


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase								=	logging.get_logger(__name__)






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    ["pixel_values"]



       def __init__(      self				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = 32				,       _SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR				,       _SCREAMING_SNAKE_CASE = True				,       **_SCREAMING_SNAKE_CASE				,       )->None:




         '''simple docstring'''





         A_      :				Optional[int]		     =					do_resize
         A_      :				int		     =					do_rescale
         A_      :				Any		     =					size_divisor
         A_      :				Dict		     =					resample
         super().__init__(**_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE		)->np.ndarray:




         '''simple docstring'''





         A_     ,       A_      :				Dict		     =					get_image_size(_SCREAMING_SNAKE_CASE		)
         # Rounds the height and width down to the closest multiple of size_divisor
         A_      :				List[Any]		     =					height // size_divisor * size_divisor
         A_      :				List[Any]		     =					width // size_divisor * size_divisor
         A_      :				List[Any]		     =					resize(_SCREAMING_SNAKE_CASE				,       (new_h, new_w)				,       resample=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return image



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE		)->np.ndarray:




         '''simple docstring'''





         return rescale(image=_SCREAMING_SNAKE_CASE				,       scale=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				,       **_SCREAMING_SNAKE_CASE				,       )->BatchFeature:




         '''simple docstring'''





         A_      :				List[Any]		     =					do_resize if do_resize is not None else self.do_resize
         A_      :				Optional[Any]		     =					do_rescale if do_rescale is not None else self.do_rescale
         A_      :				Any		     =					size_divisor if size_divisor is not None else self.size_divisor
         A_      :				Dict		     =					resample if resample is not None else self.resample

         if do_resize and size_divisor is None:
           raise ValueError('''size_divisor is required for resizing'''		)

         A_      :				str		     =					make_list_of_images(_SCREAMING_SNAKE_CASE		)

         if not valid_images(_SCREAMING_SNAKE_CASE		):
           raise ValueError('''Invalid image(s)'''		)

         # All transformations expect numpy arrays.
         A_      :				Dict		     =					[to_numpy_array(_SCREAMING_SNAKE_CASE		) for img in images]

         if do_resize:
           A_      :				Any		     =					[self.resize(_SCREAMING_SNAKE_CASE				,       size_divisor=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_rescale:
           A_      :				int		     =					[self.rescale(_SCREAMING_SNAKE_CASE				,       scale=1 / 255		) for image in images]

         A_      :				Union[str, Any]		     =					[to_channel_dimension_format(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) for image in images]

         A_      :				List[Any]		     =					{'''pixel_values''': images}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE				,       tensor_type=_SCREAMING_SNAKE_CASE		)








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


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """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   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "glpn"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=[2, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[8, 4, 2, 1]				,       _SCREAMING_SNAKE_CASE=[32, 64, 160, 256]				,       _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]				,       _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]				,       _SCREAMING_SNAKE_CASE=[1, 2, 5, 8]				,       _SCREAMING_SNAKE_CASE=[4, 4, 4, 4]				,       _SCREAMING_SNAKE_CASE="gelu"				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=10				,       _SCREAMING_SNAKE_CASE=-1				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					num_channels
         A_      :				Union[str, Any]		     =					num_encoder_blocks
         A_      :				int		     =					depths
         A_      :				Dict		     =					sr_ratios
         A_      :				Any		     =					hidden_sizes
         A_      :				int		     =					patch_sizes
         A_      :				Optional[int]		     =					strides
         A_      :				str		     =					mlp_ratios
         A_      :				List[str]		     =					num_attention_heads
         A_      :				str		     =					hidden_act
         A_      :				int		     =					hidden_dropout_prob
         A_      :				List[Any]		     =					attention_probs_dropout_prob
         A_      :				Optional[Any]		     =					initializer_range
         A_      :				Tuple		     =					drop_path_rate
         A_      :				Optional[int]		     =					layer_norm_eps
         A_      :				List[str]		     =					decoder_hidden_size
         A_      :				List[Any]		     =					max_depth
         A_      :				List[Any]		     =					head_in_index




