


'''simple docstring'''






from typing import 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():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

__A	:	Union[str, Any]     =     logging.get_logger(__name__)
@add_end_docstrings(lowercase__							)
class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:Union[str, Any]  ,*_UpperCamelCase		:Dict  ,**_UpperCamelCase		:Optional[int]			):
									super().__init__(*_UpperCamelCase  ,**_UpperCamelCase			)
									requires_backends(self  ,"""vision"""			)
									self.check_model_type(_UpperCamelCase			)





							def __call__(  self		:List[Any]  ,_UpperCamelCase		:Dict  ,**_UpperCamelCase		:str			):
									return super().__call__(_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:List[Any]  ,**_UpperCamelCase		:Union[str, Any]			):
									return {}, {}, {}





							def 		a__   (  self		:Any  ,_UpperCamelCase		:Dict			):
									snake_case_		:							Tuple			       =						load_image(_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						image.size
									snake_case_		:							Tuple			       =						self.image_processor(images=_UpperCamelCase  ,return_tensors=self.framework			)
									return model_inputs





							def 		a__   (  self		:int  ,_UpperCamelCase		:str			):
									snake_case_		:							Dict			       =						self.model(**_UpperCamelCase			)
									return model_outputs





							def 		a__   (  self		:Any  ,_UpperCamelCase		:List[str]			):
									snake_case_		:							int			       =						model_outputs.predicted_depth
									snake_case_		:							Optional[Any]			       =						torch.nn.functional.interpolate(
									    predicted_depth.unsqueeze(1			)  ,size=self.image_size[::-1]  ,mode="""bicubic"""  ,align_corners=_UpperCamelCase			)
									snake_case_		:							int			       =						prediction.squeeze().cpu().numpy()
									snake_case_		:							Tuple			       =						(output * 2_5_5 / np.max(_UpperCamelCase			)).astype("""uint8"""			)
									snake_case_		:							Union[str, Any]			       =						Image.fromarray(_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						{}
									snake_case_		:							int			       =						predicted_depth
									snake_case_		:							Union[str, Any]			       =						depth
									return output_dict



'''simple docstring'''






import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
		snake_case_		:							str			       =						0
		while arr[min(lowerCamelCase_				,      lowerCamelCase_ ) - 1] < x:
				snake_case_		:							Any			       =						step
				step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
				if prev >= n:
						return -1

		while arr[prev] < x:
				snake_case_		:							Optional[Any]			       =						prev + 1
				if prev == min(lowerCamelCase_				,      lowerCamelCase_ ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
							__A	:	Any     =     input('Enter numbers separated by a comma:\n').strip()
							__A	:	Union[str, Any]     =     [int(item) for item in user_input.split(',')]
							__A	:	Optional[int]     =     int(input('Enter the number to be searched:\n'))
							__A	:	int     =     jump_search(arr, x)
							if res == -1:
														print('Number not found!')
							else:
														print(F'Number {x} is at index {res}')