
'''simple docstring'''
import copy
from typing import Dict, List, Optional

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


__lowerCAmelCase												=	{
    '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												=	logging.get_logger(__name__)


class       _lowerCAmelCase   (   a_				):







							'''simple docstring'''

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

							def __init__(self  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 256  ,  UpperCAmelCase = 256  ,  UpperCAmelCase = 256  ,  UpperCAmelCase = 1024  ,  UpperCAmelCase = "relu"  ,  UpperCAmelCase = 6  ,  UpperCAmelCase = 10  ,  UpperCAmelCase = 8  ,  UpperCAmelCase = 0.0  ,  UpperCAmelCase = 2048  ,  UpperCAmelCase = False  ,  UpperCAmelCase = False  ,  UpperCAmelCase = 4  ,  UpperCAmelCase = 255  ,  UpperCAmelCase = 100  ,  UpperCAmelCase = 0.1  ,  UpperCAmelCase = 2.0  ,  UpperCAmelCase = 5.0  ,  UpperCAmelCase = 5.0  ,  UpperCAmelCase = 12544  ,  UpperCAmelCase = 3.0  ,  UpperCAmelCase = 0.75  ,  UpperCAmelCase = 0.02  ,  UpperCAmelCase = 1.0  ,  UpperCAmelCase = True  ,  UpperCAmelCase = [4, 8, 16, 32]  ,  UpperCAmelCase = None  ,  **UpperCAmelCase  ,  )					->       str:

														if backbone_config is None:
																					logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""  )
																					_snake_case       =  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=lowercase_  ,  out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]  ,  )

														if isinstance(lowercase_  ,  lowercase_  ):
																					_snake_case       =  backbone_config.pop("""model_type"""  )
																					_snake_case       =  CONFIG_MAPPING[backbone_model_type]
																					_snake_case       =  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 Mask2Former. """
																					    f"""Supported model types: {",".join(self.backbones_supported  )}"""  )

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

														super().__init__(**lowercase_  )

							@classmethod
							def 	lowercase							(cls  ,  UpperCAmelCase  ,  **UpperCAmelCase  )					->       Any:
														return cls(
														    backbone_config=lowercase_  ,  **lowercase_  ,  )





							def 	lowercase							(self  )					->       Dict[str, any]:
														_snake_case       =  copy.deepcopy(self.__dict__  )
														_snake_case       =  self.backbone_config.to_dict()
														_snake_case       =  self.__class__.model_type
														return output

'''simple docstring'''
import logging

from transformers.configuration_utils import PretrainedConfig


__lowerCAmelCase												=	logging.getLogger(__name__)


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						"masked_bert"

							def __init__(self  ,  UpperCAmelCase=30522  ,  UpperCAmelCase=768  ,  UpperCAmelCase=12  ,  UpperCAmelCase=12  ,  UpperCAmelCase=3072  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=1e-1_2  ,  UpperCAmelCase=0  ,  UpperCAmelCase="topK"  ,  UpperCAmelCase="constant"  ,  UpperCAmelCase=0.0  ,  **UpperCAmelCase  ,  )					->       int:
														super().__init__(pad_token_id=UpperCAmelCase  ,  **UpperCAmelCase  )

														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  hidden_act
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  initializer_range
														_snake_case       =  layer_norm_eps
														_snake_case       =  pruning_method
														_snake_case       =  mask_init
														_snake_case       =  mask_scale