






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__			:    Union[str, Any]         =				logging.get_logger(__name__)

A__			:    Optional[int]         =				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__			:    Dict         =				{
    '''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__			:    Optional[Any]         =				{
    '''gpt2''': 10_24,
    '''gpt2-medium''': 10_24,
    '''gpt2-large''': 10_24,
    '''gpt2-xl''': 10_24,
    '''distilgpt2''': 10_24,
}
class    _UpperCAmelCase      ( SCREAMING_SNAKE_CASE__			):

				"""simple docstring"""




				lowercase__       							= VOCAB_FILES_NAMES
				lowercase__       							= PRETRAINED_VOCAB_FILES_MAP
				lowercase__       							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__       							= ["""input_ids""", """attention_mask"""]
				lowercase__       							= GPTaTokenizer




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

								'''simple docstring'''
								super().__init__(
								    _lowercase,						_lowercase,						tokenizer_file=_lowercase,						unk_token=_lowercase,						bos_token=_lowercase,						eos_token=_lowercase,						add_prefix_space=_lowercase,						**_lowercase,						)

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

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

								lowercase__     				=    add_prefix_space




				def        lowercase__     (  self		:					Optional[Any],						*lowerCamelCase		:					Tuple,						**lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    kwargs.get('''is_split_into_words''',						_lowercase						)
								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(*_lowercase,						**_lowercase						)




				def        lowercase__     (  self		:					Dict,						*lowerCamelCase		:					List[Any],						**lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    kwargs.get('''is_split_into_words''',						_lowercase						)

								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(*_lowercase,						**_lowercase						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					Any,						lowerCamelCase		:					Union[str, Any] = None						):

								'''simple docstring'''
								lowercase__     				=    self._tokenizer.model.save(_lowercase,						name=_lowercase						)
								return tuple(_lowercase						)




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    []
								for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(_lowercase,						add_special_tokens=_lowercase						) + [self.eos_token_id]						)

								if len(_lowercase						) > self.model_max_length:
												lowercase__     				=    input_ids[-self.model_max_length :]
								return input_ids












from collections import defaultdict
from math import gcd






def 						a    (		lowerCamelCase_ = 150_0000       ):






				'''simple docstring'''
				lowercase__     				=    defaultdict(lowerCamelCase_       )
				lowercase__     				=    2
				while 2 * euclid_m * (euclid_m + 1) <= limit:
								for euclid_n in range((euclid_m % 2) + 1	,     lowerCamelCase_	,     2       ):
												if gcd(lowerCamelCase_	,     lowerCamelCase_       ) > 1:
																continue
												lowercase__     				=    2 * euclid_m * (euclid_m + euclid_n)
												for perimeter in range(lowerCamelCase_	,     limit + 1	,     lowerCamelCase_       ):
																frequencies[perimeter] += 1
								euclid_m += 1
				return sum(1 for frequency in frequencies.values() if frequency == 1       )


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




