



'''simple docstring'''





import copy
from typing import Dict, Optional

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


_UpperCamelCase							    =						logging.get_logger(__name__)

_UpperCamelCase							    =						{
    '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_   (    SCREAMING_SNAKE_CASE_					):
  """simple docstring"""




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




  def __init__(			self				:     Optional[int]      ,     _a				:     Optional[Dict] = None      ,     _a				:     int = 255      ,     _a				:     int = 150      ,     _a				:     int = 0.1      ,     _a				:     float = 2.0      ,     _a				:     float = 5.0      ,     _a				:     float = 5.0      ,     _a				:     float = 0.5      ,     _a				:     float = 0.07      ,     _a				:     int = 1_2544      ,     _a				:     float = 3.0      ,     _a				:     float = 0.75      ,     _a				:     float = 0.02      ,     _a				:     float = 1.0      ,     _a				:     float = 1e-05      ,     _a				:     bool = False      ,     _a				:     bool = True      ,     _a				:     bool = True      ,     _a				:     Optional[list] = [4, 8, 16, 32]      ,     _a				:     int = 77      ,     _a				:     int = 256      ,     _a				:     int = 77      ,     _a				:     int = 6      ,     _a				:     int = 4_9408      ,     _a				:     int = 2      ,     _a				:     int = 16      ,     _a				:     int = 256      ,     _a				:     int = 256      ,     _a				:     int = 256      ,     _a				:     int = 1024      ,     _a				:     str = "GN"      ,     _a				:     int = 6      ,     _a				:     int = 10      ,     _a				:     bool = True      ,     _a				:     int = 8      ,     _a				:     float = 0.1      ,     _a				:     int = 2048      ,     _a				:     bool = False      ,     _a				:     bool = False      ,     _a				:     int = 2      ,     _a				:     int = 4      ,     **_a				:     Any      ,     ) -> Optional[Any]:
    if backbone_config is None:
      logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
      __lowerCamelCase	:	int   	=			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=_a      ,     out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,     )
    elif isinstance(_a      ,     _a       ):
      __lowerCamelCase	:	str   	=			backbone_config.get('model_type'       )
      __lowerCamelCase	:	Dict   	=			CONFIG_MAPPING[backbone_model_type]
      __lowerCamelCase	:	Dict   	=			config_class.from_dict(_a       )

    __lowerCamelCase	:	Optional[Any]   	=			backbone_config

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

    super().__init__(**_a       )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> Dict[str, any]:
    __lowerCamelCase	:	List[str]   	=			copy.deepcopy(self.__dict__       )
    __lowerCamelCase	:	str   	=			self.backbone_config.to_dict()
    __lowerCamelCase	:	Tuple   	=			self.__class__.model_type
    return output











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase							    =						{
    'configuration_imagegpt': ['IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ImageGPTConfig', 'ImageGPTOnnxConfig']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						['ImageGPTFeatureExtractor']
   _UpperCamelCase							    =						['ImageGPTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase							    =						[
       'IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ImageGPTForCausalImageModeling',
       'ImageGPTForImageClassification',
       'ImageGPTModel',
       'ImageGPTPreTrainedModel',
       'load_tf_weights_in_imagegpt',
   ]


if TYPE_CHECKING:
   from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
      from .image_processing_imagegpt import ImageGPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_imagegpt import (
          IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ImageGPTForCausalImageModeling,
          ImageGPTForImageClassification,
          ImageGPTModel,
          ImageGPTPreTrainedModel,
          load_tf_weights_in_imagegpt,
      )

else:
   import sys

   _UpperCamelCase							    =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






