




import copy
from typing import Dict, Optional

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


lowercase_			:       int						       =				logging.get_logger(__name__)

lowercase_			:       List[str]						       =				{
    '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 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				Any				  =  "oneformer"
			snake_case_   :				str				  =  {"hidden_size": "hidden_dim"}







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




									"""simple docstring"""


									if backbone_config is None:
															logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."   )
															_UpperCAmelCase									=  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=snake_case__					,					out_features=["stage1", "stage2", "stage3", "stage4"]					,					)
									elif isinstance(snake_case__					,					snake_case__   ):
															_UpperCAmelCase									=  backbone_config.get("model_type"   )
															_UpperCAmelCase									=  CONFIG_MAPPING[backbone_model_type]
															_UpperCAmelCase									=  config_class.from_dict(snake_case__   )

									_UpperCAmelCase									=  backbone_config

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

									super().__init__(**snake_case__   )







			def UpperCamelCase    ( self					:    Optional[Any]   ):




									"""simple docstring"""


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










from math import exp


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						return 8 * x - 2 * exp(-x		)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  lower_bound
						_UpperCAmelCase									=  upper_bound
						for _ in range(0			,					snake_case_		):
												_UpperCAmelCase							,				_UpperCAmelCase									=  xa, xa - (f(snake_case_		) * (xa - xa)) / (f(snake_case_		) - f(snake_case_		))
						return xa


if __name__ == "__main__":
				print(f"""Example: {secant_method(1, 3, 2)}""")




