






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


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

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

SCREAMING_SNAKE_CASE__      :			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''',
    },
}

SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		{
    '''gpt2''': 10_24,
    '''gpt2-medium''': 10_24,
    '''gpt2-large''': 10_24,
    '''gpt2-xl''': 10_24,
    '''distilgpt2''': 10_24,
}
class 						lowerCamelCase_				(      SCREAMING_SNAKE_CASE__ ):
    a__					       =  VOCAB_FILES_NAMES
    a__					       =  PRETRAINED_VOCAB_FILES_MAP
    a__					       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__					       =  ['''input_ids''', '''attention_mask''']
    a__					       =  GPTaTokenizer







    def __init__(						self       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase="<|endoftext|>"       ,					__lowerCAmelCase="<|endoftext|>"       ,					__lowerCAmelCase="<|endoftext|>"       ,					__lowerCAmelCase=False       ,					**__lowerCAmelCase       ,					):
        """simple docstring"""
        super().__init__(
            _lowercase       ,					_lowercase       ,					tokenizer_file=_lowercase       ,					unk_token=_lowercase       ,					bos_token=_lowercase       ,					eos_token=_lowercase       ,					add_prefix_space=_lowercase       ,					**_lowercase       ,					)

        __magic_name__		:Optional[Any]        =      kwargs.pop('''add_bos_token'''       ,					_lowercase     )

        __magic_name__		:Optional[int]        =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
        if pre_tok_state.get('''add_prefix_space'''       ,					_lowercase     ) != add_prefix_space:
            __magic_name__		:List[Any]        =      getattr(_lowercase       ,					pre_tok_state.pop('''type'''     )     )
            __magic_name__		:Tuple        =      add_prefix_space
            __magic_name__		:Optional[int]        =      pre_tok_class(**_lowercase     )

        __magic_name__		:Optional[int]        =      add_prefix_space







    def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:Dict        =      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 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:List[str]        =      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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
        """simple docstring"""
        __magic_name__		:int        =      self._tokenizer.model.save(_lowercase       ,					name=_lowercase     )
        return tuple(_lowercase     )







    def 			A	(						self       ,					__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:Tuple        =      []
        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:
            __magic_name__		:Optional[Any]        =      input_ids[-self.model_max_length :]
        return input_ids














SCREAMING_SNAKE_CASE__      :			str							=		"""Alexander Joslin"""

import operator as op

from .stack import Stack

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

				__magic_name__		:Stack[int]        =      Stack()
				__magic_name__		:Stack[str]        =      Stack()

				for i in equation:
								if i.isdigit():
												# RULE 1
												operand_stack.push(int(snake_case ) )
								elif i in operators:
												# RULE 2
												operator_stack.push(snake_case )
								elif i == ")":
												# RULE 4
												__magic_name__		:Optional[int]        =      operator_stack.peek()
												operator_stack.pop()
												__magic_name__		:List[str]        =      operand_stack.peek()
												operand_stack.pop()
												__magic_name__		:Optional[Any]        =      operand_stack.peek()
												operand_stack.pop()

												__magic_name__		:Optional[int]        =      operators[opr](snake_case,  snake_case )
												operand_stack.push(snake_case )

    # RULE 5
				return operand_stack.peek()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Any							=		"""(5 + ((4 * 2) * (2 + 3)))"""
					# answer = 45
					print(f"{equation} = {dijkstras_two_stack_algorithm(equation)}")






