


"""simple docstring"""


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a_											=     logging.get_logger(__name__)

a_											=     {
    "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   (	_UpperCamelCase):
		__UpperCamelCase							     =      'oneformer'
		__UpperCamelCase							     =      {'hidden_size': 'hidden_dim'}







		def __init__(       self   :							Dict     , a__   :							Optional[Dict] = None     , a__   :							int = 2_55     , a__   :							int = 1_50     , a__   :							int = 0.1     , a__   :							float = 2.0     , a__   :							float = 5.0     , a__   :							float = 5.0     , a__   :							float = 0.5     , a__   :							float = 0.0_7     , a__   :							int = 1_25_44     , a__   :							float = 3.0     , a__   :							float = 0.7_5     , a__   :							float = 0.0_2     , a__   :							float = 1.0     , a__   :							float = 1E-0_5     , a__   :							bool = False     , a__   :							bool = True     , a__   :							bool = True     , a__   :							Optional[list] = [4, 8, 16, 32]     , a__   :							int = 77     , a__   :							int = 2_56     , a__   :							int = 77     , a__   :							int = 6     , a__   :							int = 4_94_08     , a__   :							int = 2     , a__   :							int = 16     , a__   :							int = 2_56     , a__   :							int = 2_56     , a__   :							int = 2_56     , a__   :							int = 10_24     , a__   :							str = "GN"     , a__   :							int = 6     , a__   :							int = 10     , a__   :							bool = True     , a__   :							int = 8     , a__   :							float = 0.1     , a__   :							int = 20_48     , a__   :							bool = False     , a__   :							bool = False     , a__   :							int = 2     , a__   :							int = 4     , **a__   :							List[str]     , )							->    str:





						'''simple docstring'''







						if backbone_config is None:
										logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."		)
										_A       	=      CONFIG_MAPPING["swin"](
										    image_size=2_24     , 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__		):
										_A       	=      backbone_config.get("model_type"		)
										_A       	=      CONFIG_MAPPING[backbone_model_type]
										_A       	=      config_class.from_dict(a__		)

						_A       	=      backbone_config

						_A       	=      ignore_value
						_A       	=      num_queries
						_A       	=      no_object_weight
						_A       	=      class_weight
						_A       	=      mask_weight
						_A       	=      dice_weight
						_A       	=      contrastive_weight
						_A       	=      contrastive_temperature
						_A       	=      train_num_points
						_A       	=      oversample_ratio
						_A       	=      importance_sample_ratio
						_A       	=      init_std
						_A       	=      init_xavier_std
						_A       	=      layer_norm_eps
						_A       	=      is_training
						_A       	=      use_auxiliary_loss
						_A       	=      output_auxiliary_logits
						_A       	=      strides
						_A       	=      task_seq_len
						_A       	=      text_encoder_width
						_A       	=      text_encoder_context_length
						_A       	=      text_encoder_num_layers
						_A       	=      text_encoder_vocab_size
						_A       	=      text_encoder_proj_layers
						_A       	=      text_encoder_n_ctx
						_A       	=      conv_dim
						_A       	=      mask_dim
						_A       	=      hidden_dim
						_A       	=      encoder_feedforward_dim
						_A       	=      norm
						_A       	=      encoder_layers
						_A       	=      decoder_layers
						_A       	=      use_task_norm
						_A       	=      num_attention_heads
						_A       	=      dropout
						_A       	=      dim_feedforward
						_A       	=      pre_norm
						_A       	=      enforce_input_proj
						_A       	=      query_dec_layers
						_A       	=      common_stride
						_A       	=      decoder_layers

						super().__init__(**a__		)







		def 			a_			(       self   :							Optional[Any]		)							->    Dict[str, any]:





						'''simple docstring'''







						_A       	=      copy.deepcopy(self.__dict__		)
						_A       	=      self.backbone_config.to_dict()
						_A       	=      self.__class__.model_type
						return output



"""simple docstring"""


from .hash_table import HashTable
class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Any     , *a__   :							Any     , **a__   :							Tuple		)							->    Tuple:





						'''simple docstring'''







						super().__init__(*a__     , **a__		)







		def 			a_			(       self   :							Tuple     , a__   :							int     , a__   :							str=None		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      1
						_A       	=      self.hash_function(key + i * i		)

						while self.values[new_key] is not None and self.values[new_key] != key:
										i += 1
										_A       	=      (
										    self.hash_function(key + i * i		)
										    if not self.balanced_factor() >= self.lim_charge
										    else None
										)

										if new_key is None:
														break

						return new_key