





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_           =      logging.get_logger(__name__)

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   'encoder-decoder'
      A_						:    Optional[int]							=   True


      def __init__(self	:				List[str]    ,      **a__	:				Any				):





         """simple docstring"""



         super().__init__(**a__				)
         assert (
             "encoder" in kwargs and "decoder" in kwargs
         ), "Config has to be initialized with encoder and decoder config"
         __snake_case								=       kwargs.pop('''encoder'''				)
         __snake_case								=       encoder_config.pop('''model_type'''				)
         __snake_case								=       kwargs.pop('''decoder'''				)
         __snake_case								=       decoder_config.pop('''model_type'''				)

         from ..auto.configuration_auto import AutoConfig

         __snake_case								=       AutoConfig.for_model(a__    ,      **a__				)
         __snake_case								=       AutoConfig.for_model(a__    ,      **a__				)
         __snake_case								=       True


      @classmethod
      def        a			(cls	:				Optional[Any]    ,      a__	:				PretrainedConfig    ,      a__	:				PretrainedConfig    ,      **a__	:				Optional[Any]				):





         """simple docstring"""



         logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''				)
         __snake_case								=       True
         __snake_case								=       True

         return cls(encoder=encoder_config.to_dict()    ,      decoder=decoder_config.to_dict()    ,      **a__				)


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       copy.deepcopy(self.__dict__				)
         __snake_case								=       self.encoder.to_dict()
         __snake_case								=       self.decoder.to_dict()
         __snake_case								=       self.__class__.model_type
         return output








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_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



         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__	:				List[str]				):





         """simple docstring"""



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


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


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





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



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

