





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
				import torch

class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		['''image_processor''', '''tokenizer''']
	__UpperCamelCase        =		'''OneFormerImageProcessor'''
	__UpperCamelCase        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')




	def __init__(	self       :    str				,			__lowerCamelCase       :    List[str]=None				,			__lowerCamelCase       :    int=None				,			__lowerCamelCase       :    int = 77				,			__lowerCamelCase       :    int = 77				,			**__lowerCamelCase       :    Union[str, Any]      )					->   Union[str, Any]:

			'''simple docstring'''




			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`.'      )

			__lowercase					=    max_seq_length
			__lowercase					=    task_seq_length

			super().__init__(__lowerCamelCase				,			__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    List[Any]=None				,			__lowerCamelCase       :    str=77      )					->   Union[str, Any]:

			'''simple docstring'''




			if text_list is None:
					raise ValueError('tokens cannot be None.'      )

			__lowercase					=    self.tokenizer(__lowerCamelCase				,			padding='max_length'				,			max_length=__lowerCamelCase				,			truncation=__lowerCamelCase      )

			__lowercase	,			__lowercase					=    tokens['attention_mask'], tokens['input_ids']

			__lowercase					=    []
			for attn_mask, input_id in zip(__lowerCamelCase				,			__lowerCamelCase      ):
					__lowercase					=    torch.tensor(__lowerCamelCase      ) * torch.tensor(__lowerCamelCase      )
					token_inputs.append(token.unsqueeze(0      )      )

			__lowercase					=    torch.cat(__lowerCamelCase				,			dim=0      )
			return token_inputs




	def __call__(	self       :    int				,			__lowerCamelCase       :    Optional[Any]=None				,			__lowerCamelCase       :    Dict=None				,			__lowerCamelCase       :    Dict=None				,			**__lowerCamelCase       :    List[str]      )					->   List[Any]:

			'''simple docstring'''




			if task_inputs is None:
					raise ValueError('You have to specify the task_input. Found None.'      )
			elif images is None:
					raise ValueError('You have to specify the image. Found None.'      )

			if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs      ):
					raise ValueError('task_inputs must be semantic, instance, or panoptic.'      )

			__lowercase					=    self.image_processor(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			**__lowerCamelCase      )

			if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
					__lowercase					=    [task_inputs]

			if isinstance(__lowerCamelCase				,			__lowerCamelCase      ) and all(isinstance(__lowerCamelCase				,			__lowerCamelCase      ) for task_input in task_inputs      ):
					__lowercase					=    []
					for task in task_inputs:
							__lowercase					=    F"the task is {task}"
							task_token_inputs.append(__lowerCamelCase      )
					__lowercase					=    self._preprocess_text(__lowerCamelCase				,			max_length=self.task_seq_length      )
			else:
					raise TypeError('Task Inputs should be a string or a list of strings.'      )

			if hasattr(__lowerCamelCase				,			'text_inputs'      ):
					__lowercase					=    encoded_inputs.text_inputs

					__lowercase					=    []
					for texts in texts_list:
							__lowercase					=    self._preprocess_text(__lowerCamelCase				,			max_length=self.max_seq_length      )
							text_inputs.append(text_input_list.unsqueeze(0      )      )

					__lowercase					=    torch.cat(__lowerCamelCase				,			dim=0      )

			return encoded_inputs




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    Union[str, Any]=None				,			__lowerCamelCase       :    int=None				,			__lowerCamelCase       :    List[Any]=None				,			**__lowerCamelCase       :    Optional[int]      )					->   Union[str, Any]:

			'''simple docstring'''




			if task_inputs is None:
					raise ValueError('You have to specify the task_input. Found None.'      )
			elif images is None:
					raise ValueError('You have to specify the image. Found None.'      )

			if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs      ):
					raise ValueError('task_inputs must be semantic, instance, or panoptic.'      )

			__lowercase					=    self.image_processor.encode_inputs(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			**__lowerCamelCase      )

			if isinstance(__lowerCamelCase				,			__lowerCamelCase      ):
					__lowercase					=    [task_inputs]

			if isinstance(__lowerCamelCase				,			__lowerCamelCase      ) and all(isinstance(__lowerCamelCase				,			__lowerCamelCase      ) for task_input in task_inputs      ):
					__lowercase					=    []
					for task in task_inputs:
							__lowercase					=    F"the task is {task}"
							task_token_inputs.append(__lowerCamelCase      )
					__lowercase					=    self._preprocess_text(__lowerCamelCase				,			max_length=self.task_seq_length      )
			else:
					raise TypeError('Task Inputs should be a string or a list of strings.'      )

			if hasattr(__lowerCamelCase				,			'text_inputs'      ):
					__lowercase					=    encoded_inputs.text_inputs

					__lowercase					=    []
					for texts in texts_list:
							__lowercase					=    self._preprocess_text(__lowerCamelCase				,			max_length=self.max_seq_length      )
							text_inputs.append(text_input_list.unsqueeze(0      )      )

					__lowercase					=    torch.cat(__lowerCamelCase				,			dim=0      )

			return encoded_inputs




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			*__lowerCamelCase       :    Any				,			**__lowerCamelCase       :    List[Any]      )					->   Any:

			'''simple docstring'''




			return self.image_processor.post_process_semantic_segmentation(*__lowerCamelCase				,			**__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[Any]				,			*__lowerCamelCase       :    Optional[int]				,			**__lowerCamelCase       :    str      )					->   Tuple:

			'''simple docstring'''




			return self.image_processor.post_process_instance_segmentation(*__lowerCamelCase				,			**__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    Optional[Any]				,			*__lowerCamelCase       :    Tuple				,			**__lowerCamelCase       :    Union[str, Any]      )					->   int:

			'''simple docstring'''




			return self.image_processor.post_process_panoptic_segmentation(*__lowerCamelCase				,			**__lowerCamelCase      )











from math import floor, sqrt



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						int:
		__lowercase					=    0.0
		__lowercase					=    1.0
		__lowercase					=    int(sqrt(snake_case       )       )
		__lowercase					=    root
		__lowercase					=    0
		while integer_part != 2 * root:
				__lowercase					=    denominator * integer_part - numerator
				__lowercase					=    (n - numerator**2) / denominator
				__lowercase					=    int((root + numerator) / denominator       )
				period += 1
		return period



def    SCREAMING_SNAKE_CASE							(			snake_case = 10_000       )		->						int:
		__lowercase					=    0
		for i in range(2	,      n + 1       ):
				__lowercase					=    sqrt(snake_case       )
				if sr - floor(snake_case       ) != 0 and continuous_fraction_period(snake_case       ) % 2 == 1:
						count_odd_periods += 1
		return count_odd_periods


if __name__ == "__main__":
		print(F"""{solution(int(input().strip()))}""")




