


'''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   )







'''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()



