
from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import (
		    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
		    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
		    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
		    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
		)


__SCREAMING_SNAKE_CASE     :				List[Any]             =       logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE     :				int             =       Dict[str, Any]
__SCREAMING_SNAKE_CASE     :				List[str]             =       List[Prediction]


@add_end_docstrings(UpperCAmelCase_      )
class 						__magic_name__				(  UpperCAmelCase_      ):





		def __init__(							self      :		str  ,					*lowerCamelCase__      :		Union[str, Any]  ,					**lowerCamelCase__      :		Tuple			):
							super().__init__(*__UpperCamelCase  ,					**__UpperCamelCase			)

							if self.framework == "tf":
												raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''			)

							requires_backends(self  ,					'''vision'''			)
							self.check_model_type(
							    dict(
							        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()			)			)





		def  _A							(							self      :		Any  ,					**lowerCamelCase__      :		str			):
							lowerCAmelCase						:   Optional[int]							=       {}
							lowerCAmelCase						:   Optional[int]							=       {}
							if "subtask" in kwargs:
												lowerCAmelCase						:   Dict							=       kwargs['''subtask''']
												lowerCAmelCase						:   Dict							=       kwargs['''subtask''']
							if "threshold" in kwargs:
												lowerCAmelCase						:   Union[str, Any]							=       kwargs['''threshold''']
							if "mask_threshold" in kwargs:
												lowerCAmelCase						:   Union[str, Any]							=       kwargs['''mask_threshold''']
							if "overlap_mask_area_threshold" in kwargs:
												lowerCAmelCase						:   Dict							=       kwargs['''overlap_mask_area_threshold''']

							return preprocess_kwargs, {}, postprocess_kwargs





		def __call__(							self      :		List[str]  ,					lowerCamelCase__      :		Any  ,					**lowerCamelCase__      :		Union[str, Any]			):
							return super().__call__(__UpperCamelCase  ,					**__UpperCamelCase			)





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Optional[int]  ,					lowerCamelCase__      :		Union[str, Any]=None			):
							lowerCAmelCase						:   Any							=       load_image(__UpperCamelCase			)
							lowerCAmelCase						:   Any							=       [(image.height, image.width)]
							if self.model.config.__class__.__name__ == "OneFormerConfig":
												if subtask is None:
																	lowerCAmelCase						:   List[str]							=       {}
												else:
																	lowerCAmelCase						:   Tuple							=       {'''task_inputs''': [subtask]}
												lowerCAmelCase						:   List[str]							=       self.image_processor(images=[image]  ,					return_tensors='''pt'''  ,					**__UpperCamelCase			)
												lowerCAmelCase						:   List[str]							=       self.tokenizer(
												    inputs['''task_inputs''']  ,					padding='''max_length'''  ,					max_length=self.model.config.task_seq_len  ,					return_tensors=self.framework  ,					)['''input_ids''']
							else:
												lowerCAmelCase						:   Tuple							=       self.image_processor(images=[image]  ,					return_tensors='''pt'''			)
							lowerCAmelCase						:   Any							=       target_size
							return inputs





		def  _A							(							self      :		Optional[Any]  ,					lowerCamelCase__      :		Optional[Any]			):
							lowerCAmelCase						:   int							=       model_inputs.pop('''target_size'''			)
							lowerCAmelCase						:   List[str]							=       self.model(**__UpperCamelCase			)
							lowerCAmelCase						:   Any							=       target_size
							return model_outputs





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		str=None  ,					lowerCamelCase__      :		str=0.9  ,					lowerCamelCase__      :		Optional[Any]=0.5  ,					lowerCamelCase__      :		Any=0.5			):
							lowerCAmelCase						:   Any							=       None
							if subtask in {"panoptic", None} and hasattr(self.image_processor  ,					'''post_process_panoptic_segmentation'''			):
												lowerCAmelCase						:   int							=       self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor  ,					'''post_process_instance_segmentation'''			):
												lowerCAmelCase						:   int							=       self.image_processor.post_process_instance_segmentation

							if fn is not None:
												lowerCAmelCase						:   List[Any]							=       fn(
												    __UpperCamelCase  ,					threshold=__UpperCamelCase  ,					mask_threshold=__UpperCamelCase  ,					overlap_mask_area_threshold=__UpperCamelCase  ,					target_sizes=model_outputs['''target_size''']  ,					)[0]

												lowerCAmelCase						:   str							=       []
												lowerCAmelCase						:   Any							=       outputs['''segmentation''']

												for segment in outputs["segments_info"]:
																	lowerCAmelCase						:   Dict							=       (segmentation == segment['''id''']) * 2_5_5
																	lowerCAmelCase						:   Optional[int]							=       Image.fromarray(mask.numpy().astype(np.uinta			)  ,					mode='''L'''			)
																	lowerCAmelCase						:   Tuple							=       self.model.config.idalabel[segment['''label_id''']]
																	lowerCAmelCase						:   int							=       segment['''score''']
																	annotation.append({'''score''': score, '''label''': label, '''mask''': mask}			)

							elif subtask in {"semantic", None} and hasattr(self.image_processor  ,					'''post_process_semantic_segmentation'''			):
												lowerCAmelCase						:   Any							=       self.image_processor.post_process_semantic_segmentation(
												    __UpperCamelCase  ,					target_sizes=model_outputs['''target_size''']			)[0]

												lowerCAmelCase						:   Optional[Any]							=       []
												lowerCAmelCase						:   Optional[int]							=       outputs.numpy()
												lowerCAmelCase						:   Dict							=       np.unique(__UpperCamelCase			)

												for label in labels:
																	lowerCAmelCase						:   Optional[Any]							=       (segmentation == label) * 2_5_5
																	lowerCAmelCase						:   Tuple							=       Image.fromarray(mask.astype(np.uinta			)  ,					mode='''L'''			)
																	lowerCAmelCase						:   Union[str, Any]							=       self.model.config.idalabel[label]
																	annotation.append({'''score''': None, '''label''': label, '''mask''': mask}			)
							else:
												raise ValueError(f'''Subtask {subtask} is not supported for model {type(self.model			)}'''			)
							return annotation


from math import floor, sqrt
def       UpperCAmelCase__							(    __magic_name__  :       int					):


					'''simple docstring'''

					lowerCAmelCase						:   int							=       0.0
					lowerCAmelCase						:   int							=       1.0
					lowerCAmelCase						:   Any							=       int(sqrt(__magic_name__					)					)
					lowerCAmelCase						:   Optional[int]							=       root
					lowerCAmelCase						:   Dict							=       0
					while integer_part != 2 * root:
										lowerCAmelCase						:   str							=       denominator * integer_part - numerator
										lowerCAmelCase						:   Dict							=       (n - numerator**2) / denominator
										lowerCAmelCase						:   str							=       int((root + numerator) / denominator					)
										period += 1
					return period
def       UpperCAmelCase__							(    __magic_name__  :       int = 1_00_00					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       0
					for i in range(2							,					n + 1					):
										lowerCAmelCase						:   Any							=       sqrt(__magic_name__					)
										if sr - floor(__magic_name__					) != 0 and continuous_fraction_period(__magic_name__					) % 2 == 1:
															count_odd_periods += 1
					return count_odd_periods


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