

'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Any  		=  """switch_transformers"""
     UpperCAmelCase_						:     Any  		=  ["""past_key_values"""]
     UpperCAmelCase_						:     str  		=  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}


     def __init__(		self			:					str					,      lowercase_			:					Union[str, Any]=32_128					,      lowercase_			:					str=768					,      lowercase_			:					Any=64					,      lowercase_			:					Optional[Any]=2_048					,      lowercase_			:					Union[str, Any]=64					,      lowercase_			:					Dict=12					,      lowercase_			:					Union[str, Any]=3					,      lowercase_			:					int=12					,      lowercase_			:					Union[str, Any]=3					,      lowercase_			:					List[Any]=12					,      lowercase_			:					Tuple=8					,      lowercase_			:					Optional[int]=False					,      lowercase_			:					Optional[Any]=0.01					,      lowercase_			:					List[str]="float32"					,      lowercase_			:					Tuple=False					,      lowercase_			:					Optional[int]=32					,      lowercase_			:					Optional[Any]=128					,      lowercase_			:					int=0.1					,      lowercase_			:					Dict=1E-6					,      lowercase_			:					Tuple=0.001					,      lowercase_			:					List[str]=0.001					,      lowercase_			:					str=1.0					,      lowercase_			:					Dict="relu"					,      lowercase_			:					str=True					,      lowercase_			:					Optional[Any]=False					,      lowercase_			:					Dict=True					,      lowercase_			:					Union[str, Any]=0					,      lowercase_			:					Optional[int]=1					,      **lowercase_			:					str					,      )						->     Any:
      UpperCAmelCase	:     str						 =			vocab_size
      UpperCAmelCase	:     Dict						 =			d_model
      UpperCAmelCase	:     Optional[int]						 =			d_kv
      UpperCAmelCase	:     str						 =			d_ff

      UpperCAmelCase	:     Tuple						 =			num_sparse_encoder_layers

      UpperCAmelCase	:     int						 =			num_layers
      UpperCAmelCase	:     Any						 =			(
          num_decoder_layers if num_decoder_layers is not None else self.num_layers
      )  # default = symmetry
      UpperCAmelCase	:     str						 =			num_sparse_decoder_layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_encoder_layers > 0:
       UpperCAmelCase	:     Optional[int]						 =			self.num_layers // self.num_sparse_encoder_layers
      else:
       UpperCAmelCase	:     int						 =			self.num_layers  # HACK: this will create 0 sparse layers

      # This tells us, each how many encoder layer we'll have to set a sparse layer.
      if self.num_sparse_decoder_layers > 0:
       UpperCAmelCase	:     str						 =			self.num_decoder_layers // self.num_sparse_decoder_layers
      else:
       UpperCAmelCase	:     Union[str, Any]						 =			self.num_decoder_layers  # HACK: this will create 0 sparse layers

      UpperCAmelCase	:     List[str]						 =			num_heads
      UpperCAmelCase	:     str						 =			num_experts
      UpperCAmelCase	:     Optional[int]						 =			expert_capacity
      UpperCAmelCase	:     Tuple						 =			router_bias
      UpperCAmelCase	:     Dict						 =			router_jitter_noise
      if router_dtype not in ["float32", "float16", "bfloat16"]:
       raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""	)
      UpperCAmelCase	:     Optional[Any]						 =			router_dtype

      UpperCAmelCase	:     int						 =			router_ignore_padding_tokens
      UpperCAmelCase	:     List[Any]						 =			relative_attention_num_buckets
      UpperCAmelCase	:     Optional[int]						 =			relative_attention_max_distance

      UpperCAmelCase	:     Optional[int]						 =			dropout_rate
      UpperCAmelCase	:     Any						 =			layer_norm_epsilon
      UpperCAmelCase	:     Optional[int]						 =			initializer_factor
      UpperCAmelCase	:     List[Any]						 =			feed_forward_proj
      UpperCAmelCase	:     List[str]						 =			use_cache
      UpperCAmelCase	:     Any						 =			add_router_probs

      UpperCAmelCase	:     Tuple						 =			router_z_loss_coef
      UpperCAmelCase	:     Tuple						 =			router_aux_loss_coef

      UpperCAmelCase	:     Optional[Any]						 =			self.feed_forward_proj.split('-'	)
      UpperCAmelCase	:     Optional[Any]						 =			act_info[-1]
      UpperCAmelCase	:     Tuple						 =			act_info[0] == 'gated'

      if len(lowercase_	) > 1 and act_info[0] != "gated" or len(lowercase_	) > 2:
       raise ValueError(
           f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
           'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
           '\'gated-gelu\' or \'relu\''	)

      # for backwards compatibility
      if feed_forward_proj == "gated-gelu":
       UpperCAmelCase	:     int						 =			'gelu_new'

      super().__init__(
          pad_token_id=lowercase_					,      eos_token_id=lowercase_					,      is_encoder_decoder=lowercase_					,      **lowercase_					,      )







'''simple docstring'''

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowercase__         =      {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowercase__         =      logging.get_logger(__name__)




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Any  		=  """maskformer"""
     UpperCAmelCase_						:     Union[str, Any]  		=  {"""hidden_size""": """mask_feature_size"""}
     UpperCAmelCase_						:     Union[str, Any]  		=  ["""resnet""", """swin"""]
     UpperCAmelCase_						:     Optional[int]  		=  ["""detr"""]


     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					int = 256					,      lowercase_			:					int = 256					,      lowercase_			:					float = 0.1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[Dict] = None					,      lowercase_			:					Optional[Dict] = None					,      lowercase_			:					float = 0.02					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 20.0					,      lowercase_			:					Optional[bool] = None					,      **lowercase_			:					int					,      )						->     Optional[Any]:
      if backbone_config is None:
       # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
       UpperCAmelCase	:     List[Any]						 =			SwinConfig(
           image_size=384					,      in_channels=3					,      patch_size=4					,      embed_dim=128					,      depths=[2, 2, 18, 2]					,      num_heads=[4, 8, 16, 32]					,      window_size=12					,      drop_path_rate=0.3					,      out_features=['stage1', 'stage2', 'stage3', 'stage4']					,      )

      if isinstance(lowercase_					,      lowercase_	):
       UpperCAmelCase	:     List[Any]						 =			backbone_config.pop('model_type'	)
       UpperCAmelCase	:     Optional[Any]						 =			CONFIG_MAPPING[backbone_model_type]
       UpperCAmelCase	:     List[Any]						 =			config_class.from_dict(lowercase_	)

      # verify that the backbone is supported
      if backbone_config.model_type not in self.backbones_supported:
       logger.warning_once(
           f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
           f"""Supported model types: {",".join(self.backbones_supported	)}"""	)

      if decoder_config is None:
       # fall back to https://huggingface.co/facebook/detr-resnet-50
       UpperCAmelCase	:     Dict						 =			DetrConfig()
      else:
       # verify that the decoder is supported
       UpperCAmelCase	:     Tuple						 =			(
           decoder_config.pop('model_type'	) if isinstance(lowercase_					,      lowercase_	) else decoder_config.model_type
       )
       if decoder_type not in self.decoders_supported:
        raise ValueError(
            f"""Transformer Decoder {decoder_type} not supported, please use one of"""
            f""" {",".join(self.decoders_supported	)}"""	)
       if isinstance(lowercase_					,      lowercase_	):
        UpperCAmelCase	:     Union[str, Any]						 =			CONFIG_MAPPING[decoder_type]
        UpperCAmelCase	:     str						 =			config_class.from_dict(lowercase_	)

      UpperCAmelCase	:     int						 =			backbone_config
      UpperCAmelCase	:     Tuple						 =			decoder_config
      # main feature dimension for the model
      UpperCAmelCase	:     Union[str, Any]						 =			fpn_feature_size
      UpperCAmelCase	:     Optional[Any]						 =			mask_feature_size
      # initializer
      UpperCAmelCase	:     Union[str, Any]						 =			init_std
      UpperCAmelCase	:     List[str]						 =			init_xavier_std
      # Hungarian matcher && loss
      UpperCAmelCase	:     int						 =			cross_entropy_weight
      UpperCAmelCase	:     Tuple						 =			dice_weight
      UpperCAmelCase	:     int						 =			mask_weight
      UpperCAmelCase	:     Any						 =			use_auxiliary_loss
      UpperCAmelCase	:     Tuple						 =			no_object_weight
      UpperCAmelCase	:     str						 =			output_auxiliary_logits

      UpperCAmelCase	:     Dict						 =			self.decoder_config.encoder_attention_heads
      UpperCAmelCase	:     Union[str, Any]						 =			self.decoder_config.num_hidden_layers
      super().__init__(**lowercase_	)


     @classmethod
     def        UpperCAmelCase_	(		cls			:					Any					,      lowercase_			:					PretrainedConfig					,      lowercase_			:					PretrainedConfig					,      **lowercase_			:					Optional[Any]	)						->     str:
      return cls(
          backbone_config=lowercase_					,      decoder_config=lowercase_					,      **lowercase_					,      )




     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Dict[str, any]:
      UpperCAmelCase	:     Optional[int]						 =			copy.deepcopy(self.__dict__	)
      UpperCAmelCase	:     List[str]						 =			self.backbone_config.to_dict()
      UpperCAmelCase	:     Any						 =			self.decoder_config.to_dict()
      UpperCAmelCase	:     Union[str, Any]						 =			self.__class__.model_type
      return output




