






'''simple docstring'''







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


_A	:			Tuple         =logging.get_logger(__name__)

_A	:			Any         ={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_A	:			Optional[int]         ={
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

_A	:			Dict         ={
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}

class _lowercase	(				_lowercase		):
							a				      =       VOCAB_FILES_NAMES
							a				      =       PRETRAINED_VOCAB_FILES_MAP
							a				      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a				      =       ["""input_ids""", """attention_mask"""]
							a				      =       GPTaTokenizer




							def __init__(							self:				Any			,							UpperCamelCase__:				Union[str, Any]=None			,							UpperCamelCase__:				Tuple=None			,							UpperCamelCase__:				Optional[int]=None			,							UpperCamelCase__:				Optional[Any]="<|endoftext|>"			,							UpperCamelCase__:				Tuple="<|endoftext|>"			,							UpperCamelCase__:				int="<|endoftext|>"			,							UpperCamelCase__:				str=False			,							**UpperCamelCase__:				Any			,							):
														super().__init__(
														    UpperCamelCase__			,							UpperCamelCase__			,							tokenizer_file=UpperCamelCase__			,							unk_token=UpperCamelCase__			,							bos_token=UpperCamelCase__			,							eos_token=UpperCamelCase__			,							add_prefix_space=UpperCamelCase__			,							**UpperCamelCase__			,							)

														lowerCamelCase__					:	Union[str, Any]         =       kwargs.pop("""add_bos_token"""			,							UpperCamelCase__     )

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

														lowerCamelCase__					:	Union[str, Any]         =       add_prefix_space




							def 		lowerCamelCase_				(							self:				str			,							*UpperCamelCase__:				str			,							**UpperCamelCase__:				Tuple     ):
														lowerCamelCase__					:	List[str]         =       kwargs.get("""is_split_into_words"""			,							UpperCamelCase__     )
														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(*UpperCamelCase__			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict			,							*UpperCamelCase__:				List[str]			,							**UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Optional[int]         =       kwargs.get("""is_split_into_words"""			,							UpperCamelCase__     )

														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(*UpperCamelCase__			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Optional[str] = None     ):
														lowerCamelCase__					:	Tuple         =       self._tokenizer.model.save(UpperCamelCase__			,							name=UpperCamelCase__     )
														return tuple(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				"Conversation"     ):
														lowerCamelCase__					:	Tuple         =       []
														for is_user, text in conversation.iter_texts():
																					input_ids.extend(self.encode(UpperCamelCase__			,							add_special_tokens=UpperCamelCase__     ) + [self.eos_token_id]     )

														if len(UpperCamelCase__     ) > self.model_max_length:
																					lowerCamelCase__					:	Optional[Any]         =       input_ids[-self.model_max_length :]
														return input_ids






'''simple docstring'''


from PIL import Image







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	Image:






   """simple docstring"""

   def brightness(A__    ) -> float:
      return 128 + level + (c - 128)

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


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

