





'''simple docstring'''

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


UpperCAmelCase					:		Tuple		=							logging.get_logger(__name__)

UpperCAmelCase					:		int		=							{
    '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						(a__					):
  _lowercase  :	Optional[int]       					=				"""glpn"""







  def __init__(		self     ,     lowercase__=3     ,     lowercase__=4     ,     lowercase__=[2, 2, 2, 2]     ,     lowercase__=[8, 4, 2, 1]     ,     lowercase__=[32, 64, 160, 256]     ,     lowercase__=[7, 3, 3, 3]     ,     lowercase__=[4, 2, 2, 2]     ,     lowercase__=[1, 2, 5, 8]     ,     lowercase__=[4, 4, 4, 4]     ,     lowercase__="gelu"     ,     lowercase__=0.0     ,     lowercase__=0.0     ,     lowercase__=0.02     ,     lowercase__=0.1     ,     lowercase__=1E-6     ,     lowercase__=64     ,     lowercase__=10     ,     lowercase__=-1     ,     **lowercase__     ,     )		->						Optional[int]:






        """simple docstring"""





        super().__init__(**lowercase__		)

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









'''simple docstring'''

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					:		Dict		=							logging.get_logger(__name__)





class lowerCamelCase						(a__					):
  _lowercase  :	int       					=				["""pixel_values"""]







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






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize
        _snake_case		:	List[str]					     =     do_rescale
        _snake_case		:	Any					     =     size_divisor
        _snake_case		:	Optional[Any]					     =     resample
        super().__init__(**lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





        _snake_case     ,							_snake_case		:	Dict					     =     get_image_size(lowercase__		)
        # Rounds the height and width down to the closest multiple of size_divisor
        _snake_case		:	Optional[int]					     =     height // size_divisor * size_divisor
        _snake_case		:	Dict					     =     width // size_divisor * size_divisor
        _snake_case		:	str					     =     resize(lowercase__     ,     (new_h, new_w)     ,     resample=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)
        return image







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__=None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = ChannelDimension.FIRST     ,     **lowercase__     ,     )		->						BatchFeature:






        """simple docstring"""





        _snake_case		:	Any					     =     do_resize if do_resize is not None else self.do_resize
        _snake_case		:	List[Any]					     =     do_rescale if do_rescale is not None else self.do_rescale
        _snake_case		:	List[str]					     =     size_divisor if size_divisor is not None else self.size_divisor
        _snake_case		:	int					     =     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'''		)

        _snake_case		:	Tuple					     =     make_list_of_images(lowercase__		)

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

        # All transformations expect numpy arrays.
        _snake_case		:	Tuple					     =     [to_numpy_array(lowercase__		) for img in images]

        if do_resize:
              _snake_case		:	Optional[int]					     =     [self.resize(lowercase__     ,     size_divisor=lowercase__     ,     resample=lowercase__		) for image in images]

        if do_rescale:
              _snake_case		:	Union[str, Any]					     =     [self.rescale(lowercase__     ,     scale=1 / 255		) for image in images]

        _snake_case		:	Union[str, Any]					     =     [to_channel_dimension_format(lowercase__     ,     lowercase__		) for image in images]

        _snake_case		:	List[str]					     =     {'''pixel_values''': images}
        return BatchFeature(data=lowercase__     ,     tensor_type=lowercase__		)


