






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

a		:	List[str]      					=			{
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

a		:	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"
        )
    },
}

a		:	Union[str, Any]      					=			{"Helsinki-NLP/opus-mt-en-de": 512}
a		:	Dict      					=			{}
class    a   (			lowerCamelCase__       ):



						"""simple docstring"""


						a  :			Optional[int]								=     VOCAB_FILES_NAMES
						a  :			Optional[int]								=     PRETRAINED_VOCAB_FILES_MAP
						a  :			str								=     PRETRAINED_INIT_CONFIGURATION
						a  :			int								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a  :			Optional[Any]								=     ['input_ids', 'attention_mask']
						a  :			List[Any]								=     re.compile('>>.+<<'       )  # type: re.Pattern
						def __init__(				self :      Dict		,				__lowercase :      List[str]		,				__lowercase :      int		,				__lowercase :      Union[str, Any]		,				__lowercase :      Dict=None		,				__lowercase :      List[str]=None		,				__lowercase :      List[Any]=None		,				__lowercase :      Any="<unk>"		,				__lowercase :      List[str]="</s>"		,				__lowercase :      str="<pad>"		,				__lowercase :      Union[str, Any]=512		,				__lowercase :      List[Any] = None		,				__lowercase :      Optional[int]=False		,				**__lowercase :      List[Any]		,				)							->				None:
								__UpperCAmelCase							:     Optional[Any]									=  {} 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=lowercase_		,				target_lang=lowercase_		,				unk_token=lowercase_		,				eos_token=lowercase_		,				pad_token=lowercase_		,				model_max_length=lowercase_		,				sp_model_kwargs=self.sp_model_kwargs		,				target_vocab_file=lowercase_		,				separate_vocabs=lowercase_		,				**lowercase_		,				)
								assert Path(lowercase_ ).exists(), f"""cannot find spm source {source_spm}"""

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

								__UpperCAmelCase							:     Union[str, Any]									=  source_lang
								__UpperCAmelCase							:     List[Any]									=  target_lang
								__UpperCAmelCase							:     Optional[int]									=  [source_spm, target_spm]

								# load SentencePiece model for pre-processing
								__UpperCAmelCase							:     Optional[Any]									=  load_spm(lowercase_		,				self.sp_model_kwargs )
								__UpperCAmelCase							:     int									=  load_spm(lowercase_		,				self.sp_model_kwargs )
								__UpperCAmelCase							:     int									=  self.spm_source
								__UpperCAmelCase							:     Tuple									=  self.encoder

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

								self._setup_normalizer()
						def      UpperCAmelCase				(				self :      List[Any] )							->				Union[str, Any]:






								try:
										from sacremoses import MosesPunctNormalizer

										__UpperCAmelCase							:     Tuple									=  MosesPunctNormalizer(self.source_lang ).normalize
								except (ImportError, FileNotFoundError):
										warnings.warn("""Recommended: pip install sacremoses.""" )
										__UpperCAmelCase							:     Dict									=  lambda __lowercase : x
						def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      Tuple )							->				str:
								return self.punc_normalizer(lowercase_ ) if x else ""
						def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      List[str] )							->				int:
								return self.current_encoder.get(lowercase_		,				self.current_encoder[self.unk_token] )
						def      UpperCAmelCase				(				self :      int		,				__lowercase :      Dict )							->				Optional[Any]:
								__UpperCAmelCase							:     Optional[int]									=  self.language_code_re.match(lowercase_ )
								__UpperCAmelCase							:     Tuple									=  [match.group(0 )] if match else []
								return code, self.language_code_re.sub(""""""		,				lowercase_ )
						def      UpperCAmelCase				(				self :      Union[str, Any]		,				__lowercase :      str )							->				List[str]:
								__UpperCAmelCase				,			__UpperCAmelCase							:     List[str]									=  self.remove_language_code(lowercase_ )
								__UpperCAmelCase							:     Union[str, Any]									=  self.current_spm.encode(lowercase_		,				out_type=lowercase_ )
								return code + pieces
						def      UpperCAmelCase				(				self :      int		,				__lowercase :      Any )							->				str:
								return self.decoder.get(lowercase_		,				self.unk_token )
						def      UpperCAmelCase				(				self :      Any		,				__lowercase :      Dict		,				**__lowercase :      Union[str, Any] )							->				Any:
								return super().batch_decode(lowercase_		,				**lowercase_ )
						def      UpperCAmelCase				(				self :      List[str]		,				__lowercase :      Optional[int]		,				**__lowercase :      Optional[int] )							->				List[Any]:
								return super().decode(lowercase_		,				**lowercase_ )
						def      UpperCAmelCase				(				self :      str		,				__lowercase :      List[Any] )							->				str:
								__UpperCAmelCase							:     str									=  self.spm_source if self._decode_use_source_tokenizer else self.spm_target
								__UpperCAmelCase							:     Any									=  []
								__UpperCAmelCase							:     int									=  """"""
								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(lowercase_ ) + token + " "
												__UpperCAmelCase							:     Dict									=  []
										else:
												current_sub_tokens.append(lowercase_ )
								out_string += sp_model.decode_pieces(lowercase_ )
								return out_string.strip()
						def      UpperCAmelCase				(				self :      Tuple		,				__lowercase :      Union[str, Any]		,				__lowercase :      List[str]=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 :      Optional[int] )							->				Optional[Any]:
								__UpperCAmelCase							:     int									=  self.spm_source
								__UpperCAmelCase							:     int									=  self.encoder
						def      UpperCAmelCase				(				self :      Optional[Any] )							->				Union[str, Any]:
								__UpperCAmelCase							:     int									=  self.spm_target
								if self.separate_vocabs:
										__UpperCAmelCase							:     Any									=  self.target_encoder
						@property
						def      UpperCAmelCase				(				self :      Any )							->				int:
								return len(self.encoder )
						def      UpperCAmelCase				(				self :      int		,				__lowercase :      int		,				__lowercase :      List[Any] = None )							->				Tuple[str]:






								if not os.path.isdir(lowercase_ ):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
										return
								__UpperCAmelCase							:     List[str]									=  []

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

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

								return tuple(lowercase_ )
						def      UpperCAmelCase				(				self :      Optional[int] )							->				Dict:
								return self.get_src_vocab()
						def      UpperCAmelCase				(				self :      Optional[Any] )							->				Tuple:
								return dict(self.encoder		,				**self.added_tokens_encoder )
						def      UpperCAmelCase				(				self :      Dict )							->				Optional[Any]:
								return dict(self.target_encoder		,				**self.added_tokens_decoder )
						def __getstate__(				self :      Dict )							->				Dict:
								__UpperCAmelCase							:     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 :      Union[str, Any]		,				__lowercase :      List[str] )							->				None:
								__UpperCAmelCase							:     List[Any]									=  d

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

								__UpperCAmelCase				,			__UpperCAmelCase							:     Dict									=  (load_spm(lowercase_		,				self.sp_model_kwargs ) for f in self.spm_files)
								__UpperCAmelCase							:     str									=  self.spm_source
								self._setup_normalizer()
						def      UpperCAmelCase				(				self :      Tuple		,				*__lowercase :      Optional[Any]		,				**__lowercase :      Optional[Any] )							->				Tuple:
								return 1
						def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      str )							->				Dict:
								__UpperCAmelCase							:     Optional[Any]									=  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]		,				__lowercase :      str		,				__lowercase :      List[str] = None		,				__lowercase :      str = False )							->				List[int]:






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






def     lowerCamelCase__				(   __lowerCamelCase      :					str  ,		__lowerCamelCase      :					Dict[str, Any]		):
		__UpperCAmelCase							:     Union[str, Any]									=  sentencepiece.SentencePieceProcessor(**_A		)
		spm.Load(_A		)
		return spm






def     lowerCamelCase__				(   __lowerCamelCase      :					Tuple  ,		__lowerCamelCase      :					str		):







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







def     lowerCamelCase__				(   __lowerCamelCase      :					str		):







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










def     lowerCamelCase__				(   __lowerCamelCase      :					list		):
  if len(__lowerCamelCase		) <= 1:
    return data
  else:
    return [
        *quick_sort([e for e in data[1:] if e <= data[0]]		),
        data[0],
        *quick_sort([e for e in data[1:] if e > data[0]]		),
    ]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


