
"""simple docstring"""







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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_albert import AlbertTokenizer
else:
							lowerCamelCase__   					=		None

lowerCamelCase__   					=		logging.get_logger(__name__)
lowerCamelCase__   					=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__   					=		{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase__   					=		{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

lowerCamelCase__   					=		"""▁"""




class  A__       (	_lowerCamelCase):
		A_						:       Union[str, Any]				   =	VOCAB_FILES_NAMES
		A_						:       Optional[Any]				   =	PRETRAINED_VOCAB_FILES_MAP
		A_						:       List[str]				   =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						:       Optional[int]				   =	AlbertTokenizer

		def __init__(      self       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=None       ,		_SCREAMING_SNAKE_CASE=True       ,		_SCREAMING_SNAKE_CASE=True       ,		_SCREAMING_SNAKE_CASE=False       ,		_SCREAMING_SNAKE_CASE="[CLS]"       ,		_SCREAMING_SNAKE_CASE="[SEP]"       ,		_SCREAMING_SNAKE_CASE="<unk>"       ,		_SCREAMING_SNAKE_CASE="[SEP]"       ,		_SCREAMING_SNAKE_CASE="<pad>"       ,		_SCREAMING_SNAKE_CASE="[CLS]"       ,		_SCREAMING_SNAKE_CASE="[MASK]"       ,		**_SCREAMING_SNAKE_CASE       ,		):
			# Mask token behave like a normal word, i.e. include the space before it and
			# is included in the raw text, there should be a match in a non-normalized sentence.
			__lowerCAmelCase			:						List[Any]									=      (
			    AddedToken(_SCREAMING_SNAKE_CASE       ,		lstrip=_SCREAMING_SNAKE_CASE       ,		rstrip=_SCREAMING_SNAKE_CASE       ,		normalized=_SCREAMING_SNAKE_CASE )
			    if isinstance(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE )
			    else mask_token
			)

			super().__init__(
			    _SCREAMING_SNAKE_CASE       ,		tokenizer_file=_SCREAMING_SNAKE_CASE       ,		do_lower_case=_SCREAMING_SNAKE_CASE       ,		remove_space=_SCREAMING_SNAKE_CASE       ,		keep_accents=_SCREAMING_SNAKE_CASE       ,		bos_token=_SCREAMING_SNAKE_CASE       ,		eos_token=_SCREAMING_SNAKE_CASE       ,		unk_token=_SCREAMING_SNAKE_CASE       ,		sep_token=_SCREAMING_SNAKE_CASE       ,		pad_token=_SCREAMING_SNAKE_CASE       ,		cls_token=_SCREAMING_SNAKE_CASE       ,		mask_token=_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE       ,		)

			__lowerCAmelCase			:						Dict									=      do_lower_case
			__lowerCAmelCase			:						List[Any]									=      remove_space
			__lowerCAmelCase			:						List[Any]									=      keep_accents
			__lowerCAmelCase			:						int									=      vocab_file
			__lowerCAmelCase			:						Dict									=      False if not self.vocab_file else True

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None ):
			__lowerCAmelCase			:						Dict									=      [self.sep_token_id]
			__lowerCAmelCase			:						Dict									=      [self.cls_token_id]
			if token_ids_a is None:
				return cls + token_ids_a + sep
			return cls + token_ids_a + sep + token_ids_a + sep

		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None ):
			__lowerCAmelCase			:						Optional[int]									=      [self.sep_token_id]
			__lowerCAmelCase			:						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 ) * [0] + len(token_ids_a + sep ) * [1]







		def 							__lowerCamelCase							(      self       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE = None ):
			if not self.can_save_slow_tokenizer:
				raise ValueError(
				    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
				    'tokenizer.' )

			if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
				logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
				return
			__lowerCAmelCase			:						str									=      os.path.join(
			    _SCREAMING_SNAKE_CASE       ,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

			if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ):
				copyfile(self.vocab_file       ,		_SCREAMING_SNAKE_CASE )

			return (out_vocab_file,)
"""simple docstring"""






from PIL import Image



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							(2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))

				def contrast(lowerCamelCase       ) -> int:
								return int(1_2_8 + factor * (c - 1_2_8)       )

				return img.point(lowerCamelCase       )


if __name__ == "__main__":
						# Load image
						with Image.open('image_data/lena.jpg') as img:
												# Change contrast to 170
												lowerCAmelCase__  :  Any						     =	change_contrast(img, 170)
												cont_img.save('image_data/lena_high_contrast.png', format='png')



