


'''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

lowerCamelCase							:   int  						=	logging.get_logger(__name__)
@add_end_docstrings(lowercase__     )
class   __lowerCAmelCase						(lowercase__     ):





      '''simple docstring'''






      def __init__(self    :    Dict   ,						*UpperCamelCase    :    Optional[int]   ,						**UpperCamelCase    :    List[str] ):

             '''simple docstring'''







             super().__init__(*__lowerCamelCase   ,						**__lowerCamelCase )
             requires_backends(self   ,						'''vision''' )
             self.check_model_type(__lowerCamelCase )



      def __call__(self    :    str   ,						UpperCamelCase    :    Union[str, List[str], "Image.Image", List["Image.Image"]]   ,						**UpperCamelCase    :    Tuple ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    List[str]   ,						**UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             return {}, {}, {}



      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= load_image(__lowerCamelCase )
             lowercase__      			= image.size
             lowercase__      			= self.image_processor(images=__lowerCamelCase   ,						return_tensors=self.framework )
             return model_inputs



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= self.model(**__lowerCamelCase )
             return model_outputs






      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    List[Any] ):

             '''simple docstring'''







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








class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			List[Any]					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  size
             lowerCamelCase__		:				List[str]    =  [0] * size
             lowerCamelCase__		:				str    =  [0] * size





       @staticmethod
       def 						lowerCAmelCase     (   __lowerCamelCase							:			int       ):


             '''simple docstring'''



             return index | (index + 1)





       @staticmethod
       def 						lowerCAmelCase     (   __lowerCamelCase							:			int       ):


             '''simple docstring'''



             return (index & (index + 1)) - 1





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  value
             while index < self.size:
                   lowerCamelCase__		:				Tuple    =  self.get_prev(__lowerCamelCase       ) + 1
                   if current_left_border == index:
                         lowerCamelCase__		:				Optional[Any]    =  value
                   else:
                         lowerCamelCase__		:				str    =  max(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )
                   lowerCamelCase__		:				Dict    =  self.get_next(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             right -= 1  # Because of right is exclusive
             lowerCamelCase__		:				str    =  0
             while left <= right:
                   lowerCamelCase__		:				Optional[Any]    =  self.get_prev(__lowerCamelCase       )
                   if left <= current_left:
                         lowerCamelCase__		:				Optional[Any]    =  max(__lowerCamelCase					,      self.tree[right]       )
                         lowerCamelCase__		:				Any    =  current_left
                   else:
                         lowerCamelCase__		:				Optional[Any]    =  max(__lowerCamelCase					,      self.arr[right]       )
                         right -= 1
             return result


if __name__ == "__main__":
    import doctest

    doctest.testmod()


