




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	:		str							       =   logging.get_logger(__name__)


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






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


    def __init__(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Optional[Dict] = None					, UpperCAmelCase__      :	int = 256					, UpperCAmelCase__      :	int = 256					, UpperCAmelCase__      :	int = 256					, UpperCAmelCase__      :	int = 1_024					, UpperCAmelCase__      :	str = "relu"					, UpperCAmelCase__      :	int = 6					, UpperCAmelCase__      :	int = 10					, UpperCAmelCase__      :	int = 8					, UpperCAmelCase__      :	float = 0.0					, UpperCAmelCase__      :	int = 2_048					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	bool = False					, UpperCAmelCase__      :	int = 4					, UpperCAmelCase__      :	int = 255					, UpperCAmelCase__      :	int = 100					, UpperCAmelCase__      :	float = 0.1					, UpperCAmelCase__      :	float = 2.0					, UpperCAmelCase__      :	float = 5.0					, UpperCAmelCase__      :	float = 5.0					, UpperCAmelCase__      :	int = 12_544					, UpperCAmelCase__      :	float = 3.0					, UpperCAmelCase__      :	float = 0.75					, UpperCAmelCase__      :	float = 0.02					, UpperCAmelCase__      :	float = 1.0					, UpperCAmelCase__      :	bool = True					, UpperCAmelCase__      :	List[int] = [4, 8, 16, 32]					, UpperCAmelCase__      :	bool = None					, **UpperCAmelCase__      :	Union[str, Any]					, )						->Optional[Any]:





           '''simple docstring'''

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

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

           # 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)}""")

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

           super().__init__(**UpperCAmelCase__)


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	List[Any]					, UpperCAmelCase__      :	PretrainedConfig					, **UpperCAmelCase__      :	List[Any])						->str:





           '''simple docstring'''

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





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





           '''simple docstring'''

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







'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


_lowerCamelCase	:       str      	=	logging.get_logger(__name__)

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    def __init__(  self      :					Dict  ,						*UpperCamelCase__      :					List[Any]  ,						**UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       warnings.warn(
           'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use ChineseCLIPImageProcessor instead.'  ,						UpperCamelCase__  ,						)
       super().__init__(*UpperCamelCase__  ,						**UpperCamelCase__ )

