'''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

__UpperCAmelCase     							=logging.get_logger(__name__)




@add_end_docstrings(UpperCAmelCase__ )
class     a__      (   UpperCAmelCase__ ):






     def __init__( self		:		List[str]  ,				*a		:		Union[str, Any]  ,				**a		:		Optional[Any]    ):





         """simple docstring"""



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






     def __call__( self		:		Any  ,				a		:		Union[str, List[str], "Image.Image", List["Image.Image"]]  ,				**a		:		Optional[int]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]  ,				**a		:		Any    ):





         """simple docstring"""



         return {}, {}, {}






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]  ,				a		:		List[str]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		int  ,				a		:		Optional[Any]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]  ,				a		:		Any    ):





         """simple docstring"""



         __lowerCamelCase		=  model_outputs.predicted_depth
         __lowerCamelCase		=  torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1    )  ,				size=self.image_size[::-1]  ,				mode='''bicubic'''  ,				align_corners=a    )
         __lowerCamelCase		=  prediction.squeeze().cpu().numpy()
         __lowerCamelCase		=  (output * 2_55 / np.max(a    )).astype('''uint8'''    )
         __lowerCamelCase		=  Image.fromarray(a    )
         __lowerCamelCase		=  {}
         __lowerCamelCase		=  predicted_depth
         __lowerCamelCase		=  depth
         return output_dict







'''simple docstring'''

from ..utils import DummyObject, requires_backends






class    UpperCAmelCase	(       metaclass=UpperCamelCase__				):
 __lowercase   		=				["""note_seq"""]
 def __init__(				self      :Optional[Any]						,       *lowercase_      :List[Any]						,       **lowercase_      :List[str]							)->							int:
     requires_backends(self						,       ["note_seq"]							)
 @classmethod
 def      UpperCAmelCase_							(				cls      :str						,       *lowercase_      :Union[str, Any]						,       **lowercase_      :Any							)->							Optional[int]:
     requires_backends(cls						,       ["note_seq"]							)



 @classmethod
 def      UpperCAmelCase_							(				cls      :Dict						,       *lowercase_      :Tuple						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     requires_backends(cls						,       ["note_seq"]							)



