





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       :   List[str]					= logging.get_logger(__name__)


@add_end_docstrings(SCREAMING_SNAKE_CASE       )
class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			def __init__(     self  :       int				,      *__lowerCAmelCase  :       Tuple				,      **__lowerCAmelCase  :       List[str]  )      ->      Optional[int]:







					"""simple docstring"""






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






			def __call__(     self  :       Any				,      __lowerCAmelCase  :       Union[str, List[str], "Image.Image", List["Image.Image"]]				,      **__lowerCAmelCase  :       List[Any]  )      ->      str:







					"""simple docstring"""






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






			def   a_			(     self  :       List[str]				,      **__lowerCAmelCase  :       Optional[Any]  )      ->      List[Any]:







					"""simple docstring"""






					return {}, {}, {}






			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       Any  )      ->      Any:







					"""simple docstring"""






					A__     =  load_image(__lowerCAmelCase  )
					A__     =  image.size
					A__     =  self.image_processor(images=__lowerCAmelCase				,      return_tensors=self.framework  )
					return model_inputs






			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       Optional[int]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  self.model(**__lowerCAmelCase  )
					return model_outputs







			def   a_			(     self  :       Dict				,      __lowerCAmelCase  :       Optional[int]  )      ->      Any:







					"""simple docstring"""






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










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


A       :   Optional[int]					= logging.get_logger(__name__)

A       :   Dict					= {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Tuple							     =       '''glpn'''






			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       Union[str, Any]=3				,      __lowerCAmelCase  :       Any=4				,      __lowerCAmelCase  :       Tuple=[2, 2, 2, 2]				,      __lowerCAmelCase  :       int=[8, 4, 2, 1]				,      __lowerCAmelCase  :       int=[32, 64, 1_60, 2_56]				,      __lowerCAmelCase  :       str=[7, 3, 3, 3]				,      __lowerCAmelCase  :       Dict=[4, 2, 2, 2]				,      __lowerCAmelCase  :       List[str]=[1, 2, 5, 8]				,      __lowerCAmelCase  :       List[Any]=[4, 4, 4, 4]				,      __lowerCAmelCase  :       str="gelu"				,      __lowerCAmelCase  :       Union[str, Any]=0.0				,      __lowerCAmelCase  :       Tuple=0.0				,      __lowerCAmelCase  :       List[Any]=0.0_2				,      __lowerCAmelCase  :       Dict=0.1				,      __lowerCAmelCase  :       Dict=1e-6				,      __lowerCAmelCase  :       Tuple=64				,      __lowerCAmelCase  :       Tuple=10				,      __lowerCAmelCase  :       Union[str, Any]=-1				,      **__lowerCAmelCase  :       Any				,      )      ->      Tuple:







					"""simple docstring"""






					super().__init__(**__lowerCAmelCase  )

					A__     =  num_channels
					A__     =  num_encoder_blocks
					A__     =  depths
					A__     =  sr_ratios
					A__     =  hidden_sizes
					A__     =  patch_sizes
					A__     =  strides
					A__     =  mlp_ratios
					A__     =  num_attention_heads
					A__     =  hidden_act
					A__     =  hidden_dropout_prob
					A__     =  attention_probs_dropout_prob
					A__     =  initializer_range
					A__     =  drop_path_rate
					A__     =  layer_norm_eps
					A__     =  decoder_hidden_size
					A__     =  max_depth
					A__     =  head_in_index



