

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

lowerCAmelCase__	   = logging.get_logger(__name__)

@add_end_docstrings(snake_case			)
class 					a__						(					snake_case			):
		"""simple docstring"""







		def __init__(    self      ,      *lowercase      ,      **lowercase       )  ->						List[Any]:
						'''simple docstring'''






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


		def __call__(    self      ,      lowercase      ,      **lowercase       )  ->						List[str]:
						'''simple docstring'''






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


		def      UpperCamelCase		(    self      ,      **lowercase       )  ->						List[str]:
						'''simple docstring'''






						return {}, {}, {}


		def      UpperCamelCase		(    self      ,      lowercase       )  ->						int:
						'''simple docstring'''






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


		def      UpperCamelCase		(    self      ,      lowercase       )  ->						Any:
						'''simple docstring'''






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


		def      UpperCamelCase		(    self      ,      lowercase       )  ->						Dict:
						'''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=lowercase       )
						A__        =			prediction.squeeze().cpu().numpy()
						A__        =			(output * 255 / np.max(lowercase       )).astype("uint8"       )
						A__        =			Image.fromarray(lowercase       )
						A__        =			{}
						A__        =			predicted_depth
						A__        =			depth
						return output_dict




def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      int:




				'''simple docstring'''





				if divisor % 5 == 0 or divisor % 2 == 0:
								return 0
				A__        =			1
				A__        =			1
				while repunit:
								A__        =			(1_0 * repunit + 1) % divisor
								repunit_index += 1
				return repunit_index



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 1_0_0_0_0_0_0      )			->      int:




				'''simple docstring'''





				A__        =			limit - 1
				if divisor % 2 == 0:
								divisor += 1
				while least_divisible_repunit(SCREAMING_SNAKE_CASE_      ) <= limit:
								divisor += 2
				return divisor


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

