




"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)


if is_vision_available():
      import PIL




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''pixel_values''']




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	bool = True     ,			snake_case_						:	Dict[str, int] = None     ,			snake_case_						:	PILImageResampling = PILImageResampling.BICUBIC     ,			snake_case_						:	bool = True     ,			snake_case_						:	Dict[str, int] = None     ,			snake_case_						:	bool = True     ,			snake_case_						:	Union[int, float] = 1 / 255     ,			snake_case_						:	bool = True     ,			snake_case_						:	Optional[Union[float, List[float]]] = None     ,			snake_case_						:	Optional[Union[float, List[float]]] = None     ,			snake_case_						:	bool = True     ,			**snake_case_						:	List[str]     ,			)				->							None:




     '''simple docstring'''

     super().__init__(**snake_case_				)
     A__	=    size if size is not None else {"shortest_edge": 224}
     A__	=    get_size_dict(snake_case_     ,			default_to_square=snake_case_				)
     A__	=    crop_size if crop_size is not None else {"height": 224, "width": 224}
     A__	=    get_size_dict(snake_case_     ,			default_to_square=snake_case_     ,			param_name="crop_size"				)

     A__	=    do_resize
     A__	=    size
     A__	=    resample
     A__	=    do_center_crop
     A__	=    crop_size
     A__	=    do_rescale
     A__	=    rescale_factor
     A__	=    do_normalize
     A__	=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     A__	=    image_std if image_std is not None else OPENAI_CLIP_STD
     A__	=    do_convert_rgb




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	np.ndarray     ,			snake_case_						:	Dict[str, int]     ,			snake_case_						:	PILImageResampling = PILImageResampling.BICUBIC     ,			snake_case_						:	Optional[Union[str, ChannelDimension]] = None     ,			**snake_case_						:	Dict     ,			)				->							np.ndarray:




     '''simple docstring'''

     A__	=    get_size_dict(snake_case_     ,			default_to_square=snake_case_				)
     if "shortest_edge" not in size:
      raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
     A__	=    get_resize_output_image_size(snake_case_     ,			size=size["shortest_edge"]     ,			default_to_square=snake_case_				)
     return resize(snake_case_     ,			size=snake_case_     ,			resample=snake_case_     ,			data_format=snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	np.ndarray     ,			snake_case_						:	Dict[str, int]     ,			snake_case_						:	Optional[Union[str, ChannelDimension]] = None     ,			**snake_case_						:	List[str]     ,			)				->							np.ndarray:




     '''simple docstring'''

     A__	=    get_size_dict(snake_case_				)
     if "height" not in size or "width" not in size:
      raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
     return center_crop(snake_case_     ,			size=(size["height"], size["width"])     ,			data_format=snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	np.ndarray     ,			snake_case_						:	Union[int, float]     ,			snake_case_						:	Optional[Union[str, ChannelDimension]] = None     ,			**snake_case_						:	str     ,			)				->							Tuple:




     '''simple docstring'''

     return rescale(snake_case_     ,			scale=snake_case_     ,			data_format=snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	np.ndarray     ,			snake_case_						:	Union[float, List[float]]     ,			snake_case_						:	Union[float, List[float]]     ,			snake_case_						:	Optional[Union[str, ChannelDimension]] = None     ,			**snake_case_						:	Union[str, Any]     ,			)				->							np.ndarray:




     '''simple docstring'''

     return normalize(snake_case_     ,			mean=snake_case_     ,			std=snake_case_     ,			data_format=snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	str     ,			snake_case_						:	ImageInput     ,			snake_case_						:	bool = None     ,			snake_case_						:	Dict[str, int] = None     ,			snake_case_						:	PILImageResampling = None     ,			snake_case_						:	bool = None     ,			snake_case_						:	int = None     ,			snake_case_						:	bool = None     ,			snake_case_						:	float = None     ,			snake_case_						:	bool = None     ,			snake_case_						:	Optional[Union[float, List[float]]] = None     ,			snake_case_						:	Optional[Union[float, List[float]]] = None     ,			snake_case_						:	bool = None     ,			snake_case_						:	Optional[Union[str, TensorType]] = None     ,			snake_case_						:	Optional[ChannelDimension] = ChannelDimension.FIRST     ,			**snake_case_						:	Union[str, Any]     ,			)				->							PIL.Image.Image:




     '''simple docstring'''

     A__	=    do_resize if do_resize is not None else self.do_resize
     A__	=    size if size is not None else self.size
     A__	=    get_size_dict(snake_case_     ,			param_name="size"     ,			default_to_square=snake_case_				)
     A__	=    resample if resample is not None else self.resample
     A__	=    do_center_crop if do_center_crop is not None else self.do_center_crop
     A__	=    crop_size if crop_size is not None else self.crop_size
     A__	=    get_size_dict(snake_case_     ,			param_name="crop_size"     ,			default_to_square=snake_case_				)
     A__	=    do_rescale if do_rescale is not None else self.do_rescale
     A__	=    rescale_factor if rescale_factor is not None else self.rescale_factor
     A__	=    do_normalize if do_normalize is not None else self.do_normalize
     A__	=    image_mean if image_mean is not None else self.image_mean
     A__	=    image_std if image_std is not None else self.image_std
     A__	=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     A__	=    make_list_of_images(snake_case_				)

     if not valid_images(snake_case_				):
      raise ValueError(
          "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
          "torch.Tensor, tf.Tensor or jax.ndarray."				)

     if do_resize and size is None:
      raise ValueError("Size must be specified if do_resize is True."				)

     if do_center_crop and crop_size is None:
      raise ValueError("Crop size must be specified if do_center_crop is True."				)

     if do_rescale and rescale_factor is None:
      raise ValueError("Rescale factor must be specified if do_rescale is True."				)

     if do_normalize and (image_mean is None or image_std is None):
      raise ValueError("Image mean and std must be specified if do_normalize is True."				)

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
      A__	=    [convert_to_rgb(snake_case_				) for image in images]

     # All transformations expect numpy arrays.
     A__	=    [to_numpy_array(snake_case_				) for image in images]

     if do_resize:
      A__	=    [self.resize(image=snake_case_     ,			size=snake_case_     ,			resample=snake_case_				) for image in images]

     if do_center_crop:
      A__	=    [self.center_crop(image=snake_case_     ,			size=snake_case_				) for image in images]

     if do_rescale:
      A__	=    [self.rescale(image=snake_case_     ,			scale=snake_case_				) for image in images]

     if do_normalize:
      A__	=    [self.normalize(image=snake_case_     ,			mean=snake_case_     ,			std=snake_case_				) for image in images]

     A__	=    [to_channel_dimension_format(snake_case_     ,			snake_case_				) for image in images]

     A__	=    {"pixel_values": images}
     return BatchFeature(data=snake_case_     ,			tensor_type=snake_case_				)











"""simple docstring"""



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''dpt'''




    def __init__(   self						:	List[Any]     ,			snake_case_						:	Union[str, Any]=768     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	Tuple=12     ,			snake_case_						:	List[Any]=3_072     ,			snake_case_						:	Dict="gelu"     ,			snake_case_						:	Tuple=0.0     ,			snake_case_						:	int=0.0     ,			snake_case_						:	Optional[int]=0.02     ,			snake_case_						:	Union[str, Any]=1e-12     ,			snake_case_						:	Tuple=384     ,			snake_case_						:	Tuple=16     ,			snake_case_						:	Optional[Any]=3     ,			snake_case_						:	Dict=False     ,			snake_case_						:	Any=True     ,			snake_case_						:	Any=[2, 5, 8, 11]     ,			snake_case_						:	Union[str, Any]="project"     ,			snake_case_						:	Union[str, Any]=[4, 2, 1, 0.5]     ,			snake_case_						:	List[str]=[96, 192, 384, 768]     ,			snake_case_						:	int=256     ,			snake_case_						:	Tuple=-1     ,			snake_case_						:	List[str]=False     ,			snake_case_						:	int=True     ,			snake_case_						:	List[Any]=0.4     ,			snake_case_						:	Optional[Any]=255     ,			snake_case_						:	List[str]=0.1     ,			snake_case_						:	List[str]=[1, 1_024, 24, 24]     ,			snake_case_						:	Union[str, Any]=[0, 1]     ,			snake_case_						:	Any=None     ,			**snake_case_						:	Optional[Any]     ,			)				->							Optional[int]:




     '''simple docstring'''

     super().__init__(**snake_case_				)

     A__	=    hidden_size
     A__	=    is_hybrid

     if self.is_hybrid:
      if backbone_config is None:
       logger.info("Initializing the config with a `BiT` backbone."				)
       A__	=    {
           "global_padding": "same",
           "layer_type": "bottleneck",
           "depths": [3, 4, 9],
           "out_features": ["stage1", "stage2", "stage3"],
           "embedding_dynamic_padding": True,
       }
       A__	=    BitConfig(**snake_case_				)
      elif isinstance(snake_case_     ,			snake_case_				):
       logger.info("Initializing the config with a `BiT` backbone."				)
       A__	=    BitConfig(**snake_case_				)
      elif isinstance(snake_case_     ,			snake_case_				):
       A__	=    backbone_config
      else:
       raise ValueError(
           F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""				)

      A__	=    backbone_featmap_shape
      A__	=    neck_ignore_stages

      if readout_type != "project":
       raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."				)
     else:
      A__	=    None
      A__	=    None
      A__	=    []

     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    intermediate_size
     A__	=    hidden_act
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    initializer_range
     A__	=    layer_norm_eps
     A__	=    image_size
     A__	=    patch_size
     A__	=    num_channels
     A__	=    qkv_bias
     A__	=    backbone_out_indices
     if readout_type not in ["ignore", "add", "project"]:
      raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"				)
     A__	=    readout_type
     A__	=    reassemble_factors
     A__	=    neck_hidden_sizes
     A__	=    fusion_hidden_size
     A__	=    head_in_index
     A__	=    use_batch_norm_in_fusion_residual
     # auxiliary head attributes (semantic segmentation)
     A__	=    use_auxiliary_head
     A__	=    auxiliary_loss_weight
     A__	=    semantic_loss_ignore_index
     A__	=    semantic_classifier_dropout




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    copy.deepcopy(self.__dict__				)

     if output["backbone_config"] is not None:
      A__	=    self.backbone_config.to_dict()

     A__	=    self.__class__.model_type
     return output





