code: 
  
  
  '''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
  
  
  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''': 5_1_2}
  UpperCAmelCase__      =							{}
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Dict							= VOCAB_FILES_NAMES
  			A	:							Any							= PRETRAINED_VOCAB_FILES_MAP
  			A	:							Optional[Any]							= PRETRAINED_INIT_CONFIGURATION
  			A	:							str							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			A	:							Dict							= ['''input_ids''', '''attention_mask''']
  			A	:							Tuple							= re.compile('''>>.+<<'''      )  # type: re.Pattern
  			def __init__(     self       : Dict ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : List[str]=None ,						lowerCAmelCase_       : Optional[Any]=None ,						lowerCAmelCase_       : int=None ,						lowerCAmelCase_       : Optional[int]="<unk>" ,						lowerCAmelCase_       : Tuple="</s>" ,						lowerCAmelCase_       : Optional[Any]="<pad>" ,						lowerCAmelCase_       : Tuple=512 ,						lowerCAmelCase_       : Optional[Dict[str, Any]] = None ,						lowerCAmelCase_       : int=False ,						**lowerCAmelCase_       : Optional[Any] ,						)			->		None:
  						__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=lowerCAmelCase_ ,						target_lang=lowerCAmelCase_ ,						unk_token=lowerCAmelCase_ ,						eos_token=lowerCAmelCase_ ,						pad_token=lowerCAmelCase_ ,						model_max_length=lowerCAmelCase_ ,						sp_model_kwargs=self.sp_model_kwargs ,						target_vocab_file=lowerCAmelCase_ ,						separate_vocabs=lowerCAmelCase_ ,						**lowerCAmelCase_ ,						)
  						assert Path(lowerCAmelCase_  ).exists(), F"""cannot find spm source {source_spm}"""
  
  						__A=  separate_vocabs
  						__A=  load_json(lowerCAmelCase_  )
  						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(lowerCAmelCase_  )
  									__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(lowerCAmelCase_ ,						self.sp_model_kwargs  )
  						__A=  load_spm(lowerCAmelCase_ ,						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 				lowerCAmelCase    (     self       : Optional[int]  )			->		List[str]:
  						try:
  									from sacremoses import MosesPunctNormalizer
  
  									__A=  MosesPunctNormalizer(self.source_lang  ).normalize
  						except (ImportError, FileNotFoundError):
  									warnings.warn('Recommended: pip install sacremoses.'  )
  									__A=  lambda lowerCAmelCase_  : x
  			def 				lowerCAmelCase    (     self       : Optional[Any] ,						lowerCAmelCase_       : str  )			->		str:
  						return self.punc_normalizer(lowerCAmelCase_  ) if x else ""
  			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Union[str, Any]  )			->		str:
  						return self.current_encoder.get(lowerCAmelCase_ ,						self.current_encoder[self.unk_token]  )
  			def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : str  )			->		int:
  						__A=  self.language_code_re.match(lowerCAmelCase_  )
  						__A=  [match.group(0  )] if match else []
  						return code, self.language_code_re.sub('' ,						lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : str  )			->		List[str]:
  						__A,  __A=  self.remove_language_code(lowerCAmelCase_  )
  						__A=  self.current_spm.encode(lowerCAmelCase_ ,						out_type=lowerCAmelCase_  )
  						return code + pieces
  			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : int  )			->		str:
  						return self.decoder.get(lowerCAmelCase_ ,						self.unk_token  )
  			def 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : List[Any] ,						**lowerCAmelCase_       : str  )			->		Any:
  						return super().batch_decode(lowerCAmelCase_ ,						**lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Union[str, Any] ,						lowerCAmelCase_       : Optional[int] ,						**lowerCAmelCase_       : Optional[int]  )			->		Dict:
  						return super().decode(lowerCAmelCase_ ,						**lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : List[str]  )			->		str:
  						__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(lowerCAmelCase_  ) + token + " "
  												__A=  []
  									else:
  												current_sub_tokens.append(lowerCAmelCase_  )
  						out_string += sp_model.decode_pieces(lowerCAmelCase_  )
  						return out_string.strip()
  			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : Union[str, Any] ,						lowerCAmelCase_       : 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 				lowerCAmelCase    (     self       : Tuple  )			->		int:
  						__A=  self.spm_source
  						__A=  self.encoder
  			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Dict:
  						__A=  self.spm_target
  						if self.separate_vocabs:
  									__A=  self.target_encoder
  			@property
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		int:
  						return len(self.encoder  )
  			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Optional[str] = None  )			->		Tuple[str]:
  						if not os.path.isdir(lowerCAmelCase_  ):
  									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
  									return
  						__A=  []
  
  						if self.separate_vocabs:
  									__A=  os.path.join(
  									    lowerCAmelCase_ ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab'] ,						)
  									__A=  os.path.join(
  									    lowerCAmelCase_ ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file'] ,						)
  									save_json(self.encoder ,						lowerCAmelCase_  )
  									save_json(self.target_encoder ,						lowerCAmelCase_  )
  									saved_files.append(lowerCAmelCase_  )
  									saved_files.append(lowerCAmelCase_  )
  						else:
  									__A=  os.path.join(
  									    lowerCAmelCase_ ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']  )
  									save_json(self.encoder ,						lowerCAmelCase_  )
  									saved_files.append(lowerCAmelCase_  )
  
  						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(
  									    lowerCAmelCase_ ,						(filename_prefix + '-' if filename_prefix else '') + spm_save_filename  )
  									if os.path.abspath(lowerCAmelCase_  ) != os.path.abspath(lowerCAmelCase_  ) and os.path.isfile(lowerCAmelCase_  ):
  												copyfile(lowerCAmelCase_ ,						lowerCAmelCase_  )
  												saved_files.append(lowerCAmelCase_  )
  									elif not os.path.isfile(lowerCAmelCase_  ):
  												with open(lowerCAmelCase_ ,						'wb'  ) as fi:
  															__A=  spm_model.serialized_model_proto()
  															fi.write(lowerCAmelCase_  )
  												saved_files.append(lowerCAmelCase_  )
  
  						return tuple(lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Dict:
  						return self.get_src_vocab()
  			def 				lowerCAmelCase    (     self       : Any  )			->		Optional[int]:
  						return dict(self.encoder ,						**self.added_tokens_encoder  )
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		int:
  						return dict(self.target_encoder ,						**self.added_tokens_decoder  )
  			def __getstate__(     self       : int  )			->		Dict:
  						__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       : List[str] ,						lowerCAmelCase_       : Dict  )			->		None:
  						__A=  d
  
  						# for backward compatibility
  						if not hasattr(self ,						'sp_model_kwargs'  ):
  									__A=  {}
  
  						__A,  __A=  (load_spm(lowerCAmelCase_ ,						self.sp_model_kwargs  ) for f in self.spm_files)
  						__A=  self.spm_source
  						self._setup_normalizer()
  			def 				lowerCAmelCase    (     self       : str ,						*lowerCAmelCase_       : int ,						**lowerCAmelCase_       : Optional[int]  )			->		Union[str, Any]:
  						return 1
  			def 				lowerCAmelCase    (     self       : Union[str, Any] ,						lowerCAmelCase_       : Any  )			->		Optional[Any]:
  						__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 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : List ,						lowerCAmelCase_       : Optional[List] = None ,						lowerCAmelCase_       : bool = False  )			->		List[int]:
  						if already_has_special_tokens:
  									return self._special_token_mask(lowerCAmelCase_  )
  						elif token_ids_a is None:
  									return self._special_token_mask(lowerCAmelCase_  ) + [1]
  						else:
  									return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   Dict[str, Any]   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  sentencepiece.SentencePieceProcessor(**_SCREAMING_SNAKE_CASE   )
  			spm.Load(_SCREAMING_SNAKE_CASE   )
  			return spm
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Dict,_SCREAMING_SNAKE_CASE				:   str   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			with open(_SCREAMING_SNAKE_CASE,'w'   ) as f:
  						json.dump(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,indent=2   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   str   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			with open(_SCREAMING_SNAKE_CASE,'r'   ) as f:
  						return json.load(_SCREAMING_SNAKE_CASE   )
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  import numpy as np
  from cva import destroyAllWindows, imread, imshow, waitKey
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Dict ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : int  )			->		Optional[int]:
  						__A=  0
  						# max greyscale value for #FFFFFF
  						__A=  int(self.get_greyscale(255 ,						255 ,						255  )  )
  
  						if not self.min_threshold < threshold < self.max_threshold:
  									__A=  F"""Factor value should be from 0 to {self.max_threshold}"""
  									raise ValueError(lowerCAmelCase_  )
  
  						__A=  input_img
  						__A=  threshold
  						__A,  __A=  self.input_img.shape[1], self.input_img.shape[0]
  
  						# error table size (+4 columns and +1 row) greater than input image because of
  						# lack of if statements
  						__A=  [
  						    [0 for _ in range(self.height + 4  )] for __ in range(self.width + 1  )
  						]
  						__A=  np.ones((self.width, self.height, 3) ,						np.uinta  ) * 255
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : Tuple ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : int  )			->		float:
  						return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red
  
  			def 				lowerCAmelCase    (     self       : str  )			->		None:
  						for y in range(self.height  ):
  									for x in range(self.width  ):
  												__A=  int(self.get_greyscale(*self.input_img[y][x]  )  )
  												if self.threshold > greyscale + self.error_table[y][x]:
  															__A=  (0, 0, 0)
  															__A=  greyscale + self.error_table[x][y]
  												else:
  															__A=  (255, 255, 255)
  															__A=  greyscale + self.error_table[x][y] - 255
  												self.error_table[y][x + 1] += int(8 / 32 * current_error  )
  												self.error_table[y][x + 2] += int(4 / 32 * current_error  )
  												self.error_table[y + 1][x] += int(8 / 32 * current_error  )
  												self.error_table[y + 1][x + 1] += int(4 / 32 * current_error  )
  												self.error_table[y + 1][x + 2] += int(2 / 32 * current_error  )
  												self.error_table[y + 1][x - 1] += int(4 / 32 * current_error  )
  												self.error_table[y + 1][x - 2] += int(2 / 32 * current_error  )
  
  
  if __name__ == "__main__":
  					# create Burke's instances with original images in greyscale
  					UpperCAmelCase__      =							[
  					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
  					    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
  					]
  
  					for burkes in burkes_instances:
  										burkes.process()
  
  					for burkes in burkes_instances:
  										imshow(
  										    F"""Original image with dithering threshold: {burkes.threshold}""",
  										    burkes.output_img,
  										)
  
  					waitKey(0)
  					destroyAllWindows()
  
  
  
  