
"""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


a		:					Any    	=					logging.get_logger(__name__)

a		:					Tuple    	=					{
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

a		:					List[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'
        )
    },
}

a		:					int    	=					{'Helsinki-NLP/opus-mt-en-de': 5_12}
a		:					str    	=					{}







class 						lowercase(_SCREAMING_SNAKE_CASE					):
     __snake_case:  Tuple  				=    VOCAB_FILES_NAMES
     __snake_case:  Optional[Any]  				=    PRETRAINED_VOCAB_FILES_MAP
     __snake_case:  Any  				=    PRETRAINED_INIT_CONFIGURATION
     __snake_case:  Any  				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case:  Optional[Any]  				=    ['input_ids', 'attention_mask']
     __snake_case:  Optional[Any]  				=    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_1_2    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE=False    ,							**__SCREAMING_SNAKE_CASE    ,							)		->    Optional[Any]:







         """simple docstring"""



         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  lowercase__     (       self      )		->    Any:







         """simple docstring"""



         try:
             from sacremoses import MosesPunctNormalizer

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



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







         """simple docstring"""



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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Optional[Any]:







         """simple docstring"""



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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Any:







         """simple docstring"""



         a__    = self.language_code_re.match(UpperCamelCase__      )
         a__    = [match.group(0      )] if match else []
         return code, self.language_code_re.sub(''    ,							UpperCamelCase__      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Dict:







         """simple docstring"""



         a__  ,    a__    = self.remove_language_code(UpperCamelCase__      )
         a__    = self.current_spm.encode(UpperCamelCase__    ,							out_type=UpperCamelCase__      )
         return code + pieces



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







         """simple docstring"""



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



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







         """simple docstring"""



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



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







         """simple docstring"""



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



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    Tuple:







         """simple docstring"""



         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  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=None      )		->    Union[str, Any]:







         """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  lowercase__     (       self      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = self.spm_source
         a__    = self.encoder



     def  lowercase__     (       self      )		->    Union[str, Any]:







         """simple docstring"""



         a__    = self.spm_target
         if self.separate_vocabs:
             a__    = self.target_encoder



     @property
     def  lowercase__     (       self      )		->    Dict:







         """simple docstring"""



         return len(self.encoder      )



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None      )		->    Optional[int]:







         """simple docstring"""



         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  lowercase__     (       self      )		->    Tuple:







         """simple docstring"""



         return self.get_src_vocab()



     def  lowercase__     (       self      )		->    Any:







         """simple docstring"""



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



     def  lowercase__     (       self      )		->    List[Any]:







         """simple docstring"""



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



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







         """simple docstring"""



         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    ,							__SCREAMING_SNAKE_CASE      )		->    Dict:







         """simple docstring"""



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







         """simple docstring"""



         return 1



     def  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE      )		->    List[Any]:







         """simple docstring"""



         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  lowercase__     (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = False      )		->    Any:







         """simple docstring"""



         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 						__magic_name__		(				UpperCamelCase    :		List[Any]       ,		UpperCamelCase    :		Optional[Any]    )    ->	List[Any]:
    a__    = sentencepiece.SentencePieceProcessor(**UpperCamelCase    )
    spm.Load(UpperCamelCase    )
    return spm



def 						__magic_name__		(				UpperCamelCase    :		Any       ,		UpperCamelCase    :		List[Any]    )    ->	str:







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



def 						__magic_name__		(				UpperCamelCase    :		Any    )    ->	Union[str, Any]:







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




"""simple docstring"""




from itertools import combinations, product



def 						__magic_name__		(				UpperCamelCase    :		int       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int       ,		UpperCamelCase    :		int    )    ->	bool:
    if xa == ya == 0 or xa == ya == 0:
        return False
    a__    = xa * xa + ya * ya
    a__    = xa * xa + ya * ya
    a__    = (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
    return (
        a_square + b_square == c_square
        or a_square + c_square == b_square
        or b_square + c_square == a_square
    )



def 						__magic_name__		(				UpperCamelCase    :		int = 50    )    ->	int:
    return sum(
        1
        for pta, pta in combinations(product(range(limit + 1    )       ,		repeat=2    )       ,		2    )
        if is_right(*UpperCamelCase       ,		*UpperCamelCase    )    )


if __name__ == "__main__":
    print(F'''{solution() = }''')


