



'''simple docstring'''




from __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCamelCase							:List[str]       = 1_0_0

lowerCamelCase							:Dict       = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCamelCase							:int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
						if prime not in primes:
												continue
						primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))



@lru_cache(maxsize=1_00   )
def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			A_     :						set[int]     =					set()
			A_     :						int
			A_     :						int

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime   ):
									ret.add(sub * prime   )

			return ret



def      a      (			lowerCamelCase__ = 50_00   ):
			'''simple docstring'''





			for number_to_partition in range(1						,				lowerCamelCase__   ):
						if len(partition(lowerCamelCase__   )   ) > number_unique_partitions:
									return number_to_partition
			return None


if __name__ == "__main__":
						print(F"{solution() = }")




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)