



'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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

a   :			Optional[Any]					  =    {"""vocab_file""": """sentencepiece.bpe.model"""}

a   :			Any					  =    {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

a   :			int					  =    {
    """camembert-base""": 5_1_2,
}

a   :			List[Any]					  =    """▁"""

class 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = VOCAB_FILES_NAMES
	lowercase          = PRETRAINED_VOCAB_FILES_MAP
	lowercase          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase          = ['input_ids', 'attention_mask']





	def __init__(  self		,		A		,		A="<s>"		,		A="</s>"		,		A="</s>"		,		A="<s>"		,		A="<unk>"		,		A="<pad>"		,		A="<mask>"		,		A=["<s>NOTUSED", "</s>NOTUSED"]		,		A = None		,		**A		,		)			->			None:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase     :		Optional[int]							     =		AddedToken(A		,		lstrip=A		,		rstrip=A    ) if isinstance(A		,		A    ) else mask_token

								UpperCAmelCase     :		int							     =		{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=A		,		eos_token=A		,		unk_token=A		,		sep_token=A		,		cls_token=A		,		pad_token=A		,		mask_token=A		,		additional_special_tokens=A		,		sp_model_kwargs=self.sp_model_kwargs		,		**A		,		)
								UpperCAmelCase     :		Union[str, Any]							     =		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
								self.sp_model.Load(str(A    )    )
								UpperCAmelCase     :		List[str]							     =		vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								UpperCAmelCase     :		Union[str, Any]							     =		{"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
								UpperCAmelCase     :		List[str]							     =		len(self.fairseq_tokens_to_ids    )
								UpperCAmelCase     :		Tuple							     =		len(self.sp_model    ) + len(self.fairseq_tokens_to_ids    )
								UpperCAmelCase     :		Union[str, Any]							     =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}





	def  _lowercase(  self		,		A		,		A = None    )			->			List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase     :		int							     =		[self.cls_token_id]
								UpperCAmelCase     :		List[str]							     =		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep





	def  _lowercase(  self		,		A		,		A = None		,		A = False    )			->			List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=A		,		token_ids_a=A		,		already_has_special_tokens=A    )

								if token_ids_a is None:
															return [1] + ([0] * len(A    )) + [1]
								return [1] + ([0] * len(A    )) + [1, 1] + ([0] * len(A    )) + [1]





	def  _lowercase(  self		,		A		,		A = None    )			->			List[int]:
								UpperCAmelCase     :		Optional[int]							     =		[self.sep_token_id]
								UpperCAmelCase     :		List[Any]							     =		[self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep    ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





	@property
	def  _lowercase(  self    )			->			List[Any]:
								return len(self.fairseq_tokens_to_ids    ) + len(self.sp_model    )





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Any							     =		{self.convert_ids_to_tokens(A    ): i for i in range(self.vocab_size    )}
								vocab.update(self.added_tokens_encoder    )
								return vocab





	def  _lowercase(  self		,		A    )			->			List[str]:
								return self.sp_model.encode(A		,		out_type=A    )





	def  _lowercase(  self		,		A    )			->			Dict:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(A    ) == 0:
															# Convert sentence piece unk token to fairseq unk token index
															return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(A    )





	def  _lowercase(  self		,		A    )			->			Tuple:
								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset    )





	def  _lowercase(  self		,		A    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]
								UpperCAmelCase     :		Tuple							     =		""""""
								UpperCAmelCase     :		List[str]							     =		False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(A    ) + token
																						UpperCAmelCase     :		Tuple							     =		True
																						UpperCAmelCase     :		List[Any]							     =		[]
															else:
																						current_sub_tokens.append(A    )
																						UpperCAmelCase     :		List[Any]							     =		False
								out_string += self.sp_model.decode(A    )
								return out_string.strip()





	def __getstate__(  self    )			->			Optional[int]:
								UpperCAmelCase     :		str							     =		self.__dict__.copy()
								UpperCAmelCase     :		Any							     =		None
								return state





	def __setstate__(  self		,		A    )			->			Any:
								UpperCAmelCase     :		Dict							     =		d

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

								UpperCAmelCase     :		Dict							     =		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
								self.sp_model.Load(self.vocab_file    )






	def  _lowercase(  self		,		A		,		A = None    )			->			Tuple[str]:
								if not os.path.isdir(A    ):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
															return
								UpperCAmelCase     :		Union[str, Any]							     =		os.path.join(
								    A		,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

								if os.path.abspath(self.vocab_file    ) != os.path.abspath(A    ) and os.path.isfile(self.vocab_file    ):
															copyfile(self.vocab_file		,		A    )
								elif not os.path.isfile(self.vocab_file    ):
															with open(A		,		"""wb"""    ) as fi:
																						UpperCAmelCase     :		List[str]							     =		self.sp_model.serialized_model_proto()
																						fi.write(A    )

								return (out_vocab_file,)






'''simple docstring'''




from PIL import Image



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Image:

							def brightness(_lowercase		) -> float:
														return 1_2_8 + level + (c - 1_2_8)

							if not -255.0 <= level <= 255.0:
														raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""		)
							return img.point(_lowercase		)


if __name__ == "__main__":
							# Load image
							with Image.open("""image_data/lena.jpg""") as img:
														# Change brightness to 100
														a   :			Optional[Any]					  =    change_brightness(img, 1_0_0)
														brigt_img.save("""image_data/lena_brightness.png""", format="""png""")

