





"""simple docstring"""




import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
		import torch


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					List[str]      					="""char"""
							lowerCamelCase   :					Optional[Any]      					="""bpe"""
							lowerCamelCase   :					int      					="""wp"""
a						: Optional[int]     	=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Any      					=["""image_processor""", """char_tokenizer"""]
							lowerCamelCase   :					int      					="""ViTImageProcessor"""
							lowerCamelCase   :					Any      					="""MgpstrTokenizer"""






							def __init__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					**lowerCAmelCase__      )	-> Optional[Any]:
								a      :		Dict	=				None
								if "feature_extractor" in kwargs:
									warnings.warn(
									    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
									    " instead."      ,					lowerCAmelCase__      ,					)
									a      :		Optional[int]	=				kwargs.pop("feature_extractor"      )

								a      :		Optional[Any]	=				image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
									raise ValueError("You need to specify an `image_processor`."      )
								if tokenizer is None:
									raise ValueError("You need to specify a `tokenizer`."      )

								a      :		Tuple	=				tokenizer
								a      :		int	=				AutoTokenizer.from_pretrained("gpt2"      )
								a      :		Tuple	=				AutoTokenizer.from_pretrained("bert-base-uncased"      )

								super().__init__(lowerCAmelCase__      ,					lowerCAmelCase__      )






							def __call__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					**lowerCAmelCase__      )	-> Optional[int]:
								if images is None and text is None:
									raise ValueError("You need to specify either an `images` or `text` input to process."      )

								if images is not None:
									a      :		Dict	=				self.image_processor(lowerCAmelCase__      ,					return_tensors=lowerCAmelCase__      ,					**lowerCAmelCase__      )
								if text is not None:
									a      :		Tuple	=				self.char_tokenizer(lowerCAmelCase__      ,					return_tensors=lowerCAmelCase__      ,					**lowerCAmelCase__      )

								if text is None:
									return inputs
								elif images is None:
									return encodings
								else:
									a      :		Tuple	=				encodings["input_ids"]
									return inputs






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Dict:
								a,       a,       a      :		str	=				sequences
								a      :		int	=				char_preds.size(0      )

								a,       a      :		Union[str, Any]	=				self._decode_helper(lowerCAmelCase__      ,					"char"      )
								a,       a      :		Dict	=				self._decode_helper(lowerCAmelCase__      ,					"bpe"      )
								a,       a      :		Any	=				self._decode_helper(lowerCAmelCase__      ,					"wp"      )

								a      :		Dict	=				[]
								a      :		int	=				[]
								for i in range(lowerCAmelCase__      ):
									a      :		Union[str, Any]	=				[char_scores[i], bpe_scores[i], wp_scores[i]]
									a      :		Optional[int]	=				[char_strs[i], bpe_strs[i], wp_strs[i]]
									a      :		str	=				scores.index(max(lowerCAmelCase__      )      )
									final_strs.append(strs[max_score_index]      )
									final_scores.append(scores[max_score_index]      )

								a      :		List[str]	=				{}
								a      :		int	=				final_strs
								a      :		List[str]	=				final_scores
								a      :		List[Any]	=				char_strs
								a      :		Tuple	=				bpe_strs
								a      :		Any	=				wp_strs
								return out






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> int:
								if format == DecodeType.CHARACTER:
									a      :		Optional[int]	=				self.char_decode
									a      :		Optional[int]	=				1
									a      :		Optional[int]	=				"[s]"
								elif format == DecodeType.BPE:
									a      :		Any	=				self.bpe_decode
									a      :		Union[str, Any]	=				2
									a      :		str	=				"#"
								elif format == DecodeType.WORDPIECE:
									a      :		Dict	=				self.wp_decode
									a      :		str	=				102
									a      :		Optional[Any]	=				"[SEP]"
								else:
									raise ValueError(f"""Format {format} is not supported."""      )

								a,       a      :		List[Any]	=				[], []
								a      :		Optional[int]	=				pred_logits.size(0      )
								a      :		List[Any]	=				pred_logits.size(1      )
								a,       a      :		List[Any]	=				pred_logits.topk(1      ,					dim=-1      ,					largest=lowerCAmelCase__      ,					sorted=lowerCAmelCase__      )
								a      :		List[str]	=				preds_index.view(-1      ,					lowerCAmelCase__      )[:, 1:]
								a      :		Dict	=				decoder(lowerCAmelCase__      )
								a,       a      :		Dict	=				torch.nn.functional.softmax(lowerCAmelCase__      ,					dim=2      ).max(dim=2      )
								a      :		List[Any]	=				preds_max_prob[:, 1:]

								for index in range(lowerCAmelCase__      ):
									a      :		Optional[int]	=				preds_str[index].find(lowerCAmelCase__      )
									a      :		List[Any]	=				preds_str[index][:pred_eos]
									a      :		Optional[int]	=				preds_index[index].cpu().tolist()
									a      :		Any	=				pred_index.index(lowerCAmelCase__      ) if eos_token in pred_index else -1
									a      :		Dict	=				preds_max_prob[index][: pred_eos_index + 1]
									a      :		List[str]	=				pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
									dec_strs.append(lowerCAmelCase__      )
									conf_scores.append(lowerCAmelCase__      )

								return dec_strs, conf_scores






							def     __a   (   self      ,					lowerCAmelCase__      )	-> str:
								a      :		List[Any]	=				[seq.replace(" "      ,					""      ) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__      )]
								return decode_strs






							def     __a   (   self      ,					lowerCAmelCase__      )	-> str:
								return self.bpe_tokenizer.batch_decode(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> Dict:
								a      :		Optional[int]	=				[seq.replace(" "      ,					""      ) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__      )]
								return decode_strs









"""simple docstring"""




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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int					) ->str:






	'''simple docstring'''






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

	a      :		Tuple	=				precision
	a      :		str	=				ceil(precision / 14					)
	a      :		List[Any]	=				42_6880 * Decimal(1_0005					).sqrt()
	a      :		Union[str, Any]	=				1
	a      :		Dict	=				1359_1409
	a      :		Optional[int]	=				Decimal(_lowercase					)
	for k in range(1    ,       _lowercase					):
		a      :		int	=				factorial(6 * k					) // (factorial(3 * k					) * factorial(_lowercase					) ** 3)
		linear_term += 5_4514_0134
		exponential_term *= -26_2537_4126_4076_8000
		partial_sum += Decimal(multinomial_term * linear_term					) / exponential_term
	return str(constant_term / partial_sum					)[:-1]


if __name__ == "__main__":
		a						: Optional[Any]     	=			50
		print(F'''The first {n} digits of pi is: {pi(n)}''')


