




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      :				Optional[Any]				      = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      :				Dict				      = {
    """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      :				str				      = {
    """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      :				Any				      = {"""Helsinki-NLP/opus-mt-en-de""": 512}
__SCREAMING_SNAKE_CASE      :				Tuple				      = {}






class      A     (						_lowerCAmelCase						):
     """simple docstring"""

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




     def __init__(      self					:				Tuple		,					A__					:				List[str]		,					A__					:				str		,					A__					:				Tuple		,					A__					:				Optional[Any]=None		,					A__					:				List[str]=None		,					A__					:				str=None		,					A__					:				int="<unk>"		,					A__					:				List[str]="</s>"		,					A__					:				Optional[int]="<pad>"		,					A__					:				int=5_1_2		,					A__					:				Optional[Dict[str, Any]] = None		,					A__					:				Tuple=False		,					**A__					:				List[str]		,					)			->    None:
           lowerCamelCase__ 					=			{} 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}"""

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

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

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

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

           self._setup_normalizer()




     def 	__snake_case				(      self					:				Dict     )			->    Union[str, Any]:
           try:
                 from sacremoses import MosesPunctNormalizer

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




     def 	__snake_case				(      self					:				Dict		,					A__					:				str     )			->    str:
           return self.punc_normalizer(A__     ) if x else ""




     def 	__snake_case				(      self					:				int		,					A__					:				List[Any]     )			->    List[str]:
           return self.current_encoder.get(A__		,					self.current_encoder[self.unk_token]     )




     def 	__snake_case				(      self					:				str		,					A__					:				str     )			->    str:
           lowerCamelCase__ 					=			self.language_code_re.match(A__     )
           lowerCamelCase__ 					=			[match.group(0     )] if match else []
           return code, self.language_code_re.sub(''		,					A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				str     )			->    List[str]:
           lowerCamelCase__					,						lowerCamelCase__ 					=			self.remove_language_code(A__     )
           lowerCamelCase__ 					=			self.current_spm.encode(A__		,					out_type=A__     )
           return code + pieces




     def 	__snake_case				(      self					:				Any		,					A__					:				int     )			->    str:
           return self.decoder.get(A__		,					self.unk_token     )




     def 	__snake_case				(      self					:				List[Any]		,					A__					:				Optional[Any]		,					**A__					:				str     )			->    Union[str, Any]:
           return super().batch_decode(A__		,					**A__     )




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				Optional[Any]		,					**A__					:				Optional[int]     )			->    str:
           return super().decode(A__		,					**A__     )




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




     def 	__snake_case				(      self					:				List[str]		,					A__					:				Optional[Any]		,					A__					:				Dict=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 	__snake_case				(      self					:				int     )			->    Optional[Any]:
           lowerCamelCase__ 					=			self.spm_source
           lowerCamelCase__ 					=			self.encoder




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    Any:
           lowerCamelCase__ 					=			self.spm_target
           if self.separate_vocabs:
                 lowerCamelCase__ 					=			self.target_encoder




     @property
     def 	__snake_case				(      self					:				List[str]     )			->    int:
           return len(self.encoder     )




     def 	__snake_case				(      self					:				Any		,					A__					:				str		,					A__					:				Optional[str] = None     )			->    Tuple[str]:
           if not os.path.isdir(A__     ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
                 return
           lowerCamelCase__ 					=			[]

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

           return tuple(A__     )




     def 	__snake_case				(      self					:				Any     )			->    Dict:
           return self.get_src_vocab()




     def 	__snake_case				(      self					:				List[Any]     )			->    Tuple:
           return dict(self.encoder		,					**self.added_tokens_encoder     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    Any:
           return dict(self.target_encoder		,					**self.added_tokens_decoder     )




     def __getstate__(      self					:				Union[str, Any]     )			->    Dict:
           lowerCamelCase__ 					=			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					:				int		,					A__					:				Dict     )			->    None:
           lowerCamelCase__ 					=			d

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

           lowerCamelCase__					,						lowerCamelCase__ 					=			(load_spm(A__		,					self.sp_model_kwargs     ) for f in self.spm_files)
           lowerCamelCase__ 					=			self.spm_source
           self._setup_normalizer()




     def 	__snake_case				(      self					:				List[str]		,					*A__					:				Any		,					**A__					:				str     )			->    Optional[Any]:
           return 1




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				Union[str, Any]     )			->    Tuple:
           lowerCamelCase__ 					=			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 	__snake_case				(      self					:				Optional[int]		,					A__					:				List		,					A__					:				Optional[List] = None		,					A__					:				bool = False     )			->    List[int]:
           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_     (					lowerCamelCase:    str       ,			lowerCamelCase:    Dict[str, Any]  )   ->     sentencepiece.SentencePieceProcessor:


      '''simple docstring'''




      lowerCamelCase__ 					=			sentencepiece.SentencePieceProcessor(**lowerCamelCase  )
      spm.Load(lowerCamelCase  )
      return spm

def      lowerCAmelCase_     (					lowerCamelCase:    List[str]       ,			lowerCamelCase:    str  )   ->     None:


      '''simple docstring'''




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


def      lowerCAmelCase_     (					lowerCamelCase:    str  )   ->     Union[Dict, List]:


      '''simple docstring'''




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





def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if right <= left:
            return
      lowerCamelCase__ 					=			lowerCamelCase__ 					=			left
      lowerCamelCase__ 					=			right
      lowerCamelCase__ 					=			sorting[left]
      while i <= b:
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[a]
                  a += 1
                  i += 1
            elif sorting[i] > pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[b]
                  b -= 1
            else:
                  i += 1
      quick_sort_apartition(lowerCamelCase       ,			lowerCamelCase       ,			a - 1  )
      quick_sort_apartition(lowerCamelCase       ,			b + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     None:


      '''simple docstring'''




      if left < right:
            lowerCamelCase__ 					=			lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			lowerCamelCase  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			lowerCamelCase       ,			pivot_index - 1  )
            quick_sort_lomuto_partition(lowerCamelCase       ,			pivot_index + 1       ,			lowerCamelCase  )

def      lowerCAmelCase_     (					lowerCamelCase:    list       ,			lowerCamelCase:    int       ,			lowerCamelCase:    int  )   ->     int:


      '''simple docstring'''




      lowerCamelCase__ 					=			sorting[right]
      lowerCamelCase__ 					=			left
      for i in range(lowerCamelCase       ,			lowerCamelCase  ):
            if sorting[i] < pivot:
                  lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[i], sorting[store_index]
                  store_index += 1
      lowerCamelCase__					,						lowerCamelCase__ 					=			sorting[store_index], sorting[right]
      return store_index

def      lowerCAmelCase_     (					lowerCamelCase:    list  )   ->     list:


      '''simple docstring'''




      if len(lowerCamelCase  ) <= 1:
            return sorting
      return (
          three_way_radix_quicksort([i for i in sorting if i < sorting[0]]  )
          + [i for i in sorting if i == sorting[0]]
          + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]  )
      )


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)

      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =input("""Enter numbers separated by a comma:\n""").strip()
      __SCREAMING_SNAKE_CASE      :				str				      =[int(item) for item in user_input.split(""",""")]
      quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
      print(unsorted)