a          =				build_tree(a					)
       _a          =				build_tree(a					)
       return root



def 						__a      (  a,		a,		a					):





       """simple docstring"""







       if index < 0 or node is None:
              return 0
       # Leaf node cases
       if node.minn == node.maxx:
              return index + 1 if node.minn == num else 0
       _a          =				(node.minn + node.maxx) // 2
       if num <= pivot:
              # go the left subtree and map index to the left subtree
              return rank_till_index(node.left,		a,		node.map_left[index] - 1					)
       else:
              # go to the right subtree and map index to the right subtree
              return rank_till_index(node.right,		a,		index - node.map_left[index]					)



def 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







       if start > end:
              return 0
       _a          =				rank_till_index(a,		a,		a					)
       _a          =				rank_till_index(a,		a,		start - 1					)
       return rank_till_end - rank_before_start



def 						__a      (  a,		a,		a,		a					):





       """simple docstring"""







       if index > (end - start) or start > end or node is None:
              return -1
       # Leaf node case
       if node.minn == node.maxx:
              return node.minn
       # Number of elements in the left subtree in interval [start, end]
       _a          =				node.map_left[end] - (
           node.map_left[start - 1] if start else 0
       )
       if num_elements_in_left_tree > index:
              return quantile(
                  node.left,		a,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		)
       else:
              return quantile(
                  node.right,		index - num_elements_in_left_tree,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		)



def 						__a      (  a,		a,		a,		a,		a					):





       """simple docstring"""







       if (
           start > end
           or node is None
           or start_num > end_num
           or node.minn > end_num
           or node.maxx < start_num
       ):
              return 0
       if start_num <= node.minn and node.maxx <= end_num:
              return end - start + 1
       _a          =				range_counting(
           node.left,		(node.map_left[start - 1] if start else 0),		node.map_left[end] - 1,		a,		a,		)
       _a          =				range_counting(
           node.right,		start - (node.map_left[start - 1] if start else 0),		end - node.map_left[end],		a,		a,		)
       return left + right


if __name__ == "__main__":
   import doctest

   doctest.testmod()
