






import copy
from typing import Dict, Optional

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


lowerCAmelCase_     =			logging.get_logger(__name__)

lowerCAmelCase_     =			{
    '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   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = "oneformer"
		snake_case			 = {"hidden_size": "hidden_dim"}
		def __init__(	self		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							Optional[Dict] = None    ,_SCREAMING_SNAKE_CASE		:							int = 2_5_5    ,_SCREAMING_SNAKE_CASE		:							int = 1_5_0    ,_SCREAMING_SNAKE_CASE		:							int = 0.1    ,_SCREAMING_SNAKE_CASE		:							float = 2.0    ,_SCREAMING_SNAKE_CASE		:							float = 5.0    ,_SCREAMING_SNAKE_CASE		:							float = 5.0    ,_SCREAMING_SNAKE_CASE		:							float = 0.5    ,_SCREAMING_SNAKE_CASE		:							float = 0.07    ,_SCREAMING_SNAKE_CASE		:							int = 1_2_5_4_4    ,_SCREAMING_SNAKE_CASE		:							float = 3.0    ,_SCREAMING_SNAKE_CASE		:							float = 0.75    ,_SCREAMING_SNAKE_CASE		:							float = 0.02    ,_SCREAMING_SNAKE_CASE		:							float = 1.0    ,_SCREAMING_SNAKE_CASE		:							float = 1E-05    ,_SCREAMING_SNAKE_CASE		:							bool = False    ,_SCREAMING_SNAKE_CASE		:							bool = True    ,_SCREAMING_SNAKE_CASE		:							bool = True    ,_SCREAMING_SNAKE_CASE		:							Optional[list] = [4, 8, 1_6, 3_2]    ,_SCREAMING_SNAKE_CASE		:							int = 7_7    ,_SCREAMING_SNAKE_CASE		:							int = 2_5_6    ,_SCREAMING_SNAKE_CASE		:							int = 7_7    ,_SCREAMING_SNAKE_CASE		:							int = 6    ,_SCREAMING_SNAKE_CASE		:							int = 4_9_4_0_8    ,_SCREAMING_SNAKE_CASE		:							int = 2    ,_SCREAMING_SNAKE_CASE		:							int = 1_6    ,_SCREAMING_SNAKE_CASE		:							int = 2_5_6    ,_SCREAMING_SNAKE_CASE		:							int = 2_5_6    ,_SCREAMING_SNAKE_CASE		:							int = 2_5_6    ,_SCREAMING_SNAKE_CASE		:							int = 1_0_2_4    ,_SCREAMING_SNAKE_CASE		:							str = "GN"    ,_SCREAMING_SNAKE_CASE		:							int = 6    ,_SCREAMING_SNAKE_CASE		:							int = 1_0    ,_SCREAMING_SNAKE_CASE		:							bool = True    ,_SCREAMING_SNAKE_CASE		:							int = 8    ,_SCREAMING_SNAKE_CASE		:							float = 0.1    ,_SCREAMING_SNAKE_CASE		:							int = 2_0_4_8    ,_SCREAMING_SNAKE_CASE		:							bool = False    ,_SCREAMING_SNAKE_CASE		:							bool = False    ,_SCREAMING_SNAKE_CASE		:							int = 2    ,_SCREAMING_SNAKE_CASE		:							int = 4    ,**_SCREAMING_SNAKE_CASE		:							str    ,)     -> List[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_2_4    ,in_channels=3    ,patch_size=4    ,embed_dim=9_6    ,depths=[2, 2, 6, 2]    ,num_heads=[3, 6, 1_2, 2_4]    ,window_size=7    ,drop_path_rate=0.3    ,use_absolute_embeddings=_SCREAMING_SNAKE_CASE    ,out_features=['stage1', 'stage2', 'stage3', 'stage4']    ,)
						elif isinstance(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		):
										A									=   backbone_config.get('model_type'		)
										A									=   CONFIG_MAPPING[backbone_model_type]
										A									=   config_class.from_dict(_SCREAMING_SNAKE_CASE		)

						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__(**_SCREAMING_SNAKE_CASE		)







		def    A(	self		:							Tuple		)     -> Dict[str, any]:


						'''simple docstring'''
						A									=   copy.deepcopy(self.__dict__		)
						A									=   self.backbone_config.to_dict()
						A									=   self.__class__.model_type
						return output













def   snake_case		(				UpperCAmelCase						:							Optional[int],    UpperCAmelCase						:							str ):
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				A									=   (boundary[1] - boundary[0]) / steps
				A									=   boundary[0]
				A									=   boundary[1]
				A									=   make_points(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase )
				A									=   0.0
				y += (h / 3.0) * f(UpperCAmelCase )
				A									=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(UpperCAmelCase )
								cnt += 1
				y += (h / 3.0) * f(UpperCAmelCase )
				return y







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							int,    UpperCAmelCase						:							List[Any] ):
				A									=   a + h
				while x < (b - h):
								yield x
								A									=   x + h







def   snake_case		(				UpperCAmelCase						:							str ):  # enter your function here
				A									=   (x - 0) * (x - 0)
				return y







def   snake_case		(				):
				A									=   0.0  # Lower bound of integration
				A									=   1.0  # Upper bound of integration
				A									=   10.0  # define number of steps or resolution
				A									=   [a, b]  # define boundary of integration
				A									=   method_a(UpperCAmelCase,    UpperCAmelCase )
				print(f'y = {y}' )


if __name__ == "__main__":
					main()





