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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE    :      Optional[int]          =			logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			'▁'

_SCREAMING_SNAKE_CASE    :      Optional[int]          =			{'vocab_file': 'spiece.model'}

_SCREAMING_SNAKE_CASE    :      int          =			{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

_SCREAMING_SNAKE_CASE    :      List[str]          =			{
    'google/reformer-crime-and-punishment': 5_2_4_2_8_8,
}







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				VOCAB_FILES_NAMES
	_lowerCAmelCase							       =				PRETRAINED_VOCAB_FILES_MAP
	_lowerCAmelCase							       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCAmelCase							       =				["input_ids", "attention_mask"]





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__="</s>"				,       UpperCamelCase__="<unk>"				,       UpperCamelCase__=[]				,       UpperCamelCase__ = None				,       **UpperCamelCase__				,       ):
					A__  :	Tuple         =							{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    eos_token=UpperCamelCase__				,       unk_token=UpperCamelCase__				,       additional_special_tokens=UpperCamelCase__				,       sp_model_kwargs=self.sp_model_kwargs				,       **UpperCamelCase__				,       )

					A__  :	Tuple         =							vocab_file
					A__  :	List[str]         =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
					self.sp_model.Load(UpperCamelCase__		)





	@property
	def   __snake_case				(			self		):
					return self.sp_model.get_piece_size()





	def   __snake_case				(			self		):
					A__  :	Dict         =							{self.convert_ids_to_tokens(UpperCamelCase__		): i for i in range(self.vocab_size		)}
					vocab.update(self.added_tokens_encoder		)
					return vocab





	def __getstate__(			self		):
					A__  :	Tuple         =							self.__dict__.copy()
					A__  :	List[str]         =							None
					return state





	def __setstate__(			self				,       UpperCamelCase__		):
					A__  :	Optional[Any]         =							d

					# for backward compatibility
					if not hasattr(self				,       '''sp_model_kwargs'''		):
									A__  :	int         =							{}

					A__  :	Union[str, Any]         =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
					self.sp_model.Load(self.vocab_file		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					return self.sp_model.encode(UpperCamelCase__				,       out_type=UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					return self.sp_model.piece_to_id(UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					if index < self.sp_model.get_piece_size():
									A__  :	Dict         =							self.sp_model.IdToPiece(UpperCamelCase__		)
					return token





	def   __snake_case				(			self				,       UpperCamelCase__		):
					A__  :	int         =							[]
					A__  :	str         =							''''''
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													out_string += self.sp_model.decode(UpperCamelCase__		) + token
													A__  :	Any         =							[]
									else:
													current_sub_tokens.append(UpperCamelCase__		)
					out_string += self.sp_model.decode(UpperCamelCase__		)
					return out_string.strip()





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__ = None		):
					if not os.path.isdir(UpperCamelCase__		):
									logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
									return
					A__  :	str         =							os.path.join(
					    UpperCamelCase__				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

					if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCamelCase__		) and os.path.isfile(self.vocab_file		):
									copyfile(self.vocab_file				,       UpperCamelCase__		)
					elif not os.path.isfile(self.vocab_file		):
									with open(UpperCamelCase__				,       '''wb'''		) as fi:
													A__  :	str         =							self.sp_model.serialized_model_proto()
													fi.write(UpperCamelCase__		)

					return (out_vocab_file,)



from PIL import Image

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Image							,						__UpperCamelCase :						float       ) ->       Image:

				"""simple docstring"""







				def brightness(__UpperCamelCase :						int       ) -> float:
								return 1_28 + level + (c - 1_28)

				if not -2_5_5.0 <= level <= 2_5_5.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''       )
				return img.point(__UpperCamelCase       )


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