code: 
  from typing import Any, Dict, List, Mapping
  
  import numpy as np
  import torch
  
  from ...utils import is_cython_available, requires_backends
  
  
  if is_cython_available():
  	import pyximport
  
  	pyximport.install(setup_args={"include_dirs": np.get_include()})
  	from . import algos_graphormer  # noqa E402
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Union[str, Any]	,	SCREAMING_SNAKE_CASE_				: int = 5_12      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[int]			=	x.shape[1] if len(x.shape      ) > 1 else 1
  							a_       :   Any			=	1 + np.arange(0	,	feature_num * offset	,	__lowerCamelCase	,	dtype=np.intaa      )
  							a_       :   List[Any]			=	x + feature_offset
  							return x
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Union[str, Any]	,	SCREAMING_SNAKE_CASE_				: Tuple=True      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							requires_backends(__lowerCamelCase	,	["""cython"""]      )
  
  							if keep_features and "edge_attr" in item.keys():  # edge_attr
  														a_       :   Tuple			=	np.asarray(item["""edge_attr"""]	,	dtype=np.intaa      )
  							else:
  														a_       :   str			=	np.ones((len(item["""edge_index"""][0]      ), 1)	,	dtype=np.intaa      )  # same embedding for all
  
  							if keep_features and "node_feat" in item.keys():  # input_nodes
  														a_       :   Union[str, Any]			=	np.asarray(item["""node_feat"""]	,	dtype=np.intaa      )
  							else:
  														a_       :   str			=	np.ones((item["""num_nodes"""], 1)	,	dtype=np.intaa      )  # same embedding for all
  
  							a_       :   str			=	np.asarray(item["""edge_index"""]	,	dtype=np.intaa      )
  
  							a_       :   str			=	convert_to_single_emb(__lowerCamelCase      ) + 1
  							a_       :   str			=	item["""num_nodes"""]
  
  							if len(edge_attr.shape      ) == 1:
  														a_       :   Optional[int]			=	edge_attr[:, None]
  							a_       :   Tuple			=	np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]	,	dtype=np.intaa      )
  							a_       :   List[Any]			=	convert_to_single_emb(__lowerCamelCase      ) + 1
  
  							# node adj matrix [num_nodes, num_nodes] bool
  							a_       :   str			=	np.zeros([num_nodes, num_nodes]	,	dtype=__lowerCamelCase      )
  							a_       :   List[Any]			=	True
  
  							a_		,				a_       :   Union[str, Any]			=	algos_graphormer.floyd_warshall(__lowerCamelCase      )
  							a_       :   str			=	np.amax(__lowerCamelCase      )
  
  							a_       :   List[str]			=	algos_graphormer.gen_edge_input(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase      )
  							a_       :   str			=	np.zeros([num_nodes + 1, num_nodes + 1]	,	dtype=np.single      )  # with graph token
  
  							# combine
  							a_       :   str			=	input_nodes + 1  # we shift all indices by one for padding
  							a_       :   List[str]			=	attn_bias
  							a_       :   Union[str, Any]			=	attn_edge_type
  							a_       :   List[str]			=	shortest_path_result.astype(np.intaa      ) + 1  # we shift all indices by one for padding
  							a_       :   Tuple			=	np.sum(__lowerCamelCase	,	axis=1      ).reshape(-1      ) + 1  # we shift all indices by one for padding
  							a_       :   Optional[int]			=	item["""in_degree"""]  # for undirected graph
  							a_       :   int			=	input_edges + 1  # we shift all indices by one for padding
  							if "labels" not in item:
  														a_       :   Union[str, Any]			=	item["""y"""]
  
  							return item
  
  
  
  
  
  class 					snake_case__  :
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_=20				,    UpperCamelCase_=False							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if not is_cython_available():
  															raise ImportError("""Graphormer preprocessing needs Cython (pyximport)"""							)
  
  								a_       :   Optional[Any]			=	spatial_pos_max
  								a_       :   Optional[int]			=	on_the_fly_processing
  
  
  
  
  
  	def __call__(  self				,    UpperCamelCase_							)		->			Dict[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.on_the_fly_processing:
  															a_       :   Any			=	[preprocess_item(UpperCamelCase_							) for i in features]
  
  								if not isinstance(features[0]				,    UpperCamelCase_							):
  															a_       :   Tuple			=	[vars(UpperCamelCase_							) for f in features]
  								a_       :   List[str]			=	{}
  
  								a_       :   Any			=	max(len(i["""input_nodes"""]							) for i in features							)
  								a_       :   List[str]			=	len(features[0]["""input_nodes"""][0]							)
  								a_       :   int			=	len(features[0]["""attn_edge_type"""][0][0]							)
  								a_       :   List[Any]			=	max(len(i["""input_edges"""][0][0]							) for i in features							)
  								a_       :   int			=	len(features[0]["""input_edges"""][0][0][0]							)
  								a_       :   Tuple			=	len(UpperCamelCase_							)
  
  								a_       :   Union[str, Any]			=	torch.zeros(UpperCamelCase_				,    max_node_num + 1				,    max_node_num + 1				,    dtype=torch.float							)
  								a_       :   List[Any]			=	torch.zeros(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    dtype=torch.long							)
  								a_       :   str			=	torch.zeros(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    dtype=torch.long							)
  								a_       :   Union[str, Any]			=	torch.zeros(UpperCamelCase_				,    UpperCamelCase_				,    dtype=torch.long							)
  								a_       :   Any			=	torch.zeros(UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    dtype=torch.long							)
  								a_       :   Tuple			=	torch.zeros(
  								    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    dtype=torch.long							)
  
  								for ix, f in enumerate(UpperCamelCase_							):
  															for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
  																						a_       :   Dict			=	torch.tensor(f[k]							)
  
  															if len(f["""attn_bias"""][1:, 1:][f["""spatial_pos"""] >= self.spatial_pos_max]							) > 0:
  																						a_       :   Union[str, Any]			=	float("""-inf"""							)
  
  															a_       :   Optional[int]			=	f["""attn_bias"""]
  															a_       :   List[str]			=	f[
  															    """attn_edge_type"""
  															]
  															a_       :   Any			=	f["""spatial_pos"""]
  															a_       :   Dict			=	f["""in_degree"""]
  															a_       :   str			=	f["""input_nodes"""]
  															a_       :   Optional[int]			=	f["""input_edges"""]
  
  								a_       :   Union[str, Any]			=	batch["""in_degree"""]
  
  								a_       :   int			=	features[0]["""labels"""]
  								if len(UpperCamelCase_							) == 1:  # one task
  															if isinstance(sample[0]				,    UpperCamelCase_							):  # regression
  																						a_       :   List[str]			=	torch.from_numpy(np.concatenate([i["""labels"""] for i in features]							)							)
  															else:  # binary classification
  																						a_       :   Union[str, Any]			=	torch.from_numpy(np.concatenate([i["""labels"""] for i in features]							)							)
  								else:  # multi task classification, left to float to keep the NaNs
  															a_       :   Tuple			=	torch.from_numpy(np.stack([i["""labels"""] for i in features]				,    axis=0							)							)
  
  								return batch
  
  
  
style_context: 
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : str          =    {
      "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
      "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
      "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
      "apple/deeplabv3-mobilevit-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-x-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
      ),
      "apple/deeplabv3-mobilevit-xx-small": (
          "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
      ),
      # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
  }
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							List[str]         = """mobilevit"""
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_=3				,    UpperCamelCase_=256				,    UpperCamelCase_=2				,    UpperCamelCase_=[144, 192, 240]				,    UpperCamelCase_=[16, 32, 64, 96, 128, 160, 640]				,    UpperCamelCase_=4				,    UpperCamelCase_=2.0				,    UpperCamelCase_=4.0				,    UpperCamelCase_="silu"				,    UpperCamelCase_=3				,    UpperCamelCase_=32				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.0				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-5				,    UpperCamelCase_=True				,    UpperCamelCase_=256				,    UpperCamelCase_=[6, 12, 18]				,    UpperCamelCase_=0.1				,    UpperCamelCase_=255				,    **UpperCamelCase_				,    )		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__(**UpperCamelCase_							)
  
  								a_       :   Optional[int]			=	num_channels
  								a_       :   Optional[Any]			=	image_size
  								a_       :   int			=	patch_size
  								a_       :   List[str]			=	hidden_sizes
  								a_       :   Optional[int]			=	neck_hidden_sizes
  								a_       :   List[Any]			=	num_attention_heads
  								a_       :   Union[str, Any]			=	mlp_ratio
  								a_       :   List[str]			=	expand_ratio
  								a_       :   str			=	hidden_act
  								a_       :   Union[str, Any]			=	conv_kernel_size
  								a_       :   Optional[Any]			=	output_stride
  								a_       :   Union[str, Any]			=	hidden_dropout_prob
  								a_       :   List[Any]			=	attention_probs_dropout_prob
  								a_       :   List[Any]			=	classifier_dropout_prob
  								a_       :   Union[str, Any]			=	initializer_range
  								a_       :   List[Any]			=	layer_norm_eps
  								a_       :   str			=	qkv_bias
  
  								# decode head attributes for semantic segmentation
  								a_       :   Optional[Any]			=	aspp_out_channels
  								a_       :   Any			=	atrous_rates
  								a_       :   Union[str, Any]			=	aspp_dropout_prob
  								a_       :   Any			=	semantic_loss_ignore_index
  
  
  
  
  
  class 					snake_case__  ( __A       ):
  	UpperCAmelCase   :							Optional[int]         = version.parse("""1.11"""       )
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.task == "image-classification":
  															return OrderedDict([("""logits""", {0: """batch"""})]							)
  								else:
  															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			float:
  								"""simple docstring"""
  
  
  
  
  
  
  								return 1e-4
  
  
  