import copy
from typing import Dict, Optional

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


_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)

_SCREAMING_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 SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							"""oneformer"""
   __lowerCAmelCase											=							{"""hidden_size""": """hidden_dim"""}







   def __init__(    self	:	Optional[int]      ,      lowerCamelCase_	:	Optional[Dict] = None      ,      lowerCamelCase_	:	int = 255      ,      lowerCamelCase_	:	int = 150      ,      lowerCamelCase_	:	int = 0.1      ,      lowerCamelCase_	:	float = 2.0      ,      lowerCamelCase_	:	float = 5.0      ,      lowerCamelCase_	:	float = 5.0      ,      lowerCamelCase_	:	float = 0.5      ,      lowerCamelCase_	:	float = 0.0_7      ,      lowerCamelCase_	:	int = 1_2544      ,      lowerCamelCase_	:	float = 3.0      ,      lowerCamelCase_	:	float = 0.7_5      ,      lowerCamelCase_	:	float = 0.0_2      ,      lowerCamelCase_	:	float = 1.0      ,      lowerCamelCase_	:	float = 1E-05      ,      lowerCamelCase_	:	bool = False      ,      lowerCamelCase_	:	bool = True      ,      lowerCamelCase_	:	bool = True      ,      lowerCamelCase_	:	Optional[list] = [4, 8, 16, 32]      ,      lowerCamelCase_	:	int = 77      ,      lowerCamelCase_	:	int = 256      ,      lowerCamelCase_	:	int = 77      ,      lowerCamelCase_	:	int = 6      ,      lowerCamelCase_	:	int = 4_9408      ,      lowerCamelCase_	:	int = 2      ,      lowerCamelCase_	:	int = 16      ,      lowerCamelCase_	:	int = 256      ,      lowerCamelCase_	:	int = 256      ,      lowerCamelCase_	:	int = 256      ,      lowerCamelCase_	:	int = 1024      ,      lowerCamelCase_	:	str = "GN"      ,      lowerCamelCase_	:	int = 6      ,      lowerCamelCase_	:	int = 10      ,      lowerCamelCase_	:	bool = True      ,      lowerCamelCase_	:	int = 8      ,      lowerCamelCase_	:	float = 0.1      ,      lowerCamelCase_	:	int = 2048      ,      lowerCamelCase_	:	bool = False      ,      lowerCamelCase_	:	bool = False      ,      lowerCamelCase_	:	int = 2      ,      lowerCamelCase_	:	int = 4      ,      **lowerCamelCase_	:	str      ,      ):





    """simple docstring"""


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

    UpperCamelCase      =			backbone_config

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

    super().__init__(**lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	List[Any]							):





    """simple docstring"""


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

from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   		=			{
    """configuration_align""": [
        """ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AlignConfig""",
        """AlignTextConfig""",
        """AlignVisionConfig""",
    ],
    """processing_align""": ["""AlignProcessor"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   		=			[
         """ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AlignModel""",
         """AlignPreTrainedModel""",
         """AlignTextModel""",
         """AlignVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

     _SCREAMING_SNAKE_CASE   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
