


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 							lowercase__(						snake_case__			):






      '''simple docstring'''







      snake_case__       =					VOCAB_FILES_NAMES
      snake_case__       =					PRETRAINED_VOCAB_FILES_MAP
      snake_case__       =					PRETRAINED_INIT_CONFIGURATION
      snake_case__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__       =					['''input_ids''', '''attention_mask''']
      snake_case__       =					re.compile('''>>.+<<'''			)  # type: re.Pattern




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







         """simple docstring"""







         UpperCamelCase__ :			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=__SCREAMING_SNAKE_CASE   ,			target_lang=__SCREAMING_SNAKE_CASE   ,			unk_token=__SCREAMING_SNAKE_CASE   ,			eos_token=__SCREAMING_SNAKE_CASE   ,			pad_token=__SCREAMING_SNAKE_CASE   ,			model_max_length=__SCREAMING_SNAKE_CASE   ,			sp_model_kwargs=self.sp_model_kwargs   ,			target_vocab_file=__SCREAMING_SNAKE_CASE   ,			separate_vocabs=__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE   ,			)
         assert Path(__SCREAMING_SNAKE_CASE).exists(), F'''cannot find spm source {source_spm}'''

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

         UpperCamelCase__ :			str 						=source_lang
         UpperCamelCase__ :			Optional[Any] 						=target_lang
         UpperCamelCase__ :			Optional[Any] 						=[source_spm, target_spm]

         # load SentencePiece model for pre-processing
         UpperCamelCase__ :			Any 						=load_spm(__SCREAMING_SNAKE_CASE   ,			self.sp_model_kwargs)
         UpperCamelCase__ :			Tuple 						=load_spm(__SCREAMING_SNAKE_CASE   ,			self.sp_model_kwargs)
         UpperCamelCase__ :			Dict 						=self.spm_source
         UpperCamelCase__ :			Tuple 						=self.encoder

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

         self._setup_normalizer()




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         try:
            from sacremoses import MosesPunctNormalizer

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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         return self.punc_normalizer(__SCREAMING_SNAKE_CASE) if x else ""




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







         return self.current_encoder.get(__SCREAMING_SNAKE_CASE   ,			self.current_encoder[self.unk_token])




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=self.language_code_re.match(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			list 						=[match.group(0)] if match else []
         return code, self.language_code_re.sub(""   ,			__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__						,	UpperCamelCase__ :			int 						=self.remove_language_code(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Optional[Any] 						=self.current_spm.encode(__SCREAMING_SNAKE_CASE   ,			out_type=__SCREAMING_SNAKE_CASE)
         return code + pieces




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         return self.decoder.get(__SCREAMING_SNAKE_CASE   ,			self.unk_token)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







         return super().batch_decode(__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







         return super().decode(__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=self.spm_source if self._decode_use_source_tokenizer else self.spm_target
         UpperCamelCase__ :			Dict 						=[]
         UpperCamelCase__ :			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(__SCREAMING_SNAKE_CASE) + token + " "
               UpperCamelCase__ :			Optional[Any] 						=[]
            else:
               current_sub_tokens.append(__SCREAMING_SNAKE_CASE)
         out_string += sp_model.decode_pieces(__SCREAMING_SNAKE_CASE)
         return out_string.strip()




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=None)					->						List[int]:







         """simple docstring"""







         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)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=self.spm_source
         UpperCamelCase__ :			Union[str, Any] 						=self.encoder




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=self.spm_target
         if self.separate_vocabs:
            UpperCamelCase__ :			str 						=self.target_encoder




      @property
      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         return len(self.encoder)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = None)					->						Tuple[str]:







         """simple docstring"""







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

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

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

         return tuple(__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						Dict:







         """simple docstring"""







         return self.get_src_vocab()




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         return dict(self.encoder   ,			**self.added_tokens_encoder)




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         return dict(self.target_encoder   ,			**self.added_tokens_decoder)




      def __getstate__(		self)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			Optional[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   ,			__SCREAMING_SNAKE_CASE)					->						None:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=d

         # for backward compatibility
         if not hasattr(self   ,			"sp_model_kwargs"):
            UpperCamelCase__ :			List[str] 						={}

         UpperCamelCase__						,	UpperCamelCase__ :			Dict 						=(load_spm(__SCREAMING_SNAKE_CASE   ,			self.sp_model_kwargs) for f in self.spm_files)
         UpperCamelCase__ :			Any 						=self.spm_source
         self._setup_normalizer()




      def    UpperCAmelCase							(		self   ,			*__SCREAMING_SNAKE_CASE   ,			**__SCREAMING_SNAKE_CASE)					->						List[str]:







         """simple docstring"""







         return 1




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			int 						=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   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = False)					->						List[int]:







         """simple docstring"""







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




def 	_lowerCamelCase  (		A_  :	str							,  A_  :	Dict[str, Any]      )			->			sentencepiece.SentencePieceProcessor:

   '''simple docstring'''






   UpperCamelCase__ :			Tuple 						=sentencepiece.SentencePieceProcessor(**A_      )
   spm.Load(A_      )
   return spm
def 	_lowerCamelCase  (		A_  :	Tuple							,  A_  :	str      )			->			None:

   '''simple docstring'''






   with open(A_							,  "w"      ) as f:
      json.dump(A_							,  A_							,  indent=2      )
def 	_lowerCamelCase  (		A_  :	str      )			->			Union[Dict, List]:

   '''simple docstring'''






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





from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap



@total_ordering
class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						None:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=word
         UpperCamelCase__ :			Dict 						=count




      def __eq__(		self   ,			__SCREAMING_SNAKE_CASE)					->						bool:







         """simple docstring"""







         if not isinstance(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
            return NotImplemented
         return self.count == other.count




      def __lt__(		self   ,			__SCREAMING_SNAKE_CASE)					->						bool:







         """simple docstring"""







         if not isinstance(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE):
            return NotImplemented
         return self.count < other.count




def 	_lowerCamelCase  (		A_  :	list[str]							,  A_  :	int      )			->			list[str]:

   '''simple docstring'''






   UpperCamelCase__ :			Heap[WordCount] 						=Heap()
   UpperCamelCase__ :			Any 						=Counter(A_      )
   heap.build_max_heap(
       [WordCount(A_							,  A_      ) for word, count in count_by_word.items()]      )
   return [heap.extract_max().word for _ in range(min(A_							,  len(A_      )      )      )]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

