

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__						=							logging.get_logger(__name__)

@add_end_docstrings(SCREAMING_SNAKE_CASE			)
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	def __init__(   self: str  , *__UpperCamelCase: Union[str, Any]  , **__UpperCamelCase: Optional[int]      ):

						'''simple docstring'''





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







	def __call__(   self: List[Any]  , __UpperCamelCase: Union[str, List[str], "Image.Image", List["Image.Image"]]  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , **__UpperCamelCase: Any      ):

						'''simple docstring'''





						return {}, {}, {}







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: int      ):

						'''simple docstring'''





						__magic_name__							=    load_image(__UpperCamelCase      )
						__magic_name__							=    image.size
						__magic_name__							=    self.image_processor(images=__UpperCamelCase  , return_tensors=self.framework      )
						return model_inputs







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]  , __UpperCamelCase: Dict      ):

						'''simple docstring'''





						__magic_name__							=    self.model(**__UpperCamelCase      )
						return model_outputs







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: Dict      ):

						'''simple docstring'''





						__magic_name__							=    model_outputs.predicted_depth
						__magic_name__							=    torch.nn.functional.interpolate(
						    predicted_depth.unsqueeze(1      )  , size=self.image_size[::-1]  , mode='bicubic'  , align_corners=__UpperCamelCase      )
						__magic_name__							=    prediction.squeeze().cpu().numpy()
						__magic_name__							=    (output * 2_55 / np.max(__UpperCamelCase      )).astype('uint8'      )
						__magic_name__							=    Image.fromarray(__UpperCamelCase      )
						__magic_name__							=    {}
						__magic_name__							=    predicted_depth
						__magic_name__							=    depth
						return output_dict







from __future__ import annotations




def 					_lowercase     (				a_				:					list[int]				)				->	int:




					'''simple docstring'''







					if not nums:
										return 0
					__magic_name__							=    nums[0]
					__magic_name__							=    0
					for num in nums[1:]:
										__magic_name__,	__magic_name__							=    (
										    max_excluding + num,
										    max(a_					,a_				),
										)
					return max(a_					,a_				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()




