



'''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

SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)


@add_end_docstrings(__lowerCamelCase   )
class 				snake_case (__lowerCamelCase   ):



							def __init__(				self     ,*UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Union[str, Any]:
											super().__init__(*SCREAMING_SNAKE_CASE_     ,**SCREAMING_SNAKE_CASE_      )
											requires_backends(self     ,"vision"      )
											self.check_model_type(SCREAMING_SNAKE_CASE_      )



							def __call__(				self     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							Any:
											return super().__call__(SCREAMING_SNAKE_CASE_     ,**SCREAMING_SNAKE_CASE_      )



							def 							_a      (				self     ,**UpperCAmelCase_      )  ->							Any:
											return {}, {}, {}



							def 							_a      (				self     ,UpperCAmelCase_      )  ->							Optional[Any]:
											lowercase__		       =						load_image(SCREAMING_SNAKE_CASE_      )
											lowercase__		       =						image.size
											lowercase__		       =						self.image_processor(images=SCREAMING_SNAKE_CASE_     ,return_tensors=self.framework      )
											return model_inputs



							def 							_a      (				self     ,UpperCAmelCase_      )  ->							Tuple:
											lowercase__		       =						self.model(**SCREAMING_SNAKE_CASE_      )
											return model_outputs







							def 							_a      (				self     ,UpperCAmelCase_      )  ->							Any:
											lowercase__		       =						model_outputs.predicted_depth
											lowercase__		       =						torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1      )     ,size=self.image_size[::-1]     ,mode="bicubic"     ,align_corners=SCREAMING_SNAKE_CASE_      )
											lowercase__		       =						prediction.squeeze().cpu().numpy()
											lowercase__		       =						(output * 255 / np.max(SCREAMING_SNAKE_CASE_      )).astype("uint8"      )
											lowercase__		       =						Image.fromarray(SCREAMING_SNAKE_CASE_      )
											lowercase__		       =						{}
											lowercase__		       =						predicted_depth
											lowercase__		       =						depth
											return output_dict









'''simple docstring'''





print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))




