


"""simple docstring"""



import copy
from typing import Dict, List, Optional

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


UpperCAmelCase      		=			{
    '''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
}

UpperCAmelCase      		=			logging.get_logger(__name__)
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      List[str]        =   "mask2former"
       __A			:      Tuple        =   ["swin"]
       __A			:      List[str]        =   {"hidden_size": "hidden_dim"}






       def __init__(   self  :    str  ,     snake_case__  :    Optional[Dict] = None  ,     snake_case__  :    int = 2_5_6  ,     snake_case__  :    int = 2_5_6  ,     snake_case__  :    int = 2_5_6  ,     snake_case__  :    int = 1_0_2_4  ,     snake_case__  :    str = "relu"  ,     snake_case__  :    int = 6  ,     snake_case__  :    int = 1_0  ,     snake_case__  :    int = 8  ,     snake_case__  :    float = 0.0  ,     snake_case__  :    int = 2_0_4_8  ,     snake_case__  :    bool = False  ,     snake_case__  :    bool = False  ,     snake_case__  :    int = 4  ,     snake_case__  :    int = 2_5_5  ,     snake_case__  :    int = 1_0_0  ,     snake_case__  :    float = 0.1  ,     snake_case__  :    float = 2.0  ,     snake_case__  :    float = 5.0  ,     snake_case__  :    float = 5.0  ,     snake_case__  :    int = 1_2_5_4_4  ,     snake_case__  :    float = 3.0  ,     snake_case__  :    float = 0.75  ,     snake_case__  :    float = 0.02  ,     snake_case__  :    float = 1.0  ,     snake_case__  :    bool = True  ,     snake_case__  :    List[int] = [4, 8, 1_6, 3_2]  ,     snake_case__  :    bool = None  ,     **snake_case__  :    int  ,     ):

              '''simple docstring'''






              if backbone_config is None:
                     logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''				)
                     lowercase		:int       							=				CONFIG_MAPPING['''swin'''](
                         image_size=2_2_4  ,     in_channels=3  ,     patch_size=4  ,     embed_dim=9_6  ,     depths=[2, 2, 1_8, 2]  ,     num_heads=[3, 6, 1_2, 2_4]  ,     window_size=7  ,     drop_path_rate=0.3  ,     use_absolute_embeddings=snake_case__  ,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']  ,     )

              if isinstance(snake_case__  ,     snake_case__				):
                     lowercase		:Tuple       							=				backbone_config.pop('''model_type'''				)
                     lowercase		:int       							=				CONFIG_MAPPING[backbone_model_type]
                     lowercase		:Optional[Any]       							=				config_class.from_dict(snake_case__				)

              # 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				)}"""				)

              lowercase		:str       							=				backbone_config
              lowercase		:Dict       							=				feature_size
              lowercase		:List[Any]       							=				mask_feature_size
              lowercase		:Union[str, Any]       							=				hidden_dim
              lowercase		:Dict       							=				encoder_feedforward_dim
              lowercase		:Any       							=				activation_function
              lowercase		:Any       							=				encoder_layers
              lowercase		:List[str]       							=				decoder_layers
              lowercase		:Dict       							=				num_attention_heads
              lowercase		:Optional[int]       							=				dropout
              lowercase		:List[Any]       							=				dim_feedforward
              lowercase		:Any       							=				pre_norm
              lowercase		:List[str]       							=				enforce_input_projection
              lowercase		:List[str]       							=				common_stride
              lowercase		:Tuple       							=				ignore_value
              lowercase		:Optional[Any]       							=				num_queries
              lowercase		:Tuple       							=				no_object_weight
              lowercase		:Optional[int]       							=				class_weight
              lowercase		:Dict       							=				mask_weight
              lowercase		:List[Any]       							=				dice_weight
              lowercase		:str       							=				train_num_points
              lowercase		:Tuple       							=				oversample_ratio
              lowercase		:str       							=				importance_sample_ratio
              lowercase		:List[str]       							=				init_std
              lowercase		:List[Any]       							=				init_xavier_std
              lowercase		:List[Any]       							=				use_auxiliary_loss
              lowercase		:List[str]       							=				feature_strides
              lowercase		:Optional[int]       							=				output_auxiliary_logits
              lowercase		:int       							=				decoder_layers

              super().__init__(**snake_case__				)






       @classmethod
       def  __snake_case						(   cls  :    int  ,     snake_case__  :    PretrainedConfig  ,     **snake_case__  :    Any				):

              '''simple docstring'''






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







       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Any       							=				copy.deepcopy(self.__dict__				)
              lowercase		:Any       							=				self.backbone_config.to_dict()
              lowercase		:str       							=				self.__class__.model_type
              return output





"""simple docstring"""



import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
 import jax
@require_flax
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      str        =   FlaxAutoencoderKL






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Dict       							=				4
              lowercase		:Optional[int]       							=				3
              lowercase		:Any       							=				(3_2, 3_2)

              lowercase		:Optional[int]       							=				jax.random.PRNGKey(0				)
              lowercase		:Any       							=				jax.random.uniform(snake_case__  ,     ((batch_size, num_channels) + sizes)				)

              return {"sample": image, "prng_key": prng_key}







       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				{
                  '''block_out_channels''': [3_2, 6_4],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 4,
              }
              lowercase		:Optional[int]       							=				self.dummy_input
              return init_dict, inputs_dict

