






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


UpperCAmelCase__							  =						{
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

UpperCAmelCase__							  =						logging.get_logger(__name__)






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    '''maskformer'''
      __snake_case	    =    {'''hidden_size''': '''mask_feature_size'''}
      __snake_case	    =    ['''resnet''', '''swin''']
      __snake_case	    =    ['''detr''']







      def __init__( self :		Union[str, Any]					,   __UpperCAmelCase :		int = 256					,   __UpperCAmelCase :		int = 256					,   __UpperCAmelCase :		float = 0.1					,   __UpperCAmelCase :		bool = False					,   __UpperCAmelCase :		Optional[Dict] = None					,   __UpperCAmelCase :		Optional[Dict] = None					,   __UpperCAmelCase :		float = 0.02					,   __UpperCAmelCase :		float = 1.0					,   __UpperCAmelCase :		float = 1.0					,   __UpperCAmelCase :		float = 1.0					,   __UpperCAmelCase :		float = 20.0					,   __UpperCAmelCase :		Optional[bool] = None					,   **__UpperCAmelCase :		List[str]					,   )       ->Optional[Any]:




       """simple docstring"""


       if backbone_config is None:
        # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
        a						    = SwinConfig(
            image_size=384					,   in_channels=3					,   patch_size=4					,   embed_dim=128					,   depths=[2, 2, 18, 2]					,   num_heads=[4, 8, 16, 32]					,   window_size=12					,   drop_path_rate=0.3					,   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 MaskFormer. """
            F"""Supported model types: {','.join(self.backbones_supported    )}"""    )

       if decoder_config is None:
        # fall back to https://huggingface.co/facebook/detr-resnet-50
        a						    = DetrConfig()
       else:
        # verify that the decoder is supported
        a						    = (
            decoder_config.pop('''model_type'''    ) if isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ) else decoder_config.model_type
        )
        if decoder_type not in self.decoders_supported:
         raise ValueError(
             F"""Transformer Decoder {decoder_type} not supported, please use one of"""
             F""" {','.join(self.decoders_supported    )}"""    )
        if isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ):
         a						    = CONFIG_MAPPING[decoder_type]
         a						    = config_class.from_dict(__UpperCAmelCase    )

       a						    = backbone_config
       a						    = decoder_config
       # main feature dimension for the model
       a						    = fpn_feature_size
       a						    = mask_feature_size
       # initializer
       a						    = init_std
       a						    = init_xavier_std
       # Hungarian matcher && loss
       a						    = cross_entropy_weight
       a						    = dice_weight
       a						    = mask_weight
       a						    = use_auxiliary_loss
       a						    = no_object_weight
       a						    = output_auxiliary_logits

       a						    = self.decoder_config.encoder_attention_heads
       a						    = self.decoder_config.num_hidden_layers
       super().__init__(**__UpperCAmelCase    )







      @classmethod
      def  __lowerCAmelCase   ( cls :		List[str]					,   __UpperCAmelCase :		PretrainedConfig					,   __UpperCAmelCase :		PretrainedConfig					,   **__UpperCAmelCase :		int    )       ->List[Any]:




       """simple docstring"""


       return cls(
           backbone_config=__UpperCAmelCase					,   decoder_config=__UpperCAmelCase					,   **__UpperCAmelCase					,   )







      def  __lowerCAmelCase   ( self :		List[str]    )       ->Dict[str, any]:




       """simple docstring"""


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










# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		Optional[int]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		bool = False    )       ->Tuple:




       """simple docstring"""


       a						    = scheduler
       a						    = optimizers if isinstance(__UpperCAmelCase					,   (list, tuple)    ) else [optimizers]
       a						    = split_batches
       a						    = step_with_optimizer
       a						    = GradientState()







      def  __lowerCAmelCase   ( self :		Tuple					,   *__UpperCAmelCase :		List[Any]					,   **__UpperCAmelCase :		Optional[Any]    )       ->int:




       """simple docstring"""


       if not self.step_with_optimizer:
        # No link between scheduler and optimizer -> just step
        self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )
        return

       # Otherwise, first make sure the optimizer was stepped.
       if not self.gradient_state.sync_gradients:
        if self.gradient_state.adjust_scheduler:
         self.scheduler._step_count += 1
        return

       for opt in self.optimizers:
        if opt.step_was_skipped:
         return
       if self.split_batches:
        # Split batches -> the training dataloader batch size is not changed so one step per training step
        self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )
       else:
        # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
        # num_processes steps per training step
        a						    = AcceleratorState().num_processes
        for _ in range(__UpperCAmelCase    ):
         # Special case when using OneCycle and `drop_last` was not used
         if hasattr(self.scheduler					,   '''total_steps'''    ):
          if self.scheduler._step_count <= self.scheduler.total_steps:
           self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )
         else:
          self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->List[str]:




       """simple docstring"""


       return self.scheduler.get_last_lr()







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """simple docstring"""


       return self.scheduler.state_dict()







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		Any    )       ->Tuple:




       """simple docstring"""


       self.scheduler.load_state_dict(__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Dict    )       ->Optional[int]:




       """simple docstring"""


       return self.scheduler.get_lr()







      def  __lowerCAmelCase   ( self :		int					,   *__UpperCAmelCase :		List[str]					,   **__UpperCAmelCase :		Optional[Any]    )       ->List[str]:




       """simple docstring"""


       return self.scheduler.print_lr(*__UpperCAmelCase					,   **__UpperCAmelCase    )


