


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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


a__       :							str        =  logging.get_logger(__name__)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						List[str]					      =    ["pixel_values"]




	def __init__(				self    :       Optional[int]    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Dict[str, int] = None    ,    UpperCAmelCase__    :       float = None    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BILINEAR    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Union[int, float] = 1 / 2_5_5    ,    UpperCAmelCase__    :       bool = True    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    **UpperCAmelCase__    :       Union[str, Any]    ,    ) ->			None:
						super().__init__(**UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       size if size is not None else {"shortest_edge": 3_8_4}
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__    ,    default_to_square=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       do_resize
						__SCREAMING_SNAKE_CASE		     =       size
						# Default value set here for backwards compatibility where the value in config is None
						__SCREAMING_SNAKE_CASE		     =       crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
						__SCREAMING_SNAKE_CASE		     =       resample
						__SCREAMING_SNAKE_CASE		     =       do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else IMAGENET_STANDARD_STD




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Dict[str, int]    ,    UpperCAmelCase__    :       float    ,    UpperCAmelCase__    :       PILImageResampling = PILImageResampling.BICUBIC    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Optional[int]    ,    ) ->			np.ndarray:
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__    ,    default_to_square=UpperCAmelCase__     )
						if "shortest_edge" not in size:
											raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""     )
						__SCREAMING_SNAKE_CASE		     =       size["shortest_edge"]

						if shortest_edge < 3_8_4:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											__SCREAMING_SNAKE_CASE		     =       int(shortest_edge / crop_pct     )
											__SCREAMING_SNAKE_CASE		     =       get_resize_output_image_size(UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    default_to_square=UpperCAmelCase__     )
											__SCREAMING_SNAKE_CASE		     =       resize(image=UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    resample=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=UpperCAmelCase__    ,    size=(shortest_edge, shortest_edge)    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )
						else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    UpperCAmelCase__    ,    size=(shortest_edge, shortest_edge)    ,    resample=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[int, float]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       int    ,    ) ->			Dict:
						return rescale(UpperCAmelCase__    ,    scale=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       np.ndarray    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Union[float, List[float]]    ,    UpperCAmelCase__    :       Optional[Union[str, ChannelDimension]] = None    ,    **UpperCAmelCase__    :       Union[str, Any]    ,    ) ->			np.ndarray:
						return normalize(UpperCAmelCase__    ,    mean=UpperCAmelCase__    ,    std=UpperCAmelCase__    ,    data_format=UpperCAmelCase__    ,    **UpperCAmelCase__     )




	def 						UpperCAmelCase_   (				self    :       Tuple    ,    UpperCAmelCase__    :       ImageInput    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       Dict[str, int] = None    ,    UpperCAmelCase__    :       float = None    ,    UpperCAmelCase__    :       PILImageResampling = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       float = None    ,    UpperCAmelCase__    :       bool = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[float, List[float]]] = None    ,    UpperCAmelCase__    :       Optional[Union[str, TensorType]] = None    ,    UpperCAmelCase__    :       ChannelDimension = ChannelDimension.FIRST    ,    **UpperCAmelCase__    :       List[str]    ,    ) ->			PIL.Image.Image:
						__SCREAMING_SNAKE_CASE		     =       do_resize if do_resize is not None else self.do_resize
						__SCREAMING_SNAKE_CASE		     =       crop_pct if crop_pct is not None else self.crop_pct
						__SCREAMING_SNAKE_CASE		     =       resample if resample is not None else self.resample
						__SCREAMING_SNAKE_CASE		     =       do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE		     =       rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE		     =       do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE		     =       image_mean if image_mean is not None else self.image_mean
						__SCREAMING_SNAKE_CASE		     =       image_std if image_std is not None else self.image_std

						__SCREAMING_SNAKE_CASE		     =       size if size is not None else self.size
						__SCREAMING_SNAKE_CASE		     =       get_size_dict(UpperCAmelCase__    ,    default_to_square=UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       make_list_of_images(UpperCAmelCase__     )

						if not valid_images(UpperCAmelCase__     ):
											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 or resample is None:
											raise ValueError("Size and resample must be specified if do_resize is True."     )

						if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
											raise ValueError("crop_pct must be specified if size < 384."     )

						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."     )

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

						if do_resize:
											__SCREAMING_SNAKE_CASE		     =       [self.resize(image=UpperCAmelCase__    ,    size=UpperCAmelCase__    ,    crop_pct=UpperCAmelCase__    ,    resample=UpperCAmelCase__     ) for image in images]

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

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

						__SCREAMING_SNAKE_CASE		     =       [to_channel_dimension_format(UpperCAmelCase__    ,    UpperCAmelCase__     ) for image in images]

						__SCREAMING_SNAKE_CASE		     =       {"pixel_values": images}
						return BatchFeature(data=UpperCAmelCase__    ,    tensor_type=UpperCAmelCase__     )




"""simple docstring"""







import copy

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


a__       :							Dict        =  logging.get_logger(__name__)

a__       :							str        =  {
    '''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_			(				UpperCamelCase):

	"""simple docstring"""





	snake_case__       :						Optional[Any]					      =    "dpt"




	def __init__(				self    :       Tuple    ,    UpperCAmelCase__    :       int=7_6_8    ,    UpperCAmelCase__    :       List[str]=1_2    ,    UpperCAmelCase__    :       Any=1_2    ,    UpperCAmelCase__    :       int=3_0_7_2    ,    UpperCAmelCase__    :       List[str]="gelu"    ,    UpperCAmelCase__    :       Union[str, Any]=0.0    ,    UpperCAmelCase__    :       Any=0.0    ,    UpperCAmelCase__    :       List[Any]=0.02    ,    UpperCAmelCase__    :       List[Any]=1E-12    ,    UpperCAmelCase__    :       List[str]=3_8_4    ,    UpperCAmelCase__    :       Tuple=1_6    ,    UpperCAmelCase__    :       Optional[Any]=3    ,    UpperCAmelCase__    :       str=False    ,    UpperCAmelCase__    :       Any=True    ,    UpperCAmelCase__    :       Optional[Any]=[2, 5, 8, 1_1]    ,    UpperCAmelCase__    :       Union[str, Any]="project"    ,    UpperCAmelCase__    :       Dict=[4, 2, 1, 0.5]    ,    UpperCAmelCase__    :       Optional[Any]=[9_6, 1_9_2, 3_8_4, 7_6_8]    ,    UpperCAmelCase__    :       List[str]=2_5_6    ,    UpperCAmelCase__    :       Optional[Any]=-1    ,    UpperCAmelCase__    :       Union[str, Any]=False    ,    UpperCAmelCase__    :       Any=True    ,    UpperCAmelCase__    :       Dict=0.4    ,    UpperCAmelCase__    :       Union[str, Any]=2_5_5    ,    UpperCAmelCase__    :       List[Any]=0.1    ,    UpperCAmelCase__    :       Optional[int]=[1, 1_0_2_4, 2_4, 2_4]    ,    UpperCAmelCase__    :       List[Any]=[0, 1]    ,    UpperCAmelCase__    :       List[str]=None    ,    **UpperCAmelCase__    :       List[Any]    ,    ) ->			Dict:
						super().__init__(**UpperCAmelCase__     )

						__SCREAMING_SNAKE_CASE		     =       hidden_size
						__SCREAMING_SNAKE_CASE		     =       is_hybrid

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

											__SCREAMING_SNAKE_CASE		     =       backbone_featmap_shape
											__SCREAMING_SNAKE_CASE		     =       neck_ignore_stages

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

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




	def 						UpperCAmelCase_   (				self    :       Optional[int]     ) ->			str:
						__SCREAMING_SNAKE_CASE		     =       copy.deepcopy(self.__dict__     )

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

						__SCREAMING_SNAKE_CASE		     =       self.__class__.model_type
						return output
