





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[Any]			=				logging.get_logger(__name__)


@add_end_docstrings(SCREAMING_SNAKE_CASE   )
class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			def __init__(      self       :   List[Any]							,     *__lowerCAmelCase       :   Dict							,     **__lowerCAmelCase       :   List[Any]       )     ->     Union[str, Any]:







					"""simple docstring"""






					super().__init__(*__lowerCAmelCase							,     **__lowerCAmelCase       )
					requires_backends(self							,     """vision"""       )
					self.check_model_type(__lowerCAmelCase       )






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







					"""simple docstring"""






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






			def        a_	(      self       :   List[Any]							,     **__lowerCAmelCase       :   Any       )     ->     Optional[Any]:







					"""simple docstring"""






					return {}, {}, {}






			def        a_	(      self       :   Tuple							,     __lowerCAmelCase       :   Optional[Any]       )     ->     Any:







					"""simple docstring"""






					A__  				=       load_image(__lowerCAmelCase       )
					A__  				=       image.size
					A__  				=       self.image_processor(images=__lowerCAmelCase							,     return_tensors=self.framework       )
					return model_inputs






			def        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   Dict       )     ->     Tuple:







					"""simple docstring"""






					A__  				=       self.model(**__lowerCAmelCase       )
					return model_outputs







			def        a_	(      self       :   Optional[int]							,     __lowerCAmelCase       :   Dict       )     ->     str:







					"""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=__lowerCAmelCase       )
					A__  				=       prediction.squeeze().cpu().numpy()
					A__  				=       (output * 2_55 / np.max(__lowerCAmelCase       )).astype("""uint8"""       )
					A__  				=       Image.fromarray(__lowerCAmelCase       )
					A__  				=       {}
					A__  				=       predicted_depth
					A__  				=       depth
					return output_dict










import math







def        __lowerCamelCase (							__a       :int   )		->    bool:






		"""simple docstring"""

		A__  				=       math.loga(math.sqrt(4 * positive_integer + 1   ) / 2 + 1 / 2   )

		return exponent == int(__a   )







def        __lowerCamelCase (							__a       :float = 1 / 1_2_3_4_5   )		->    int:






		"""simple docstring"""

		A__  				=       0
		A__  				=       0

		A__  				=       3
		while True:
				A__  				=       (integer**2 - 1) / 4
				# if candidate is an integer, then there is a partition for k
				if partition_candidate == int(__a   ):
						A__  				=       int(__a   )
						total_partitions += 1
						if check_partition_perfect(__a   ):
								perfect_partitions += 1
				if perfect_partitions > 0:
						if perfect_partitions / total_partitions < max_proportion:
								return int(__a   )
				integer += 1


if __name__ == "__main__":
					print(F'''{solution() = }''')



