




import copy
from typing import Dict, List, Optional

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


lowerCamelCase    :		Union[str, Any]			   =       {
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

lowerCamelCase    :		Optional[Any]			   =       logging.get_logger(__name__)







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''mask2former'''
 UpperCamelCase						       =						['''swin''']
 UpperCamelCase						       =						{'''hidden_size''': '''hidden_dim'''}

 def __init__(    self :  str						,							A_ :  Optional[Dict] = None						,							A_ :  int = 256						,							A_ :  int = 256						,							A_ :  int = 256						,							A_ :  int = 1024						,							A_ :  str = "relu"						,							A_ :  int = 6						,							A_ :  int = 10						,							A_ :  int = 8						,							A_ :  float = 0.0						,							A_ :  int = 2048						,							A_ :  bool = False						,							A_ :  bool = False						,							A_ :  int = 4						,							A_ :  int = 255						,							A_ :  int = 100						,							A_ :  float = 0.1						,							A_ :  float = 2.0						,							A_ :  float = 5.0						,							A_ :  float = 5.0						,							A_ :  int = 12544						,							A_ :  float = 3.0						,							A_ :  float = 0.75						,							A_ :  float = 0.02						,							A_ :  float = 1.0						,							A_ :  bool = True						,							A_ :  List[int] = [4, 8, 16, 32]						,							A_ :  bool = None						,							**A_ :  Tuple						,							)       ->     Optional[Any]:

       """simple docstring"""






       if backbone_config is None:
             logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'  )
             lowerCamelCase_							=   CONFIG_MAPPING['swin'](
                 image_size=224						,							in_channels=3						,							patch_size=4						,							embed_dim=96						,							depths=[2, 2, 18, 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']						,							)

       if isinstance(A_						,							A_  ):
             lowerCamelCase_							=   backbone_config.pop('model_type'  )
             lowerCamelCase_							=   CONFIG_MAPPING[backbone_model_type]
             lowerCamelCase_							=   config_class.from_dict(A_  )

       # 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 Mask2Former. """
                 f"""Supported model types: {",".join(self.backbones_supported  )}"""  )

       lowerCamelCase_							=   backbone_config
       lowerCamelCase_							=   feature_size
       lowerCamelCase_							=   mask_feature_size
       lowerCamelCase_							=   hidden_dim
       lowerCamelCase_							=   encoder_feedforward_dim
       lowerCamelCase_							=   activation_function
       lowerCamelCase_							=   encoder_layers
       lowerCamelCase_							=   decoder_layers
       lowerCamelCase_							=   num_attention_heads
       lowerCamelCase_							=   dropout
       lowerCamelCase_							=   dim_feedforward
       lowerCamelCase_							=   pre_norm
       lowerCamelCase_							=   enforce_input_projection
       lowerCamelCase_							=   common_stride
       lowerCamelCase_							=   ignore_value
       lowerCamelCase_							=   num_queries
       lowerCamelCase_							=   no_object_weight
       lowerCamelCase_							=   class_weight
       lowerCamelCase_							=   mask_weight
       lowerCamelCase_							=   dice_weight
       lowerCamelCase_							=   train_num_points
       lowerCamelCase_							=   oversample_ratio
       lowerCamelCase_							=   importance_sample_ratio
       lowerCamelCase_							=   init_std
       lowerCamelCase_							=   init_xavier_std
       lowerCamelCase_							=   use_auxiliary_loss
       lowerCamelCase_							=   feature_strides
       lowerCamelCase_							=   output_auxiliary_logits
       lowerCamelCase_							=   decoder_layers

       super().__init__(**A_  )

 @classmethod
 def     a__			(    cls :  Dict						,							A_ :  PretrainedConfig						,							**A_ :  Union[str, Any]  )       ->     int:

       """simple docstring"""






       return cls(
           backbone_config=A_						,							**A_						,							)





 def     a__			(    self :  Any  )       ->     Dict[str, any]:

       """simple docstring"""






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









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


lowerCamelCase    :		Optional[Any]			   =       logging.get_logger(__name__)

lowerCamelCase    :		Tuple			   =       {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''transfo-xl'''
 UpperCamelCase						       =						['''mems''']
 UpperCamelCase						       =						{
     '''n_token''': '''vocab_size''',
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(    self :  Any						,							A_ :  Optional[Any]=267735						,							A_ :  Optional[Any]=[20000, 40000, 200000]						,							A_ :  Union[str, Any]=1024						,							A_ :  Optional[Any]=1024						,							A_ :  Optional[int]=16						,							A_ :  Any=64						,							A_ :  List[Any]=4096						,							A_ :  str=4						,							A_ :  int=False						,							A_ :  List[Any]=18						,							A_ :  Optional[int]=1600						,							A_ :  Union[str, Any]=1000						,							A_ :  Optional[Any]=True						,							A_ :  Optional[int]=True						,							A_ :  List[str]=0						,							A_ :  int=-1						,							A_ :  List[Any]=True						,							A_ :  List[Any]=0.1						,							A_ :  str=0.0						,							A_ :  Dict=True						,							A_ :  Dict="normal"						,							A_ :  Dict=0.01						,							A_ :  Optional[Any]=0.01						,							A_ :  Any=0.02						,							A_ :  int=1E-5						,							A_ :  List[str]=0						,							**A_ :  Optional[Any]						,							)       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   []
       self.cutoffs.extend(A_  )
       if proj_share_all_but_first:
             lowerCamelCase_							=   [False] + [True] * len(self.cutoffs  )
       else:
             lowerCamelCase_							=   [False] + [False] * len(self.cutoffs  )
       lowerCamelCase_							=   d_model
       lowerCamelCase_							=   d_embed
       lowerCamelCase_							=   d_head
       lowerCamelCase_							=   d_inner
       lowerCamelCase_							=   div_val
       lowerCamelCase_							=   pre_lnorm
       lowerCamelCase_							=   n_layer
       lowerCamelCase_							=   n_head
       lowerCamelCase_							=   mem_len
       lowerCamelCase_							=   same_length
       lowerCamelCase_							=   attn_type
       lowerCamelCase_							=   clamp_len
       lowerCamelCase_							=   sample_softmax
       lowerCamelCase_							=   adaptive
       lowerCamelCase_							=   dropout
       lowerCamelCase_							=   dropatt
       lowerCamelCase_							=   untie_r
       lowerCamelCase_							=   init
       lowerCamelCase_							=   init_range
       lowerCamelCase_							=   proj_init_std
       lowerCamelCase_							=   init_std
       lowerCamelCase_							=   layer_norm_epsilon
       super().__init__(eos_token_id=A_						,							**A_  )

 @property
 def     a__			(    self :  Tuple  )       ->     Any:

       """simple docstring"""






       logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )
       return -1





 @max_position_embeddings.setter
 def     a__			(    self :  Dict						,							A_ :  Optional[int]  )       ->     List[Any]:

       """simple docstring"""






       raise NotImplementedError(
           f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )



