






'''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 a_     (     UpperCamelCase_				):
   _snake_case	   =				"""oneformer"""
   _snake_case	   =				{"""hidden_size""": """hidden_dim"""}







   def __init__(self       , __a = None       , __a = 2_5_5       , __a = 1_5_0       , __a = 0.1       , __a = 2.0       , __a = 5.0       , __a = 5.0       , __a = 0.5       , __a = 0.07       , __a = 1_2_5_4_4       , __a = 3.0       , __a = 0.75       , __a = 0.02       , __a = 1.0       , __a = 1E-05       , __a = False       , __a = True       , __a = True       , __a = [4, 8, 1_6, 3_2]       , __a = 7_7       , __a = 2_5_6       , __a = 7_7       , __a = 6       , __a = 4_9_4_0_8       , __a = 2       , __a = 1_6       , __a = 2_5_6       , __a = 2_5_6       , __a = 2_5_6       , __a = 1_0_2_4       , __a = "GN"       , __a = 6       , __a = 1_0       , __a = True       , __a = 8       , __a = 0.1       , __a = 2_0_4_8       , __a = False       , __a = False       , __a = 2       , __a = 4       , **__a       , )      ->   List[str]:
      """simple docstring"""






      if backbone_config is None:
         logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.')
         __snake_case					:   Optional[Any]  				=			CONFIG_MAPPING['swin'](
             image_size=2_2_4       , in_channels=3       , patch_size=4       , embed_dim=9_6       , depths=[2, 2, 6, 2]       , num_heads=[3, 6, 1_2, 2_4]       , window_size=7       , drop_path_rate=0.3       , use_absolute_embeddings=__a       , out_features=['stage1', 'stage2', 'stage3', 'stage4']       , )
      elif isinstance(__a       , __a):
         __snake_case					:   int  				=			backbone_config.get('model_type')
         __snake_case					:   str  				=			CONFIG_MAPPING[backbone_model_type]
         __snake_case					:   Optional[int]  				=			config_class.from_dict(__a)

      __snake_case					:   List[Any]  				=			backbone_config

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

      super().__init__(**__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict[str, any]:
      """simple docstring"""






      __snake_case					:   Dict  				=			copy.deepcopy(self.__dict__)
      __snake_case					:   str  				=			self.backbone_config.to_dict()
      __snake_case					:   Optional[int]  				=			self.__class__.model_type
      return output







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A     						= {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

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