






import copy
from typing import Dict, Optional

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


__a :    Optional[int]								=				logging.get_logger(__name__)

__a :    Optional[int]								=				{
    "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  __lowercase (		lowercase__					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			'''oneformer'''
     SCREAMING_SNAKE_CASE				  =			{'''hidden_size''': '''hidden_dim'''}




     def __init__(			self    :     Tuple	,		UpperCamelCase_    :     Optional[Dict] = None	,		UpperCamelCase_    :     int = 255	,		UpperCamelCase_    :     int = 150	,		UpperCamelCase_    :     int = 0.1	,		UpperCamelCase_    :     float = 2.0	,		UpperCamelCase_    :     float = 5.0	,		UpperCamelCase_    :     float = 5.0	,		UpperCamelCase_    :     float = 0.5	,		UpperCamelCase_    :     float = 0.07	,		UpperCamelCase_    :     int = 12_544	,		UpperCamelCase_    :     float = 3.0	,		UpperCamelCase_    :     float = 0.75	,		UpperCamelCase_    :     float = 0.02	,		UpperCamelCase_    :     float = 1.0	,		UpperCamelCase_    :     float = 1e-05	,		UpperCamelCase_    :     bool = False	,		UpperCamelCase_    :     bool = True	,		UpperCamelCase_    :     bool = True	,		UpperCamelCase_    :     Optional[list] = [4, 8, 16, 32]	,		UpperCamelCase_    :     int = 77	,		UpperCamelCase_    :     int = 256	,		UpperCamelCase_    :     int = 77	,		UpperCamelCase_    :     int = 6	,		UpperCamelCase_    :     int = 49_408	,		UpperCamelCase_    :     int = 2	,		UpperCamelCase_    :     int = 16	,		UpperCamelCase_    :     int = 256	,		UpperCamelCase_    :     int = 256	,		UpperCamelCase_    :     int = 256	,		UpperCamelCase_    :     int = 1_024	,		UpperCamelCase_    :     str = "GN"	,		UpperCamelCase_    :     int = 6	,		UpperCamelCase_    :     int = 10	,		UpperCamelCase_    :     bool = True	,		UpperCamelCase_    :     int = 8	,		UpperCamelCase_    :     float = 0.1	,		UpperCamelCase_    :     int = 2_048	,		UpperCamelCase_    :     bool = False	,		UpperCamelCase_    :     bool = False	,		UpperCamelCase_    :     int = 2	,		UpperCamelCase_    :     int = 4	,		**UpperCamelCase_    :     Union[str, Any]	,		):


          """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=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=lowerCAmelCase__	,		out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]	,		)
          elif isinstance(lowerCAmelCase__	,		lowerCAmelCase__			):
               __A             =		backbone_config.get("""model_type"""			)
               __A             =		CONFIG_MAPPING[backbone_model_type]
               __A             =		config_class.from_dict(lowerCAmelCase__			)

          __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__(**lowerCAmelCase__			)





     def 						lowerCAmelCase_       (			self    :     Dict			):


          """simple docstring"""



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












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     list[int]      )  -> list[int]:





    """simple docstring"""

    if len(__lowercase      ) > 1:
        __A             =		len(__lowercase      ) // 2  # Finds the middle of the array
        __A             =		arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        __A             =		arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        __A             =		len(__lowercase      )
        __A             =		len(__lowercase      )
        merge(__lowercase      )  # Starts sorting the left.
        merge(__lowercase      )  # Starts sorting the right
        __A             =		0  # Left Counter
        __A             =		0  # Right Counter
        __A             =		0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                __A             =		left_array[left_index]
                left_index += 1
            else:
                __A             =		right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            __A             =		left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            __A             =		right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
  import doctest

  doctest.testmod()




