
"""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		:       Optional[int]     						=       logging.get_logger(__name__)







@add_end_docstrings(_lowercase       )
class     lowercase(_lowercase       ):



     def __init__(							self     ,				*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



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



     def __call__(							self     ,				__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				**__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         return {}, {}, {}



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			load_image(__SCREAMING_SNAKE_CASE   )
         a__		=			image.size
         a__		=			self.image_processor(images=__SCREAMING_SNAKE_CASE     ,				return_tensors=self.framework   )
         return model_inputs



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         a__		=			self.model(**__SCREAMING_SNAKE_CASE   )
         return model_outputs




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Any:







         """simple docstring"""



         a__		=			model_outputs.predicted_depth
         a__		=			torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1   )     ,				size=self.image_size[::-1]     ,				mode='bicubic'     ,				align_corners=__SCREAMING_SNAKE_CASE   )
         a__		=			prediction.squeeze().cpu().numpy()
         a__		=			(output * 2_5_5 / np.max(__SCREAMING_SNAKE_CASE   )).astype('uint8'   )
         a__		=			Image.fromarray(__SCREAMING_SNAKE_CASE   )
         a__		=			{}
         a__		=			predicted_depth
         a__		=			depth
         return output_dict




"""simple docstring"""




from math import ceil



def     __magic_name__		(					UpperCamelCase     :		int = 1001     )     ->						int:
    a__		=			1

    for i in range(1							,		int(ceil(n / 2.0     )     )     ):
        a__		=			2 * i + 1
        a__		=			2 * i
        a__		=			total + 4 * odd**2 - 6 * even

    return total


if __name__ == "__main__":
    import sys

    if len(sys.argv) == 1:
        print(solution())
    else:
        try:
            a		:       Any     						=       int(sys.argv[1])
            print(solution(n))
        except ValueError:
            print('Invalid entry - please enter a number')


