



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

lowercase_	       =			logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE				)
class 						__a (     SCREAMING_SNAKE_CASE				):



	def __init__(						self							:    Optional[int]				,	*snake_case_							:    Optional[int]				,	**snake_case_							:    int)->							Optional[int]:
						super().__init__(*snake_case_				,	**snake_case_)
						requires_backends(self				,	"""vision""")
						self.check_model_type(snake_case_)



	def __call__(						self							:    str				,	snake_case_							:    Union[str, List[str], "Image.Image", List["Image.Image"]]				,	**snake_case_							:    Union[str, Any])->							Tuple:
						return super().__call__(snake_case_				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    int				,	**snake_case_							:    Union[str, Any])->							Optional[int]:
						return {}, {}, {}



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    Optional[int])->							Dict:
						__lowerCAmelCase	   =load_image(snake_case_)
						__lowerCAmelCase	   =image.size
						__lowerCAmelCase	   =self.image_processor(images=snake_case_				,	return_tensors=self.framework)
						return model_inputs



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    int)->							List[str]:
						__lowerCAmelCase	   =self.model(**snake_case_)
						return model_outputs



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    Any)->							Optional[Any]:
						__lowerCAmelCase	   =model_outputs.predicted_depth
						__lowerCAmelCase	   =torch.nn.functional.interpolate(
						    predicted_depth.unsqueeze(1)				,	size=self.image_size[::-1]				,	mode="""bicubic"""				,	align_corners=snake_case_)
						__lowerCAmelCase	   =prediction.squeeze().cpu().numpy()
						__lowerCAmelCase	   =(output * 2_55 / np.max(snake_case_)).astype("""uint8""")
						__lowerCAmelCase	   =Image.fromarray(snake_case_)
						__lowerCAmelCase	   ={}
						__lowerCAmelCase	   =predicted_depth
						__lowerCAmelCase	   =depth
						return output_dict









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list				,   __lowerCamelCase	:    int				,   __lowerCamelCase	:    int = 0				,   __lowerCamelCase	:    int = 0	)  ->						int:
					__lowerCAmelCase	   =right or len(__lowerCamelCase	) - 1
					if left > right:
										return -1
					elif list_data[left] == key:
										return left
					elif list_data[right] == key:
										return right
					else:
										return search(__lowerCamelCase				,   __lowerCamelCase				,   left + 1				,   right - 1	)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




