




'''simple docstring'''

def 					UpperCAmelCase__      (							_lowerCAmelCase						: str,			_lowerCAmelCase						: Optional[int] = 1000)						->		Optional[int]:



						"""simple docstring"""

						if (not isinstance(__A,			__A)) or (digit_position <= 0):
												raise ValueError("Digit position must be a positive integer")
						elif (not isinstance(__A,			__A)) or (precision < 0):
												raise ValueError("Precision must be a nonnegative integer")

						# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
						# accurate
						snake_case   :					Optional[Any]							=						(
						    4 * _subsum(__A,			1,			__A)
						    - 2 * _subsum(__A,			4,			__A)
						    - _subsum(__A,			5,			__A)
						    - _subsum(__A,			6,			__A)
						)

						# return the first hex digit of the fractional part of the result
						return hex(int((sum_result % 1) * 16))[2:]


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Tuple,			_lowerCAmelCase						: str,			_lowerCAmelCase						: str)						->		Union[str, Any]:



						"""simple docstring"""

						snake_case   :					int							=						0.0
						for sum_index in range(digit_pos_to_extract + precision):
												snake_case   :					List[Any]							=						8 * sum_index + denominator_addend
												if sum_index < digit_pos_to_extract:
																		# if the exponential term is an integer and we mod it by the denominator
																		# before dividing, only the integer part of the sum will change;
																		# the fractional part will not
																		snake_case   :					int							=						pow(
																		    16,			digit_pos_to_extract - 1 - sum_index,			__A)
												else:
																		snake_case   :					Union[str, Any]							=						pow(16,			digit_pos_to_extract - 1 - sum_index)
												total += exponential_term / denominator
						return total


if __name__ == "__main__":
						import doctest

						doctest.testmod()






'''simple docstring'''

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


A_        =   logging.get_logger(__name__)

A_        =   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

A_        =   {
    "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"
        ),
    },
}

A_        =   {
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

A_        =   {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Optional[Any]             =					VOCAB_FILES_NAMES
			__magic_name__   :							int             =					PRETRAINED_VOCAB_FILES_MAP
			__magic_name__   :							Dict             =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__magic_name__   :							List[str]             =					["""input_ids""", """attention_mask""", """token_type_ids"""]
			__magic_name__   :							Optional[int]             =					DebertaTokenizer



			def __init__(       self							, snake_case_=None							, snake_case_=None							, snake_case_=None							, snake_case_="replace"							, snake_case_="[CLS]"							, snake_case_="[SEP]"							, snake_case_="[SEP]"							, snake_case_="[CLS]"							, snake_case_="[UNK]"							, snake_case_="[PAD]"							, snake_case_="[MASK]"							, snake_case_=False							, **snake_case_							, ):
									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_							, )
									snake_case   :					List[str]							=						kwargs.pop("add_bos_token"							, snake_case_				)

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

									snake_case   :					Any							=						add_prefix_space



			@property
			def      lowerCamelCase   (       self				):
									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      lowerCamelCase   (       self							, snake_case_				):
									snake_case   :					Optional[Any]							=						AddedToken(snake_case_							, lstrip=snake_case_							, rstrip=snake_case_				) if isinstance(snake_case_							, snake_case_				) else value
									snake_case   :					Optional[int]							=						value



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_ = None				):
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case   :					Optional[int]							=						[self.cls_token_id]
									snake_case   :					Optional[int]							=						[self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_ = None				):
									snake_case   :					Dict							=						[self.sep_token_id]
									snake_case   :					Dict							=						[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      lowerCamelCase   (       self							, *snake_case_							, **snake_case_				):
									snake_case   :					str							=						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      lowerCamelCase   (       self							, *snake_case_							, **snake_case_				):
									snake_case   :					List[Any]							=						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      lowerCamelCase   (       self							, snake_case_							, snake_case_ = None				):
									snake_case   :					List[str]							=						self._tokenizer.model.save(snake_case_							, name=snake_case_				)
									return tuple(snake_case_				)






			def      lowerCamelCase   (       self							, snake_case_				):
									snake_case   :					int							=						[]
									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:
															snake_case   :					Union[str, Any]							=						input_ids[-self.model_max_length :]
									return input_ids





