



'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


a   :			Dict					  =    logging.get_logger(__name__)

a   :			str					  =    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a   :			int					  =    {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

a   :			Optional[int]					  =    {
    """roberta-base""": 5_1_2,
    """roberta-large""": 5_1_2,
    """roberta-large-mnli""": 5_1_2,
    """distilroberta-base""": 5_1_2,
    """roberta-base-openai-detector""": 5_1_2,
    """roberta-large-openai-detector""": 5_1_2,
}

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





	def __init__(  self		,		A=None		,		A=None		,		A=None		,		A="replace"		,		A="<s>"		,		A="</s>"		,		A="</s>"		,		A="<s>"		,		A="<unk>"		,		A="<pad>"		,		A="<mask>"		,		A=False		,		A=True		,		**A		,		)			->			List[Any]:
								super().__init__(
								    A		,		A		,		tokenizer_file=A		,		errors=A		,		bos_token=A		,		eos_token=A		,		sep_token=A		,		cls_token=A		,		unk_token=A		,		pad_token=A		,		mask_token=A		,		add_prefix_space=A		,		trim_offsets=A		,		**A		,		)

								UpperCAmelCase     :		Optional[Any]							     =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
								if pre_tok_state.get("""add_prefix_space"""		,		A    ) != add_prefix_space:
															UpperCAmelCase     :		Union[str, Any]							     =		getattr(A		,		pre_tok_state.pop("""type"""    )    )
															UpperCAmelCase     :		Dict							     =		add_prefix_space
															UpperCAmelCase     :		Optional[Any]							     =		pre_tok_class(**A    )

								UpperCAmelCase     :		List[Any]							     =		add_prefix_space

								UpperCAmelCase     :		List[str]							     =		"""post_processor"""
								UpperCAmelCase     :		List[Any]							     =		getattr(self.backend_tokenizer		,		A		,		A    )
								if tokenizer_component_instance:
															UpperCAmelCase     :		Dict							     =		json.loads(tokenizer_component_instance.__getstate__()    )

															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
															if "sep" in state:
																						UpperCAmelCase     :		Any							     =		tuple(state["""sep"""]    )
															if "cls" in state:
																						UpperCAmelCase     :		Any							     =		tuple(state["""cls"""]    )

															UpperCAmelCase     :		List[str]							     =		False

															if state.get("""add_prefix_space"""		,		A    ) != add_prefix_space:
																						UpperCAmelCase     :		Any							     =		add_prefix_space
																						UpperCAmelCase     :		int							     =		True

															if state.get("""trim_offsets"""		,		A    ) != trim_offsets:
																						UpperCAmelCase     :		Optional[int]							     =		trim_offsets
																						UpperCAmelCase     :		Tuple							     =		True

															if changes_to_apply:
																						UpperCAmelCase     :		Dict							     =		getattr(A		,		state.pop("""type"""    )    )
																						UpperCAmelCase     :		Union[str, Any]							     =		component_class(**A    )
																						setattr(self.backend_tokenizer		,		A		,		A    )





	@property
	def  _lowercase(  self    )			->			str:
								if self._mask_token is None:
															if self.verbose:
																						logger.error("""Using mask_token, but it is not set yet."""    )
															return None
								return str(self._mask_token    )





	@mask_token.setter
	def  _lowercase(  self		,		A    )			->			Union[str, Any]:
								UpperCAmelCase     :		Optional[Any]							     =		AddedToken(A		,		lstrip=A		,		rstrip=A    ) if isinstance(A		,		A    ) else value
								UpperCAmelCase     :		Optional[Any]							     =		value





	def  _lowercase(  self		,		*A		,		**A    )			->			BatchEncoding:
								UpperCAmelCase     :		int							     =		kwargs.get("""is_split_into_words"""		,		A    )
								assert self.add_prefix_space or not is_split_into_words, (
								    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*A		,		**A    )





	def  _lowercase(  self		,		*A		,		**A    )			->			BatchEncoding:
								UpperCAmelCase     :		Any							     =		kwargs.get("""is_split_into_words"""		,		A    )

								assert self.add_prefix_space or not is_split_into_words, (
								    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*A		,		**A    )





	def  _lowercase(  self		,		A		,		A = None    )			->			Tuple[str]:
								UpperCAmelCase     :		int							     =		self._tokenizer.model.save(A		,		name=A    )
								return tuple(A    )





	def  _lowercase(  self		,		A		,		A=None    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
															return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






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






'''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""")

