




'''simple docstring'''




import copy
from typing import Dict, Optional

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


snake_case__      				=  logging.get_logger(__name__)

snake_case__      				=  {
    """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_   (a__   ):







						"""simple docstring"""


						_lowerCAmelCase			  =    "oneformer"
						_lowerCAmelCase			  =    {"hidden_size": "hidden_dim"}







						def __init__(   self					:				List[str]							,       _lowerCamelCase					:				Union[str, Any] = None							,       _lowerCamelCase					:				List[Any] = 255							,       _lowerCamelCase					:				Any = 150							,       _lowerCamelCase					:				str = 0.1							,       _lowerCamelCase					:				Any = 2.0							,       _lowerCamelCase					:				int = 5.0							,       _lowerCamelCase					:				List[str] = 5.0							,       _lowerCamelCase					:				str = 0.5							,       _lowerCamelCase					:				List[str] = 0.07							,       _lowerCamelCase					:				str = 12544							,       _lowerCamelCase					:				int = 3.0							,       _lowerCamelCase					:				Tuple = 0.75							,       _lowerCamelCase					:				Tuple = 0.02							,       _lowerCamelCase					:				Optional[int] = 1.0							,       _lowerCamelCase					:				List[Any] = 1E-05							,       _lowerCamelCase					:				str = False							,       _lowerCamelCase					:				Any = True							,       _lowerCamelCase					:				List[Any] = True							,       _lowerCamelCase					:				Optional[int] = [4, 8, 16, 32]							,       _lowerCamelCase					:				Any = 77							,       _lowerCamelCase					:				List[str] = 256							,       _lowerCamelCase					:				Union[str, Any] = 77							,       _lowerCamelCase					:				List[Any] = 6							,       _lowerCamelCase					:				Union[str, Any] = 49408							,       _lowerCamelCase					:				Optional[Any] = 2							,       _lowerCamelCase					:				Optional[Any] = 16							,       _lowerCamelCase					:				Optional[int] = 256							,       _lowerCamelCase					:				List[str] = 256							,       _lowerCamelCase					:				str = 256							,       _lowerCamelCase					:				str = 1024							,       _lowerCamelCase					:				Tuple = "GN"							,       _lowerCamelCase					:				Optional[Any] = 6							,       _lowerCamelCase					:				int = 10							,       _lowerCamelCase					:				str = True							,       _lowerCamelCase					:				int = 8							,       _lowerCamelCase					:				int = 0.1							,       _lowerCamelCase					:				int = 2048							,       _lowerCamelCase					:				Optional[Any] = False							,       _lowerCamelCase					:				Dict = False							,       _lowerCamelCase					:				List[Any] = 2							,       _lowerCamelCase					:				str = 4							,       **_lowerCamelCase					:				Tuple							,       ):





											"""simple docstring"""







											if backbone_config is None:
																logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''			)
																A_       :   Any       =							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=lowercase__							,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							,       )
											elif isinstance(lowercase__							,       lowercase__			):
																A_       :   Union[str, Any]       =							backbone_config.get('''model_type'''			)
																A_       :   Optional[Any]       =							CONFIG_MAPPING[backbone_model_type]
																A_       :   List[Any]       =							config_class.from_dict(lowercase__			)

											A_       :   List[str]       =							backbone_config

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

											super().__init__(**lowercase__			)







						def 					_a			(   self					:				List[str]			):





											"""simple docstring"""







											A_       :   Tuple       =							copy.deepcopy(self.__dict__			)
											A_       :   List[str]       =							self.backbone_config.to_dict()
											A_       :   Union[str, Any]       =							self.__class__.model_type
											return output





"""simple docstring"""




from __future__ import annotations

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	m - 1, 0
					while i >= 0 and j < n:
										if key == mat[i][j]:
															print(f'''Key {key} found at row- {i + 1} column- {j + 1}'''			)
															return
										if key < mat[i][j]:
															i -= 1
										else:
															j += 1
					print(f'''Key {key} not found'''			)

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE				=	[[2, 5, 7], [4, 8, 1_3], [9, 1_1, 1_5], [1_2, 1_7, 2_0]]
					SCREAMING_SNAKE_CASE				=	int(input('Enter the element to be searched:'			)			)
					print(SCREAMING_SNAKE_CASE_			)
					search_in_a_sorted_matrix(SCREAMING_SNAKE_CASE_,     len(SCREAMING_SNAKE_CASE_			),     len(mat[0]			),     SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()
