




"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					int = 10      ,       snake_case			:					int = 1000      ,       snake_case			:					bool = True    ):
					assert (
					    isinstance(snake_case      ,       snake_case    )
					    and isinstance(snake_case      ,       snake_case    )
					    and isinstance(snake_case      ,       snake_case    )
					), "Invalid type of value(s) specified to function!"

					if min_val > max_val:
										raise ValueError('''Invalid value for min_val or max_val (min_value < max_value)'''    )
					return min_val if option else max_val





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return int((number_a + number_a) / 2    )





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int      ,       snake_case			:					int    ):
					assert (
					    isinstance(snake_case      ,       snake_case    ) and isinstance(snake_case      ,       snake_case    ) and isinstance(snake_case      ,       snake_case    )
					), 'argument values must be type of "int"'

					if lower > higher:
										raise ValueError('''argument value for lower and higher must be(lower > higher)'''    )

					if not lower < to_guess < higher:
										raise ValueError(
										    '''guess value must be within the range of lower and higher value'''    )

					def answer(snake_case			:					int    ) -> str:
										if number > to_guess:
															return "high"
										elif number < to_guess:
															return "low"
										else:
															return "same"

					print('''started...'''    )

					_lowerCAmelCase:Optional[Any]									=  lower
					_lowerCAmelCase:Union[str, Any]									=  higher

					_lowerCAmelCase:List[str]									=  []

					while True:
										_lowerCAmelCase:Union[str, Any]									=  get_avg(snake_case      ,       snake_case    )
										last_numbers.append(snake_case    )

										if answer(snake_case    ) == "low":
															_lowerCAmelCase:Union[str, Any]									=  number
										elif answer(snake_case    ) == "high":
															_lowerCAmelCase:int									=  number
										else:
															break

					print(F'guess the number : {last_numbers[-1]}'    )
					print(F'details : {last_numbers!s}'    )





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:str									=  int(input('''Enter lower value : '''    ).strip()    )
					_lowerCAmelCase:int									=  int(input('''Enter high value : '''    ).strip()    )
					_lowerCAmelCase:Union[str, Any]									=  int(input('''Enter value to guess : '''    ).strip()    )
					guess_the_number(snake_case      ,       snake_case      ,       snake_case    )


if __name__ == "__main__":
				main()












"""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

UpperCamelCase__						=			logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_  )
class   a__			(       UpperCamelCase_  ):





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




											"""simple docstring"""







											super().__init__(*a__     ,**a__)
											requires_backends(self     ,'''vision''')
											self.check_model_type(a__)





						def __call__(							self      :	str     ,a__      :	Union[str, List[str], "Image.Image", List["Image.Image"]]     ,**a__      :	List[str])						->       Optional[int]:




											"""simple docstring"""







											return super().__call__(a__     ,**a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,**a__      :	List[Any])						->       Any:




											"""simple docstring"""







											return {}, {}, {}





						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	Optional[int])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  load_image(a__)
											_lowerCAmelCase:int									=  image.size
											_lowerCAmelCase:int									=  self.image_processor(images=a__     ,return_tensors=self.framework)
											return model_inputs





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	List[str])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.model(**a__)
											return model_outputs







						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Dict)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  model_outputs.predicted_depth
											_lowerCAmelCase:Union[str, Any]									=  torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1)     ,size=self.image_size[::-1]     ,mode='''bicubic'''     ,align_corners=a__)
											_lowerCAmelCase:List[str]									=  prediction.squeeze().cpu().numpy()
											_lowerCAmelCase:Any									=  (output * 255 / np.max(a__)).astype('''uint8''')
											_lowerCAmelCase:Dict									=  Image.fromarray(a__)
											_lowerCAmelCase:Tuple									=  {}
											_lowerCAmelCase:Optional[int]									=  predicted_depth
											_lowerCAmelCase:str									=  depth
											return output_dict






