


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


lowerCAmelCase	=							logging.get_logger(__name__)

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

lowerCAmelCase	=							{
    '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',
    },
}

lowerCAmelCase	=							{
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			VOCAB_FILES_NAMES
							_lowercase     :      Optional[int]  				=			PRETRAINED_VOCAB_FILES_MAP
							_lowercase     :      Any  				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowercase     :      List[str]  				=			['''input_ids''', '''attention_mask''']
							_lowercase     :      Optional[int]  				=			GPTaTokenizer

							def __init__(							self:     Any   ,						UpperCamelCase_:     Tuple=None   ,						UpperCamelCase_:     Optional[int]=None   ,						UpperCamelCase_:     Optional[int]=None   ,						UpperCamelCase_:     Dict="<|endoftext|>"   ,						UpperCamelCase_:     Tuple="<|endoftext|>"   ,						UpperCamelCase_:     List[str]="<|endoftext|>"   ,						UpperCamelCase_:     List[Any]=False   ,						**UpperCamelCase_:     str   ,						)      ->       Optional[Any]:





									"""simple docstring"""







									super().__init__(
									    UpperCamelCase_   ,						UpperCamelCase_   ,						tokenizer_file=UpperCamelCase_   ,						unk_token=UpperCamelCase_   ,						bos_token=UpperCamelCase_   ,						eos_token=UpperCamelCase_   ,						add_prefix_space=UpperCamelCase_   ,						**UpperCamelCase_   ,						)

									lowercase__						=				kwargs.pop('''add_bos_token'''   ,						UpperCamelCase_							)

									lowercase__						=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
									if pre_tok_state.get('''add_prefix_space'''   ,						UpperCamelCase_							) != add_prefix_space:
											lowercase__						=				getattr(UpperCamelCase_   ,						pre_tok_state.pop('''type'''							)							)
											lowercase__						=				add_prefix_space
											lowercase__						=				pre_tok_class(**UpperCamelCase_							)

									lowercase__						=				add_prefix_space

							def 				lowerCamelCase_			(							self:     Dict   ,						*UpperCamelCase_:     int   ,						**UpperCamelCase_:     Optional[Any]							)      ->       BatchEncoding:





									"""simple docstring"""







									lowercase__						=				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:     Tuple   ,						*UpperCamelCase_:     int   ,						**UpperCamelCase_:     int							)      ->       BatchEncoding:





									"""simple docstring"""







									lowercase__						=				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							)      ->       Tuple[str]:





									"""simple docstring"""







									lowercase__						=				self._tokenizer.model.save(UpperCamelCase_   ,						name=UpperCamelCase_							)
									return tuple(UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     "Conversation"							)      ->       List[int]:





									"""simple docstring"""







									lowercase__						=				[]
									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:
											lowercase__						=				input_ids[-self.model_max_length :]
									return input_ids






def       _a			(				SCREAMING_SNAKE_CASE = 10_00					):


		"""simple docstring"""




		return sum(e for e in range(3						,    SCREAMING_SNAKE_CASE					) if e % 3 == 0 or e % 5 == 0					)


if __name__ == "__main__":
			print(f"""{solution() = }""")


