"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


SCREAMING_SNAKE_CASE									=						logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE									=						{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

SCREAMING_SNAKE_CASE									=						{
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}



class  _lowerCamelCase			(_snake_case     ):
				_snake_case     =  VOCAB_FILES_NAMES
				_snake_case     =  PRETRAINED_VOCAB_FILES_MAP
				_snake_case     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case     =  ["input_ids", "attention_mask"]
				_snake_case     =  RobertaTokenizer







				def __init__( self    :       Dict			,					lowerCamelCase_    :       List[Any]=None			,					lowerCamelCase_    :       List[Any]=None			,					lowerCamelCase_    :       Optional[Any]=None			,					lowerCamelCase_    :       List[str]="replace"			,					lowerCamelCase_    :       List[Any]="<s>"			,					lowerCamelCase_    :       str="</s>"			,					lowerCamelCase_    :       Optional[int]="</s>"			,					lowerCamelCase_    :       Dict="<s>"			,					lowerCamelCase_    :       Tuple="<unk>"			,					lowerCamelCase_    :       List[Any]="<pad>"			,					lowerCamelCase_    :       Any="<mask>"			,					lowerCamelCase_    :       Optional[Any]=False			,					lowerCamelCase_    :       Union[str, Any]=True			,					**lowerCamelCase_    :       Tuple			,					):

					"""simple docstring"""






					super().__init__(
					    lowerCAmelCase__			,					lowerCAmelCase__			,					tokenizer_file=lowerCAmelCase__			,					errors=lowerCAmelCase__			,					bos_token=lowerCAmelCase__			,					eos_token=lowerCAmelCase__			,					sep_token=lowerCAmelCase__			,					cls_token=lowerCAmelCase__			,					unk_token=lowerCAmelCase__			,					pad_token=lowerCAmelCase__			,					mask_token=lowerCAmelCase__			,					add_prefix_space=lowerCAmelCase__			,					trim_offsets=lowerCAmelCase__			,					**lowerCAmelCase__			,					)

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

					_lowercase :    Tuple							=							add_prefix_space

					_lowercase :    Optional[Any]							=							'post_processor'
					_lowercase :    int							=							getattr(self.backend_tokenizer			,					lowerCAmelCase__			,					lowerCAmelCase__						)
					if tokenizer_component_instance:
						_lowercase :    List[Any]							=							json.loads(tokenizer_component_instance.__getstate__()						)

						# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
						if "sep" in state:
							_lowercase :    Any							=							tuple(state['sep']						)
						if "cls" in state:
							_lowercase :    Union[str, Any]							=							tuple(state['cls']						)

						_lowercase :    str							=							False

						if state.get('add_prefix_space'			,					lowerCAmelCase__						) != add_prefix_space:
							_lowercase :    List[str]							=							add_prefix_space
							_lowercase :    Optional[int]							=							True

						if state.get('trim_offsets'			,					lowerCAmelCase__						) != trim_offsets:
							_lowercase :    Any							=							trim_offsets
							_lowercase :    Optional[Any]							=							True

						if changes_to_apply:
							_lowercase :    Dict							=							getattr(lowerCAmelCase__			,					state.pop('type'						)						)
							_lowercase :    Optional[int]							=							component_class(**lowerCAmelCase__						)
							setattr(self.backend_tokenizer			,					lowerCAmelCase__			,					lowerCAmelCase__						)







				@property
				def 							__UpperCAmelCase					( self    :       str						):

					"""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 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Union[str, Any]						):

					"""simple docstring"""






					_lowercase :    str							=							AddedToken(lowerCAmelCase__			,					lstrip=lowerCAmelCase__			,					rstrip=lowerCAmelCase__						) if isinstance(lowerCAmelCase__			,					lowerCAmelCase__						) else value
					_lowercase :    Any							=							value







				def 							__UpperCAmelCase					( self    :       Tuple			,					*lowerCamelCase_    :       List[Any]			,					**lowerCamelCase_    :       List[Any]						):

					"""simple docstring"""






					_lowercase :    List[str]							=							kwargs.get('is_split_into_words'			,					lowerCAmelCase__						)
					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(*lowerCAmelCase__			,					**lowerCAmelCase__						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					*lowerCamelCase_    :       Dict			,					**lowerCamelCase_    :       str						):

					"""simple docstring"""






					_lowercase :    Optional[Any]							=							kwargs.get('is_split_into_words'			,					lowerCAmelCase__						)

					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(*lowerCAmelCase__			,					**lowerCAmelCase__						)







				def 							__UpperCAmelCase					( self    :       Optional[int]			,					lowerCamelCase_    :       str			,					lowerCamelCase_    :       Optional[str] = None						):

					"""simple docstring"""






					_lowercase :    Tuple							=							self._tokenizer.model.save(lowerCAmelCase__			,					name=lowerCAmelCase__						)
					return tuple(lowerCAmelCase__						)







				def 							__UpperCAmelCase					( self    :       Optional[Any]			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Union[str, Any]=None						):

					"""simple docstring"""






					_lowercase :    str							=							[self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
						return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







				def 							__UpperCAmelCase					( self    :       List[str]			,					lowerCamelCase_    :       List[int]			,					lowerCamelCase_    :       Optional[List[int]] = None						):

					"""simple docstring"""






					_lowercase :    Any							=							[self.sep_token_id]
					_lowercase :    Union[str, Any]							=							[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 + sep + token_ids_a + sep						) * [0]




"""simple docstring"""







from decimal import Decimal, getcontext
from math import ceil, factorial


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if not isinstance(__UpperCAmelCase     ,__UpperCAmelCase			):
		raise TypeError('Undefined for non-integers'			)
	elif precision < 1:
		raise ValueError('Undefined for non-natural numbers'			)

	_lowercase :    Optional[Any]							=							precision
	_lowercase :    Dict							=							ceil(precision / 14			)
	_lowercase :    int							=							426_880 * Decimal(10_005			).sqrt()
	_lowercase :    Optional[Any]							=							1
	_lowercase :    Union[str, Any]							=							13_591_409
	_lowercase :    Optional[int]							=							Decimal(__UpperCAmelCase			)
	for k in range(1     ,__UpperCAmelCase			):
		_lowercase :    List[str]							=							factorial(6 * k			) // (factorial(3 * k			) * factorial(__UpperCAmelCase			) ** 3)
		linear_term += 545_140_134
		exponential_term *= -262_537_412_640_768_000
		partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
	return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						50
						print(f"""The first {n} digits of pi is: {pi(n)}""")



