"""simple docstring"""






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				if isinstance(lowerCamelCase      ,    lowerCamelCase ):
								UpperCAmelCase__    =							np.full((len(lowerCamelCase ), sequence_length, 2)      ,    lowerCamelCase )
				else:
								UpperCAmelCase__    =							np.full((len(lowerCamelCase ), sequence_length)      ,    lowerCamelCase )

				for i, tensor in enumerate(lowerCamelCase ):
								if padding_side == "right":
												if isinstance(lowerCamelCase      ,    lowerCamelCase ):
																UpperCAmelCase__    =							tensor[:sequence_length]
												else:
																UpperCAmelCase__    =							tensor[:sequence_length]
								else:
												if isinstance(lowerCamelCase      ,    lowerCamelCase ):
																UpperCAmelCase__    =							tensor[:sequence_length]
												else:
																UpperCAmelCase__    =							tensor[:sequence_length]

				return out_tensor.tolist()



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							ord(lowerCamelCase )
				if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
								return True
				UpperCAmelCase__    =							unicodedata.category(lowerCamelCase )
				if cat.startswith('P' ):
								return True
				return False







@dataclass
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				42
					snake_case__       					=				True
					snake_case__       					=				None
					snake_case__       					=				None
					snake_case__       					=				-1_00
					snake_case__       					=				"pt"







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			Optional[Any]    ):
									import torch

									UpperCAmelCase__    =							'label' if 'label' in features[0].keys() else 'labels'
									UpperCAmelCase__    =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
									UpperCAmelCase__    =							self.tokenizer.pad(
									    lowerCamelCase__					,padding=self.padding					,max_length=self.max_length					,pad_to_multiple_of=self.pad_to_multiple_of					,return_tensors='pt' if labels is None else None					,)

									if labels is None:
													return batch

									UpperCAmelCase__    =							torch.tensor(batch['entity_ids']    ).shape[1]
									UpperCAmelCase__    =							self.tokenizer.padding_side
									if padding_side == "right":
													UpperCAmelCase__    =							[
													    list(lowerCamelCase__    ) + [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase__    )) for label in labels
													]
									else:
													UpperCAmelCase__    =							[
													    [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase__    )) + list(lowerCamelCase__    ) for label in labels
													]

									UpperCAmelCase__    =							[feature['ner_tags'] for feature in features]
									UpperCAmelCase__    =							padding_tensor(lowerCamelCase__					,-1					,lowerCamelCase__					,lowerCamelCase__    )
									UpperCAmelCase__    =							[feature['original_entity_spans'] for feature in features]
									UpperCAmelCase__    =							padding_tensor(lowerCamelCase__					,(-1, -1)					,lowerCamelCase__					,lowerCamelCase__    )
									UpperCAmelCase__    =							{k: torch.tensor(lowerCamelCase__					,dtype=torch.intaa    ) for k, v in batch.items()}

									return batch




"""simple docstring"""






from __future__ import annotations

from math import ceil, floor, sqrt



def 						a_		(      lowerCamelCase = 2_0_0_0_0_0_0 ):
				UpperCAmelCase__    =							[0]
				UpperCAmelCase__    =							42

				for idx in range(1      ,    ceil(sqrt(target * 2 ) * 1.1 ) ):
								triangle_numbers.append(triangle_numbers[-1] + idx )

				# we want this to be as close as possible to target
				UpperCAmelCase__    =							0
				# the area corresponding to the grid that gives the product closest to target
				UpperCAmelCase__    =							0
				# an estimate of b, using the quadratic formula
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the largest integer less than b_estimate
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_floor
				UpperCAmelCase__    =							42
				# the triangle number corresponding to b_ceil
				UpperCAmelCase__    =							42

				for idx_a, triangle_a in enumerate(triangle_numbers[1:]      ,    1 ):
								UpperCAmelCase__    =							(-1 + sqrt(1 + 8 * target / triangle_a )) / 2
								UpperCAmelCase__    =							floor(lowerCamelCase )
								UpperCAmelCase__    =							ceil(lowerCamelCase )
								UpperCAmelCase__    =							triangle_numbers[b_floor]
								UpperCAmelCase__    =							triangle_numbers[b_ceil]

								if abs(target - triangle_b_first_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_first_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_floor

								if abs(target - triangle_b_second_guess * triangle_a ) < abs(
								    target - best_product ):
												UpperCAmelCase__    =							triangle_b_second_guess * triangle_a
												UpperCAmelCase__    =							idx_a * b_ceil

				return area


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



