



import copy

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


__snake_case     :Dict			       =logging.get_logger(__name__)

__snake_case     :int			       ={
    '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        lowerCAmelCase__					(  _a       ):
				A_   :     Any								=     'dpt'


				def __init__(							self      :  List[Any]						, __UpperCamelCase      :  int=768						, __UpperCamelCase      :  str=12						, __UpperCamelCase      :  Any=12						, __UpperCamelCase      :  List[Any]=3_072						, __UpperCamelCase      :  Any="gelu"						, __UpperCamelCase      :  Any=0.0						, __UpperCamelCase      :  Union[str, Any]=0.0						, __UpperCamelCase      :  List[str]=0.0_2						, __UpperCamelCase      :  Any=1e-12						, __UpperCamelCase      :  List[Any]=384						, __UpperCamelCase      :  Optional[Any]=16						, __UpperCamelCase      :  Optional[int]=3						, __UpperCamelCase      :  int=False						, __UpperCamelCase      :  str=True						, __UpperCamelCase      :  Dict=[2, 5, 8, 11]						, __UpperCamelCase      :  Optional[Any]="project"						, __UpperCamelCase      :  int=[4, 2, 1, 0.5]						, __UpperCamelCase      :  Optional[Any]=[96, 192, 384, 768]						, __UpperCamelCase      :  List[Any]=256						, __UpperCamelCase      :  Optional[int]=-1						, __UpperCamelCase      :  int=False						, __UpperCamelCase      :  Optional[int]=True						, __UpperCamelCase      :  Any=0.4						, __UpperCamelCase      :  List[str]=255						, __UpperCamelCase      :  Any=0.1						, __UpperCamelCase      :  Tuple=[1, 1_024, 24, 24]						, __UpperCamelCase      :  Optional[int]=[0, 1]						, __UpperCamelCase      :  int=None						, **__UpperCamelCase      :  Dict						, )		->     Union[str, Any]:
											super().__init__(**lowerCAmelCase_       )

											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(**lowerCAmelCase_       )
																		elif isinstance(lowerCAmelCase_						, lowerCAmelCase_       ):
																									logger.info('Initializing the config with a `BiT` backbone.'       )
																									A      						=    BitConfig(**lowerCAmelCase_       )
																		elif isinstance(lowerCAmelCase_						, lowerCAmelCase_       ):
																									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 __UpperCamelCase						(							self      :  Tuple       )		->     List[str]:
											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





'''simple docstring'''


import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
				_snake_case : Tuple  				=						{
				    'linear': PIL.Image.Resampling.BILINEAR,
				    'bilinear': PIL.Image.Resampling.BILINEAR,
				    'bicubic': PIL.Image.Resampling.BICUBIC,
				    'lanczos': PIL.Image.Resampling.LANCZOS,
				    'nearest': PIL.Image.Resampling.NEAREST,
				}
else:
				_snake_case : Any  				=						{
				    'linear': PIL.Image.LINEAR,
				    'bilinear': PIL.Image.BILINEAR,
				    'bicubic': PIL.Image.BICUBIC,
				    'lanczos': PIL.Image.LANCZOS,
				    'nearest': PIL.Image.NEAREST,
				}
def 						snake_case_       (UpperCamelCase			:    Optional[int] ):





							'''simple docstring'''


							_a      	=							(images / 2 + 0.5).clamp(0					,		1 )
							_a      	=							images.cpu().permute(0					,		2					,		3					,		1 ).float().numpy()
							_a      	=							numpy_to_pil(UpperCamelCase )
							return images

def 						snake_case_       (UpperCamelCase			:    str ):





							'''simple docstring'''


							if images.ndim == 3:
														_a      	=							images[None, ...]
							_a      	=							(images * 255).round().astype('''uint8''' )
							if images.shape[-1] == 1:
														# special case for grayscale (single channel) images
														_a      	=							[Image.fromarray(image.squeeze()					,		mode='''L''' ) for image in images]
							else:
														_a      	=							[Image.fromarray(UpperCamelCase ) for image in images]

							return pil_images

