



'''simple docstring'''




import copy
from typing import Dict, Optional

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


a  :     List[Any]       =       logging.get_logger(__name__)

a  :     Dict       =       {
    """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_					(				__magic_name__     ):
	lowercase		=					'oneformer'
	lowercase		=					{'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 = 0.0_7						,						A = 12544						,						A = 3.0						,						A = 0.7_5						,						A = 0.0_2						,						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						,						)     ->     Optional[int]:
								if backbone_config is None:
															logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""       )
															UpperCAmelCase	:						int					=						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       ):
															UpperCAmelCase	:						Optional[int]					=						backbone_config.get("""model_type"""       )
															UpperCAmelCase	:						Optional[int]					=						CONFIG_MAPPING[backbone_model_type]
															UpperCAmelCase	:						int					=						config_class.from_dict(A       )

								UpperCAmelCase	:						int					=						backbone_config

								UpperCAmelCase	:						List[str]					=						ignore_value
								UpperCAmelCase	:						Optional[int]					=						num_queries
								UpperCAmelCase	:						Any					=						no_object_weight
								UpperCAmelCase	:						Union[str, Any]					=						class_weight
								UpperCAmelCase	:						Optional[Any]					=						mask_weight
								UpperCAmelCase	:						str					=						dice_weight
								UpperCAmelCase	:						Optional[int]					=						contrastive_weight
								UpperCAmelCase	:						Any					=						contrastive_temperature
								UpperCAmelCase	:						Any					=						train_num_points
								UpperCAmelCase	:						Tuple					=						oversample_ratio
								UpperCAmelCase	:						int					=						importance_sample_ratio
								UpperCAmelCase	:						List[Any]					=						init_std
								UpperCAmelCase	:						Any					=						init_xavier_std
								UpperCAmelCase	:						str					=						layer_norm_eps
								UpperCAmelCase	:						List[str]					=						is_training
								UpperCAmelCase	:						List[str]					=						use_auxiliary_loss
								UpperCAmelCase	:						List[str]					=						output_auxiliary_logits
								UpperCAmelCase	:						List[Any]					=						strides
								UpperCAmelCase	:						List[Any]					=						task_seq_len
								UpperCAmelCase	:						Optional[Any]					=						text_encoder_width
								UpperCAmelCase	:						Optional[Any]					=						text_encoder_context_length
								UpperCAmelCase	:						List[Any]					=						text_encoder_num_layers
								UpperCAmelCase	:						List[Any]					=						text_encoder_vocab_size
								UpperCAmelCase	:						Dict					=						text_encoder_proj_layers
								UpperCAmelCase	:						Dict					=						text_encoder_n_ctx
								UpperCAmelCase	:						Union[str, Any]					=						conv_dim
								UpperCAmelCase	:						List[str]					=						mask_dim
								UpperCAmelCase	:						List[str]					=						hidden_dim
								UpperCAmelCase	:						Dict					=						encoder_feedforward_dim
								UpperCAmelCase	:						Any					=						norm
								UpperCAmelCase	:						Any					=						encoder_layers
								UpperCAmelCase	:						List[str]					=						decoder_layers
								UpperCAmelCase	:						Union[str, Any]					=						use_task_norm
								UpperCAmelCase	:						int					=						num_attention_heads
								UpperCAmelCase	:						Any					=						dropout
								UpperCAmelCase	:						Dict					=						dim_feedforward
								UpperCAmelCase	:						Tuple					=						pre_norm
								UpperCAmelCase	:						int					=						enforce_input_proj
								UpperCAmelCase	:						Any					=						query_dec_layers
								UpperCAmelCase	:						Optional[int]					=						common_stride
								UpperCAmelCase	:						Optional[Any]					=						decoder_layers

								super().__init__(**A       )






	def 					_lowercase(    self       )     ->     Dict[str, any]:
								UpperCAmelCase	:						List[Any]					=						copy.deepcopy(self.__dict__       )
								UpperCAmelCase	:						Any					=						self.backbone_config.to_dict()
								UpperCAmelCase	:						str					=						self.__class__.model_type
								return output






'''simple docstring'''




class      UpperCamelCase_					:





	def __init__(    self       )     ->     None:
								UpperCAmelCase	:						int					=						[0, 1]






	def 					_lowercase(    self						,						A       )     ->     list:
								if (difference := index - (len(self.sequence       ) - 2)) >= 1:
															for _ in range(A       ):
																						self.sequence.append(self.sequence[-1] + self.sequence[-2]       )
								return self.sequence[:index]




def  __lowerCamelCase    (   ) ->     None:
							print(
							    """Fibonacci Series Using Dynamic Programming\n"""      ,							"""Enter the index of the Fibonacci number you want to calculate """      ,							"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""      ,							sep=""""""      ,							)
							UpperCAmelCase	:						Tuple					=						Fibonacci()

							while True:
														UpperCAmelCase	:						str					=						input(""">> """						)
														if prompt in {"exit", "quit"}:
																					break

														try:
																					UpperCAmelCase	:						int					=						int(_lowercase						)
														except ValueError:
																					print("""Enter a number or 'exit'"""						)
																					continue

														print(fibonacci.get(_lowercase						)						)


if __name__ == "__main__":
							main()

