


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

A      :    int		=					logging.get_logger(__name__)







@add_end_docstrings(lowercase__)
class 			_lowercase (							lowercase__):


       """simple docstring"""




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


             '''simple docstring'''



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





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


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			List[str]					,      **__lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             return {}, {}, {}





       def 						lowerCAmelCase     (   self							:			str					,      __lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  load_image(__lowerCamelCase       )
             lowerCamelCase__		:				List[str]    =  image.size
             lowerCamelCase__		:				Dict    =  self.image_processor(images=__lowerCamelCase					,      return_tensors=self.framework       )
             return model_inputs





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  self.model(**__lowerCamelCase       )
             return model_outputs





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


             '''simple docstring'''



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


