

"""simple docstring"""






import copy
from typing import Dict, Optional

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


lowerCamelCase__			:					List[str]							  =					logging.get_logger(__name__)

lowerCamelCase__			:					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       _UpperCAmelCase     (						lowercase__):
			__a	:    List[str]			   =							'''oneformer'''
			__a	:    List[str]			   =							{'''hidden_size''': '''hidden_dim'''}
			def __init__(						self ,						_A = None ,						_A = 2_55 ,						_A = 1_50 ,						_A = 0.1 ,						_A = 2.0 ,						_A = 5.0 ,						_A = 5.0 ,						_A = 0.5 ,						_A = 0.07 ,						_A = 1_25_44 ,						_A = 3.0 ,						_A = 0.75 ,						_A = 0.02 ,						_A = 1.0 ,						_A = 1e-05 ,						_A = False ,						_A = True ,						_A = True ,						_A = [4, 8, 16, 32] ,						_A = 77 ,						_A = 2_56 ,						_A = 77 ,						_A = 6 ,						_A = 4_94_08 ,						_A = 2 ,						_A = 16 ,						_A = 2_56 ,						_A = 2_56 ,						_A = 2_56 ,						_A = 10_24 ,						_A = "GN" ,						_A = 6 ,						_A = 10 ,						_A = True ,						_A = 8 ,						_A = 0.1 ,						_A = 20_48 ,						_A = False ,						_A = False ,						_A = 2 ,						_A = 4 ,						**_A ,						)      ->      Optional[Any]:






				'''simple docstring'''







				if backbone_config is None:
					logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""       )
					_UpperCAmelCase      :							List[Any]       =   CONFIG_MAPPING["""swin"""](
					    image_size=2_24 ,						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=lowerCAmelCase__ ,						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] ,						)
				elif isinstance(lowerCAmelCase__ ,						lowerCAmelCase__       ):
					_UpperCAmelCase      :							Any       =   backbone_config.get("""model_type"""       )
					_UpperCAmelCase      :							int       =   CONFIG_MAPPING[backbone_model_type]
					_UpperCAmelCase      :							int       =   config_class.from_dict(lowerCAmelCase__       )

				_UpperCAmelCase      :							List[Any]       =   backbone_config

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

				super().__init__(**lowerCAmelCase__       )
			def 							__snake_case	(						self       )      ->      Dict[str, any]:






				'''simple docstring'''







				_UpperCAmelCase      :							List[Any]       =   copy.deepcopy(self.__dict__       )
				_UpperCAmelCase      :							Dict       =   self.backbone_config.to_dict()
				_UpperCAmelCase      :							List[Any]       =   self.__class__.model_type
				return output








"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCamelCase__			:					Union[str, Any]							  =					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					List[str]							  =					[
						    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''UniSpeechSatForAudioFrameClassification''',
						    '''UniSpeechSatForCTC''',
						    '''UniSpeechSatForPreTraining''',
						    '''UniSpeechSatForSequenceClassification''',
						    '''UniSpeechSatForXVector''',
						    '''UniSpeechSatModel''',
						    '''UniSpeechSatPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_unispeech_sat import (
												    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    UniSpeechSatForAudioFrameClassification,
												    UniSpeechSatForCTC,
												    UniSpeechSatForPreTraining,
												    UniSpeechSatForSequenceClassification,
												    UniSpeechSatForXVector,
												    UniSpeechSatModel,
												    UniSpeechSatPreTrainedModel,
												)

else:
						import sys

						lowerCamelCase__			:					Any							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





