code: 
  
  
  '''simple docstring'''
  
  
  
  
  import copy
  from typing import Dict, Optional
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  from ..auto import CONFIG_MAPPING
  
  
  __lowercase  :      str      		=				logging.get_logger(__name__)
  
  __lowercase  :      List[Any]      		=				{
      """shi-labs/oneformer_ade20k_swin_tiny""": (
          """https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"""
      ),
      # See all OneFormer models at https://huggingface.co/models?filter=oneformer
  }
  
  
  
  
  
  class _snake_case   ( a__   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     '''oneformer'''
  							__a												=     {'''hidden_size''': '''hidden_dim'''}
  
  
  
  
  
  
  
  							def __init__(   self			,				__A = None			,				__A = 255			,				__A = 150			,				__A = 0.1			,				__A = 2.0			,				__A = 5.0			,				__A = 5.0			,				__A = 0.5			,				__A = 0.07			,				__A = 12544			,				__A = 3.0			,				__A = 0.75			,				__A = 0.02			,				__A = 1.0			,				__A = 1E-05			,				__A = False			,				__A = True			,				__A = True			,				__A = [4, 8, 16, 32]			,				__A = 77			,				__A = 256			,				__A = 77			,				__A = 6			,				__A = 49408			,				__A = 2			,				__A = 16			,				__A = 256			,				__A = 256			,				__A = 256			,				__A = 1024			,				__A = "GN"			,				__A = 6			,				__A = 10			,				__A = True			,				__A = 8			,				__A = 0.1			,				__A = 2048			,				__A = False			,				__A = False			,				__A = 2			,				__A = 4			,				**__A			,				)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if backbone_config is None:
  															logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."	)
  															__lowerCamelCase		:      str											=					CONFIG_MAPPING["swin"](
  															    image_size=224			,				in_channels=3			,				patch_size=4			,				embed_dim=96			,				depths=[2, 2, 6, 2]			,				num_heads=[3, 6, 12, 24]			,				window_size=7			,				drop_path_rate=0.3			,				use_absolute_embeddings=__A			,				out_features=["stage1", "stage2", "stage3", "stage4"]			,				)
  											elif isinstance(__A			,				__A	):
  															__lowerCamelCase		:      Any											=					backbone_config.get("model_type"	)
  															__lowerCamelCase		:      Union[str, Any]											=					CONFIG_MAPPING[backbone_model_type]
  															__lowerCamelCase		:      str											=					config_class.from_dict(__A	)
  
  											__lowerCamelCase		:      List[str]											=					backbone_config
  
  											__lowerCamelCase		:      List[Any]											=					ignore_value
  											__lowerCamelCase		:      Optional[int]											=					num_queries
  											__lowerCamelCase		:      int											=					no_object_weight
  											__lowerCamelCase		:      Optional[Any]											=					class_weight
  											__lowerCamelCase		:      str											=					mask_weight
  											__lowerCamelCase		:      Any											=					dice_weight
  											__lowerCamelCase		:      Any											=					contrastive_weight
  											__lowerCamelCase		:      Optional[Any]											=					contrastive_temperature
  											__lowerCamelCase		:      Tuple											=					train_num_points
  											__lowerCamelCase		:      int											=					oversample_ratio
  											__lowerCamelCase		:      str											=					importance_sample_ratio
  											__lowerCamelCase		:      Optional[int]											=					init_std
  											__lowerCamelCase		:      Union[str, Any]											=					init_xavier_std
  											__lowerCamelCase		:      Optional[int]											=					layer_norm_eps
  											__lowerCamelCase		:      Any											=					is_training
  											__lowerCamelCase		:      Union[str, Any]											=					use_auxiliary_loss
  											__lowerCamelCase		:      Dict											=					output_auxiliary_logits
  											__lowerCamelCase		:      Dict											=					strides
  											__lowerCamelCase		:      int											=					task_seq_len
  											__lowerCamelCase		:      int											=					text_encoder_width
  											__lowerCamelCase		:      Union[str, Any]											=					text_encoder_context_length
  											__lowerCamelCase		:      List[Any]											=					text_encoder_num_layers
  											__lowerCamelCase		:      Any											=					text_encoder_vocab_size
  											__lowerCamelCase		:      List[Any]											=					text_encoder_proj_layers
  											__lowerCamelCase		:      Dict											=					text_encoder_n_ctx
  											__lowerCamelCase		:      List[Any]											=					conv_dim
  											__lowerCamelCase		:      str											=					mask_dim
  											__lowerCamelCase		:      str											=					hidden_dim
  											__lowerCamelCase		:      Union[str, Any]											=					encoder_feedforward_dim
  											__lowerCamelCase		:      Union[str, Any]											=					norm
  											__lowerCamelCase		:      Tuple											=					encoder_layers
  											__lowerCamelCase		:      Tuple											=					decoder_layers
  											__lowerCamelCase		:      Any											=					use_task_norm
  											__lowerCamelCase		:      Dict											=					num_attention_heads
  											__lowerCamelCase		:      str											=					dropout
  											__lowerCamelCase		:      Union[str, Any]											=					dim_feedforward
  											__lowerCamelCase		:      Dict											=					pre_norm
  											__lowerCamelCase		:      int											=					enforce_input_proj
  											__lowerCamelCase		:      Any											=					query_dec_layers
  											__lowerCamelCase		:      int											=					common_stride
  											__lowerCamelCase		:      Any											=					decoder_layers
  
  											super().__init__(**__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict[str, any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					copy.deepcopy(self.__dict__	)
  											__lowerCamelCase		:      int											=					self.backbone_config.to_dict()
  											__lowerCamelCase		:      Any											=					self.__class__.model_type
  											return output
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  from __future__ import annotations
  
  from collections import deque
  from enum import Enum
  from math import atana, degrees
  from sys import maxsize
  
  
  
  
  
  class _snake_case   ( a__   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     1
  							__a												=     2
  							__a												=     3
  
  
  
  
  
  
  
  							def __repr__(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return f'{self.__class__.__name__}.{self.name}'
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->float:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase						,					__lowerCamelCase		:      str											=					point
  				return degrees(atana(y - miny							,						x - minx    )    )
  
  
  
  
  
  def 							a_       (						lowercase							,						lowercase							,						lowercase    )			->Direction:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase						,					__lowerCamelCase		:      List[str]											=					starting
  				__lowerCamelCase						,					__lowerCamelCase		:      Any											=					via
  				__lowerCamelCase						,					__lowerCamelCase		:      str											=					target
  				__lowerCamelCase		:      str											=					degrees(atana(ya - ya							,						xa - xa    )    )
  				via_angle %= 360
  				__lowerCamelCase		:      List[Any]											=					degrees(atana(ya - ya							,						xa - xa    )    )
  				target_angle %= 360
  				# t-
  				#  \ \
  				#   \ v
  				#    \|
  				#     s
  				# via_angle is always lower than target_angle, if direction is left.
  				# If they are same, it means they are on a same line of convex hull.
  				if target_angle > via_angle:
  								return Direction.left
  				elif target_angle == via_angle:
  								return Direction.straight
  				else:
  								return Direction.right
  
  
  
  
  
  def 							a_       (						lowercase    )			->list[tuple[int, int]]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				if len(lowercase    ) <= 2:
  								# There is no convex hull
  								raise ValueError("graham_scan: argument must contain more than 3 points."    )
  				if len(lowercase    ) == 3:
  								return points
  				# find the lowest and the most left point
  				__lowerCamelCase		:      Optional[Any]											=					0
  				__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					maxsize, maxsize
  				for i, point in enumerate(lowercase    ):
  								__lowerCamelCase		:      Dict											=					point[0]
  								__lowerCamelCase		:      Optional[int]											=					point[1]
  								if y < miny:
  												__lowerCamelCase		:      Dict											=					y
  												__lowerCamelCase		:      int											=					x
  												__lowerCamelCase		:      List[Any]											=					i
  								if y == miny and x < minx:
  												__lowerCamelCase		:      Optional[int]											=					x
  												__lowerCamelCase		:      Any											=					i
  
      # remove the lowest and the most left point from points for preparing for sort
  				points.pop(lowercase    )
  
  				__lowerCamelCase		:      Optional[Any]											=					sorted(lowercase							,						key=lambda lowercase    : angle_comparer(lowercase							,						lowercase							,						lowercase    )    )
  				# This insert actually costs complexity,
  				# and you should instead add (minx, miny) into stack later.
  				# I'm using insert just for easy understanding.
  				sorted_points.insert(0							,						(minx, miny)    )
  
  				__lowerCamelCase		:      deque[tuple[int, int]]											=					deque()
  				stack.append(sorted_points[0]    )
  				stack.append(sorted_points[1]    )
  				stack.append(sorted_points[2]    )
  				# In any ways, the first 3 points line are towards left.
  				# Because we sort them the angle from minx, miny.
  				__lowerCamelCase		:      str											=					Direction.left
  
  				for i in range(3							,						len(lowercase    )    ):
  								while True:
  												__lowerCamelCase		:      List[Any]											=					stack[-2]
  												__lowerCamelCase		:      str											=					stack[-1]
  												__lowerCamelCase		:      int											=					sorted_points[i]
  												__lowerCamelCase		:      List[Any]											=					check_direction(lowercase							,						lowercase							,						lowercase    )
  
  												if next_direction == Direction.left:
  																__lowerCamelCase		:      Optional[int]											=					Direction.left
  																break
  												if next_direction == Direction.straight:
  																if current_direction == Direction.left:
  																				# We keep current_direction as left.
  																				# Because if the straight line keeps as straight,
  																				# we want to know if this straight line is towards left.
  																				break
  																elif current_direction == Direction.right:
  																				# If the straight line is towards right,
  																				# every previous points on those straigh line is not convex hull.
  																				stack.pop()
  												if next_direction == Direction.right:
  																stack.pop()
  								stack.append(sorted_points[i]    )
  				return list(lowercase    )
  
  
  
  
  
  