


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 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Union[str, Any]:
				if isinstance(lowerCAmelCase__ ,   lowerCAmelCase__ ):
								lowerCAmelCase_ :      Tuple    				=				np.full((len(lowerCAmelCase__ ), sequence_length, 2) ,   lowerCAmelCase__ )
				else:
								lowerCAmelCase_ :      List[Any]    				=				np.full((len(lowerCAmelCase__ ), sequence_length) ,   lowerCAmelCase__ )

				for i, tensor in enumerate(lowerCAmelCase__ ):
								if padding_side == "right":
												if isinstance(lowerCAmelCase__ ,   lowerCAmelCase__ ):
																lowerCAmelCase_ :      Dict    				=				tensor[:sequence_length]
												else:
																lowerCAmelCase_ :      Tuple    				=				tensor[:sequence_length]
								else:
												if isinstance(lowerCAmelCase__ ,   lowerCAmelCase__ ):
																lowerCAmelCase_ :      str    				=				tensor[:sequence_length]
												else:
																lowerCAmelCase_ :      Tuple    				=				tensor[:sequence_length]

				return out_tensor.tolist()



def 			lowerCAmelCase (		lowerCAmelCase_ )-> Union[str, Any]:
				lowerCAmelCase_ :      Any    				=				ord(lowerCAmelCase__ )
				if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
								return True
				lowerCAmelCase_ :      Dict    				=				unicodedata.category(lowerCAmelCase__ )
				if cat.startswith('''P''' ):
								return True
				return False







@dataclass
class 				snake_case__(							__SCREAMING_SNAKE_CASE							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  PreTrainedTokenizerBase
						SCREAMING_SNAKE_CASE__      :  Union[bool, str, PaddingStrategy]					=							True
						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							None
						SCREAMING_SNAKE_CASE__      :  Optional[int]					=							None
						SCREAMING_SNAKE_CASE__      :  int					=							-100
						SCREAMING_SNAKE_CASE__      :  str					=							"pt"




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										import torch

										lowerCAmelCase_ :      int    				=				'''label''' if '''label''' in features[0].keys() else '''labels'''
										lowerCAmelCase_ :      List[str]    				=				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
										lowerCAmelCase_ :      Union[str, Any]    				=				self.tokenizer.pad(
										    _a   ,						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

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

										lowerCAmelCase_ :      List[str]    				=				[feature['''ner_tags'''] for feature in features]
										lowerCAmelCase_ :      Union[str, Any]    				=				padding_tensor(_a   ,						-1   ,						_a   ,						_a							)
										lowerCAmelCase_ :      str    				=				[feature['''original_entity_spans'''] for feature in features]
										lowerCAmelCase_ :      Optional[int]    				=				padding_tensor(_a   ,						(-1, -1)   ,						_a   ,						_a							)
										lowerCAmelCase_ :      Dict    				=				{k: torch.tensor(_a   ,						dtype=torch.intaa							) for k, v in batch.items()}

										return batch



from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase			:  Tuple     =10



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				for i in range(lowerCAmelCase_ ,   lowerCAmelCase_ ):
								if array[i] == target:
												return i
				return -1



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Any    				=				0
				lowerCAmelCase_ :      int    				=				len(lowerCAmelCase_ )
				while left <= right:
								if right - left < precision:
												return lin_search(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )

								lowerCAmelCase_ :      List[Any]    				=				(left + right) // 3 + 1
								lowerCAmelCase_ :      List[str]    				=				2 * (left + right) // 3 + 1

								if array[one_third] == target:
												return one_third
								elif array[two_third] == target:
												return two_third

								elif target < array[one_third]:
												lowerCAmelCase_ :      Dict    				=				one_third - 1
								elif array[two_third] < target:
												lowerCAmelCase_ :      List[Any]    				=				two_third + 1

								else:
												lowerCAmelCase_ :      Union[str, Any]    				=				one_third + 1
												lowerCAmelCase_ :      Tuple    				=				two_third - 1
				else:
								return -1



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				if left < right:
								if right - left < precision:
												return lin_search(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								lowerCAmelCase_ :      Union[str, Any]    				=				(left + right) // 3 + 1
								lowerCAmelCase_ :      Optional[int]    				=				2 * (left + right) // 3 + 1

								if array[one_third] == target:
												return one_third
								elif array[two_third] == target:
												return two_third

								elif target < array[one_third]:
												return rec_ternary_search(lowerCAmelCase_ ,   one_third - 1 ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								elif array[two_third] < target:
												return rec_ternary_search(two_third + 1 ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								else:
												return rec_ternary_search(one_third + 1 ,   two_third - 1 ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
				else:
								return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_UpperCAmelCase			:  Tuple     =input("""Enter numbers separated by comma:\n""").strip()
						_UpperCAmelCase			:  Union[str, Any]     =[int(item.strip()) for item in user_input.split(""",""")]
						assert collection == sorted(collection), f"List must be ordered.\n{collection}."
						_UpperCAmelCase			:  int     =int(input("""Enter the number to be found in the list:\n""").strip())
						_UpperCAmelCase			:  Optional[Any]     =ite_ternary_search(collection, target)
						_UpperCAmelCase			:  List[str]     =rec_ternary_search(0, len(collection) - 1, collection, target)
						if resulta != -1:
												print(f"""Iterative search: {target} found at positions: {resulta}""")
												print(f"""Recursive search: {target} found at positions: {resulta}""")
						else:
												print("""Not found""")