



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

snake_case    :						Tuple  =						logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Tuple			,		*_A      :     List[str]			,		**_A      :     Optional[int]):
											super().__init__(*_A			,		**_A)
											requires_backends(self			,		"vision")
											self.check_model_type(_A)







					def __call__(       self      :     Optional[Any]			,		_A      :     Union[str, List[str], "Image.Image", List["Image.Image"]]			,		**_A      :     Dict):
											return super().__call__(_A			,		**_A)







					def  _lowercase			(       self      :     str			,		**_A      :     Dict):
											return {}, {}, {}







					def  _lowercase			(       self      :     str			,		_A      :     List[Any]):
											A__	:							Any						    = load_image(_A)
											A__	:							Union[str, Any]						    = image.size
											A__	:							List[str]						    = self.image_processor(images=_A			,		return_tensors=self.framework)
											return model_inputs







					def  _lowercase			(       self      :     Any			,		_A      :     Dict):
											A__	:							int						    = self.model(**_A)
											return model_outputs







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     Dict):
											A__	:							int						    = model_outputs.predicted_depth
											A__	:							Tuple						    = torch.nn.functional.interpolate(
											    predicted_depth.unsqueeze(1)			,		size=self.image_size[::-1]			,		mode="bicubic"			,		align_corners=_A)
											A__	:							List[Any]						    = prediction.squeeze().cpu().numpy()
											A__	:							Any						    = (output * 255 / np.max(_A)).astype("uint8")
											A__	:							Dict						    = Image.fromarray(_A)
											A__	:							List[str]						    = {}
											A__	:							Tuple						    = predicted_depth
											A__	:							Any						    = depth
											return output_dict




def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase = 0							,     __lowercase = 0       )		->		int:







						"""simple docstring"""

						A__	:							Tuple						    = right or len(__lowercase       ) - 1
						if left > right:
												return -1
						elif list_data[left] == key:
												return left
						elif list_data[right] == key:
												return right
						else:
												return search(__lowercase							,     __lowercase							,     left + 1							,     right - 1       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()