





from typing import TYPE_CHECKING

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


__A									=				{
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				["Mask2FormerImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
         "Mask2FormerForUniversalSegmentation",
         "Mask2FormerModel",
         "Mask2FormerPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_maskaformer import MaskaFormerImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskaformer import (
              MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskaFormerForUniversalSegmentation,
              MaskaFormerModel,
              MaskaFormerPreTrainedModel,
          )


else:
     import sys

     __A									=				_LazyModule(__name__, globals()["__file__"], _import_structure)








import logging

from transformers.configuration_utils import PretrainedConfig


__A									=				logging.getLogger(__name__)



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			"masked_bert"

 def __init__(self				:   Dict       , UpperCAmelCase_				:   Any=30_522       , UpperCAmelCase_				:   List[Any]=768       , UpperCAmelCase_				:   Optional[Any]=12       , UpperCAmelCase_				:   str=12       , UpperCAmelCase_				:   Tuple=3_072       , UpperCAmelCase_				:   str="gelu"       , UpperCAmelCase_				:   Any=0.1       , UpperCAmelCase_				:   Tuple=0.1       , UpperCAmelCase_				:   Optional[Any]=512       , UpperCAmelCase_				:   Union[str, Any]=2       , UpperCAmelCase_				:   str=0.02       , UpperCAmelCase_				:   str=1E-1_2       , UpperCAmelCase_				:   Union[str, Any]=0       , UpperCAmelCase_				:   str="topK"       , UpperCAmelCase_				:   List[str]="constant"       , UpperCAmelCase_				:   str=0.0       , **UpperCAmelCase_				:   int       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(pad_token_id=UpperCAmelCase_       , **UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =vocab_size
   lowerCamelCase__:      Dict			  =hidden_size
   lowerCamelCase__:      Optional[int]			  =num_hidden_layers
   lowerCamelCase__:      Any			  =num_attention_heads
   lowerCamelCase__:      List[Any]			  =hidden_act
   lowerCamelCase__:      str			  =intermediate_size
   lowerCamelCase__:      Dict			  =hidden_dropout_prob
   lowerCamelCase__:      str			  =attention_probs_dropout_prob
   lowerCamelCase__:      int			  =max_position_embeddings
   lowerCamelCase__:      Tuple			  =type_vocab_size
   lowerCamelCase__:      str			  =initializer_range
   lowerCamelCase__:      List[Any]			  =layer_norm_eps
   lowerCamelCase__:      str			  =pruning_method
   lowerCamelCase__:      Union[str, Any]			  =mask_init
   lowerCamelCase__:      Optional[Any]			  =mask_scale

