


import copy
from typing import Dict, Optional

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


A :	Any												=     logging.get_logger(__name__)

A :	Any												=     {
    '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 							A					( UpperCAmelCase__							):
					'''simple docstring'''






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

					def __init__(self					:		Tuple   ,      _UpperCAmelCase					:		Any = None   ,      _UpperCAmelCase					:		Union[str, Any] = 255   ,      _UpperCAmelCase					:		Optional[int] = 150   ,      _UpperCAmelCase					:		Any = 0.1   ,      _UpperCAmelCase					:		str = 2.0   ,      _UpperCAmelCase					:		List[Any] = 5.0   ,      _UpperCAmelCase					:		Optional[Any] = 5.0   ,      _UpperCAmelCase					:		Optional[int] = 0.5   ,      _UpperCAmelCase					:		str = 0.07   ,      _UpperCAmelCase					:		str = 1_2544   ,      _UpperCAmelCase					:		Any = 3.0   ,      _UpperCAmelCase					:		Optional[int] = 0.75   ,      _UpperCAmelCase					:		List[Any] = 0.02   ,      _UpperCAmelCase					:		int = 1.0   ,      _UpperCAmelCase					:		Dict = 1E-0_5   ,      _UpperCAmelCase					:		Dict = False   ,      _UpperCAmelCase					:		List[Any] = True   ,      _UpperCAmelCase					:		Union[str, Any] = True   ,      _UpperCAmelCase					:		int = [4, 8, 16, 32]   ,      _UpperCAmelCase					:		Optional[Any] = 77   ,      _UpperCAmelCase					:		Union[str, Any] = 256   ,      _UpperCAmelCase					:		Union[str, Any] = 77   ,      _UpperCAmelCase					:		List[str] = 6   ,      _UpperCAmelCase					:		List[Any] = 4_9408   ,      _UpperCAmelCase					:		Tuple = 2   ,      _UpperCAmelCase					:		int = 16   ,      _UpperCAmelCase					:		Optional[Any] = 256   ,      _UpperCAmelCase					:		Tuple = 256   ,      _UpperCAmelCase					:		str = 256   ,      _UpperCAmelCase					:		Dict = 1024   ,      _UpperCAmelCase					:		int = "GN"   ,      _UpperCAmelCase					:		str = 6   ,      _UpperCAmelCase					:		List[str] = 10   ,      _UpperCAmelCase					:		int = True   ,      _UpperCAmelCase					:		Optional[int] = 8   ,      _UpperCAmelCase					:		Any = 0.1   ,      _UpperCAmelCase					:		Dict = 2048   ,      _UpperCAmelCase					:		Optional[Any] = False   ,      _UpperCAmelCase					:		Dict = False   ,      _UpperCAmelCase					:		Optional[Any] = 2   ,      _UpperCAmelCase					:		Dict = 4   ,      **_UpperCAmelCase					:		str   ,      )		->     str:




							"""simple docstring"""


							if backbone_config is None:
									logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""	)
									lowercase__      =	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	):
									lowercase__      =	backbone_config.get("""model_type"""	)
									lowercase__      =	CONFIG_MAPPING[backbone_model_type]
									lowercase__      =	config_class.from_dict(_UpperCAmelCase	)

							lowercase__      =	backbone_config

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

							super().__init__(**_UpperCAmelCase	)


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




							"""simple docstring"""


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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


A :	int												=     {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	List[Any]												=     [
	    'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RwkvForCausalLM',
	    'RwkvModel',
	    'RwkvPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_rwkv import (
		    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RwkvForCausalLM,
		    RwkvModel,
		    RwkvPreTrainedModel,
		)
else:
	import sys

	A :	Optional[int]												=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

