




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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE:    int											=   logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE:    Optional[int]											=   {
    '''vocab_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt''',
        '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt''',
        '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt''',
        '''microsoft/deberta-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'''
        ),
    },
}

SCREAMING_SNAKE_CASE:    int											=   {
    '''microsoft/deberta-base''': 5_1_2,
    '''microsoft/deberta-large''': 5_1_2,
    '''microsoft/deberta-xlarge''': 5_1_2,
    '''microsoft/deberta-base-mnli''': 5_1_2,
    '''microsoft/deberta-large-mnli''': 5_1_2,
    '''microsoft/deberta-xlarge-mnli''': 5_1_2,
}

SCREAMING_SNAKE_CASE:    int											=   {
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}



class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			=VOCAB_FILES_NAMES
						lowerCAmelCase__ 			=PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__ 			=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__ 			=["input_ids", "attention_mask", "token_type_ids"]
						lowerCAmelCase__ 			=DebertaTokenizer



						def __init__( self  :      List[Any]				,       snake_case__  :      str=None				,       snake_case__  :      Dict=None				,       snake_case__  :      Dict=None				,       snake_case__  :      Optional[int]="replace"				,       snake_case__  :      List[Any]="[CLS]"				,       snake_case__  :      Dict="[SEP]"				,       snake_case__  :      Any="[SEP]"				,       snake_case__  :      Dict="[CLS]"				,       snake_case__  :      List[str]="[UNK]"				,       snake_case__  :      str="[PAD]"				,       snake_case__  :      Optional[int]="[MASK]"				,       snake_case__  :      Optional[Any]=False				,       **snake_case__  :      int				,       ):

								"""simple docstring"""

								super().__init__(
								    snake_case__				,       snake_case__				,       tokenizer_file=snake_case__				,       errors=snake_case__				,       bos_token=snake_case__				,       eos_token=snake_case__				,       unk_token=snake_case__				,       sep_token=snake_case__				,       cls_token=snake_case__				,       pad_token=snake_case__				,       mask_token=snake_case__				,       add_prefix_space=snake_case__				,       **snake_case__				,       )
								SCREAMING_SNAKE_CASE_							=       kwargs.pop('add_bos_token'				,       snake_case__     )

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

								SCREAMING_SNAKE_CASE_							=       add_prefix_space



						@property
						def   __a				( self  :      int     ):

								"""simple docstring"""

								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   __a				( self  :      int				,       snake_case__  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       AddedToken(snake_case__				,       lstrip=snake_case__				,       rstrip=snake_case__     ) if isinstance(snake_case__				,       snake_case__     ) else value
								SCREAMING_SNAKE_CASE_							=       value



						def   __a				( self  :      Optional[Any]				,       snake_case__  :      List[int]				,       snake_case__  :      Optional[List[int]] = None     ):

								"""simple docstring"""

								if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								SCREAMING_SNAKE_CASE_							=       [self.cls_token_id]
								SCREAMING_SNAKE_CASE_							=       [self.sep_token_id]
								return cls + token_ids_a + sep + token_ids_a + sep



						def   __a				( self  :      Dict				,       snake_case__  :      List[int]				,       snake_case__  :      Optional[List[int]] = None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       [self.sep_token_id]
								SCREAMING_SNAKE_CASE_							=       [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     ) * [0] + len(token_ids_a + sep     ) * [1]



						def   __a				( self  :      Dict				,       *snake_case__  :      Union[str, Any]				,       **snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       kwargs.get('is_split_into_words'				,       snake_case__     )
								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(*snake_case__				,       **snake_case__     )



						def   __a				( self  :      Tuple				,       *snake_case__  :      int				,       **snake_case__  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       kwargs.get('is_split_into_words'				,       snake_case__     )

								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(*snake_case__				,       **snake_case__     )



						def   __a				( self  :      int				,       snake_case__  :      str				,       snake_case__  :      Optional[str] = None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self._tokenizer.model.save(snake_case__				,       name=snake_case__     )
								return tuple(snake_case__     )






						def   __a				( self  :      Any				,       snake_case__  :      "Conversation"     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       []
								for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(snake_case__				,       add_special_tokens=snake_case__     ) + [self.eos_token_id]     )

								if len(snake_case__     ) > self.model_max_length:
										SCREAMING_SNAKE_CASE_							=       input_ids[-self.model_max_length :]
								return input_ids





from __future__ import annotations

import sys


def 				_a       (				lowerCAmelCase   )->  int:
		if n < 0:
				raise ValueError('Negative arguments are not supported'   )
		return _fib(lowerCAmelCase   )[0]


def 				_a       (				lowerCAmelCase   )->  tuple[int, int]:
		if n == 0:  # (F(0), F(1))
				return (0, 1)

		# F(2n) = F(n)[2F(n+1) − F(n)]
		# F(2n+1) = F(n+1)^2+F(n)^2
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       _fib(n // 2   )
		SCREAMING_SNAKE_CASE_							=       a * (b * 2 - a)
		SCREAMING_SNAKE_CASE_							=       a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    Optional[int]											=   int(sys.argv[1])
						print(f"""fibonacci({n}) is {fibonacci(n)}""")