
"""simple docstring"""


import copy
from typing import Dict, Optional

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


A_						       =				logging.get_logger(__name__)

A_						       =				{
    """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     (   lowerCAmelCase ):
				a__:			Dict									=						"oneformer"
				a__:			List[Any]									=						{"hidden_size": "hidden_dim"}




				def __init__(							self       ,	UpperCAmelCase = None       ,	UpperCAmelCase = 255       ,	UpperCAmelCase = 150       ,	UpperCAmelCase = 0.1       ,	UpperCAmelCase = 2.0       ,	UpperCAmelCase = 5.0       ,	UpperCAmelCase = 5.0       ,	UpperCAmelCase = 0.5       ,	UpperCAmelCase = 0.0_7       ,	UpperCAmelCase = 1_2544       ,	UpperCAmelCase = 3.0       ,	UpperCAmelCase = 0.7_5       ,	UpperCAmelCase = 0.0_2       ,	UpperCAmelCase = 1.0       ,	UpperCAmelCase = 1e-0_5       ,	UpperCAmelCase = False       ,	UpperCAmelCase = True       ,	UpperCAmelCase = True       ,	UpperCAmelCase = [4, 8, 16, 32]       ,	UpperCAmelCase = 77       ,	UpperCAmelCase = 256       ,	UpperCAmelCase = 77       ,	UpperCAmelCase = 6       ,	UpperCAmelCase = 4_9408       ,	UpperCAmelCase = 2       ,	UpperCAmelCase = 16       ,	UpperCAmelCase = 256       ,	UpperCAmelCase = 256       ,	UpperCAmelCase = 256       ,	UpperCAmelCase = 1024       ,	UpperCAmelCase = "GN"       ,	UpperCAmelCase = 6       ,	UpperCAmelCase = 10       ,	UpperCAmelCase = True       ,	UpperCAmelCase = 8       ,	UpperCAmelCase = 0.1       ,	UpperCAmelCase = 2048       ,	UpperCAmelCase = False       ,	UpperCAmelCase = False       ,	UpperCAmelCase = 2       ,	UpperCAmelCase = 4       ,	**UpperCAmelCase       ,	):






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

							lowerCamelCase_ 		=     backbone_config

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

							super().__init__(**UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     copy.deepcopy(self.__dict__      )
							lowerCamelCase_ 		=     self.backbone_config.to_dict()
							lowerCamelCase_ 		=     self.__class__.model_type
							return output





"""simple docstring"""


import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


A_						       =				logging.get_logger(__name__)




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			List[str]									=						'hybrid-clip'
				a__:			List[Any]									=						True




				def __init__(							self       ,	UpperCAmelCase=512       ,	**UpperCAmelCase      ):
							super().__init__(**UpperCAmelCase      )

							if "text_config" not in kwargs:
										raise ValueError('''`text_config` can not be `None`.'''      )

							if "vision_config" not in kwargs:
										raise ValueError('''`vision_config` can not be `None`.'''      )

							lowerCamelCase_ 		=     kwargs.pop('''text_config'''      )
							lowerCamelCase_ 		=     kwargs.pop('''vision_config'''      )

							lowerCamelCase_ 		=     text_config.pop('''model_type'''      )
							lowerCamelCase_ 		=     vision_config.pop('''model_type'''      )

							from transformers import AutoConfig

							lowerCamelCase_ 		=     AutoConfig.for_model(UpperCAmelCase       ,	**UpperCAmelCase      )

							if vision_model_type == "clip":
										lowerCamelCase_ 		=     AutoConfig.for_model(UpperCAmelCase       ,	**UpperCAmelCase      ).vision_config
							elif vision_model_type == "clip_vision_model":
										from transformers import CLIPVisionConfig

										lowerCamelCase_ 		=     CLIPVisionConfig(**UpperCAmelCase      )
							else:
										lowerCamelCase_ 		=     AutoConfig.for_model(UpperCAmelCase       ,	**UpperCAmelCase      )

							lowerCamelCase_ 		=     projection_dim
							lowerCamelCase_ 		=     1.0




				@classmethod
				def 					UpperCAmelCase__	(							cls       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	**UpperCAmelCase      ):
							return cls(text_config=text_config.to_dict()       ,	vision_config=vision_config.to_dict()       ,	**UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     copy.deepcopy(self.__dict__      )
							lowerCamelCase_ 		=     self.text_config.to_dict()
							lowerCamelCase_ 		=     self.vision_config.to_dict()
							lowerCamelCase_ 		=     self.__class__.model_type
							return output



