


"""simple docstring"""



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    (CMStochasticIterativeScheduler,)
   __UpperCAmelCase					:			Any				    =    10







   def 					lowerCamelCase						(      self		,      **UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       snake_case       : str    =       {
           "num_train_timesteps": 201,
           "sigma_min": 0.002,
           "sigma_max": 80.0,
       }

       config.update(**UpperCamelCase__		)
       return config







   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       snake_case       : str    =       10

       snake_case       : List[str]    =       self.get_scheduler_config()
       snake_case       : Optional[Any]    =       self.scheduler_classes[0](**UpperCamelCase__		)

       scheduler.set_timesteps(UpperCamelCase__		)

       snake_case       : Any    =       scheduler.timesteps[0]
       snake_case       : List[str]    =       scheduler.timesteps[1]

       snake_case       : str    =       self.dummy_sample
       snake_case       : Union[str, Any]    =       0.1 * sample

       snake_case       : List[Any]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		).prev_sample
       snake_case       : str    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		).prev_sample

       self.assertEqual(output_a.shape		,      sample.shape		)
       self.assertEqual(output_a.shape		,      output_a.shape		)







   def 					lowerCamelCase						(      self		)      ->						List[str]:

       '''simple docstring'''




       for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Dict:

       '''simple docstring'''




       for clip_denoised in [True, False]:
           self.check_over_configs(clip_denoised=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : Optional[int]    =       self.scheduler_classes[0]
       snake_case       : Dict    =       self.get_scheduler_config()
       snake_case       : int    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : str    =       1
       scheduler.set_timesteps(UpperCamelCase__		)
       snake_case       : int    =       scheduler.timesteps

       snake_case       : Tuple    =       torch.manual_seed(0		)

       snake_case       : Optional[int]    =       self.dummy_model()
       snake_case       : Tuple    =       self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(UpperCamelCase__		):
           # 1. scale model input
           snake_case       : Any    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           # 2. predict noise residual
           snake_case       : List[Any]    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           # 3. predict previous sample x_t-1
           snake_case       : List[str]    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      generator=UpperCamelCase__		).prev_sample

           snake_case       : Any    =       pred_prev_sample

       snake_case       : Dict    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : str    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       assert abs(result_sum.item() - 192.7614		) < 1e-2
       assert abs(result_mean.item() - 0.2510		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       snake_case       : Tuple    =       self.scheduler_classes[0]
       snake_case       : List[Any]    =       self.get_scheduler_config()
       snake_case       : Optional[int]    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : Tuple    =       [106, 0]
       scheduler.set_timesteps(timesteps=UpperCamelCase__		)
       snake_case       : Optional[int]    =       scheduler.timesteps

       snake_case       : str    =       torch.manual_seed(0		)

       snake_case       : str    =       self.dummy_model()
       snake_case       : Optional[Any]    =       self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
           # 1. scale model input
           snake_case       : str    =       scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)

           # 2. predict noise residual
           snake_case       : Tuple    =       model(UpperCamelCase__		,      UpperCamelCase__		)

           # 3. predict previous sample x_t-1
           snake_case       : str    =       scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      generator=UpperCamelCase__		).prev_sample

           snake_case       : Optional[int]    =       pred_prev_sample

       snake_case       : Union[str, Any]    =       torch.sum(torch.abs(UpperCamelCase__		)		)
       snake_case       : Any    =       torch.mean(torch.abs(UpperCamelCase__		)		)

       assert abs(result_sum.item() - 347.6357		) < 1e-2
       assert abs(result_mean.item() - 0.4527		) < 1e-3







   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       snake_case       : Any    =       self.scheduler_classes[0]
       snake_case       : Tuple    =       self.get_scheduler_config()
       snake_case       : Optional[int]    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : int    =       [39, 30, 12, 15, 0]

       with self.assertRaises(UpperCamelCase__		,      msg="`timesteps` must be in descending order."		):
           scheduler.set_timesteps(timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						Tuple:

       '''simple docstring'''




       snake_case       : List[str]    =       self.scheduler_classes[0]
       snake_case       : Dict    =       self.get_scheduler_config()
       snake_case       : Union[str, Any]    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : Optional[Any]    =       [39, 30, 12, 1, 0]
       snake_case       : List[Any]    =       len(UpperCamelCase__		)

       with self.assertRaises(UpperCamelCase__		,      msg="Can only pass one of `num_inference_steps` or `timesteps`."		):
           scheduler.set_timesteps(num_inference_steps=UpperCamelCase__		,      timesteps=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       snake_case       : Union[str, Any]    =       self.scheduler_classes[0]
       snake_case       : Union[str, Any]    =       self.get_scheduler_config()
       snake_case       : str    =       scheduler_class(**UpperCamelCase__		)

       snake_case       : List[Any]    =       [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           UpperCamelCase__		,      msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"		,      ):
           scheduler.set_timesteps(timesteps=UpperCamelCase__		)









"""simple docstring"""



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


__snake_case       =       {
    """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
}

__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Any				    =    '''maskformer'''
   __UpperCAmelCase					:			List[str]				    =    {'''hidden_size''': '''mask_feature_size'''}
   __UpperCAmelCase					:			Optional[Any]				    =    ['''resnet''', '''swin''']
   __UpperCAmelCase					:			str				    =    ['''detr''']







   def __init__(      self		,      UpperCamelCase__ = 256		,      UpperCamelCase__ = 256		,      UpperCamelCase__ = 0.1		,      UpperCamelCase__ = False		,      UpperCamelCase__ = None		,      UpperCamelCase__ = None		,      UpperCamelCase__ = 0.02		,      UpperCamelCase__ = 1.0		,      UpperCamelCase__ = 1.0		,      UpperCamelCase__ = 1.0		,      UpperCamelCase__ = 20.0		,      UpperCamelCase__ = None		,      **UpperCamelCase__		,      )      ->						Optional[int]:

       '''simple docstring'''




       if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           snake_case       : Tuple    =       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__		):
           snake_case       : Optional[Any]    =       backbone_config.pop("model_type"		)
           snake_case       : List[str]    =       CONFIG_MAPPING[backbone_model_type]
           snake_case       : str    =       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
           snake_case       : Optional[int]    =       DetrConfig()
       else:
           # verify that the decoder is supported
           snake_case       : Optional[Any]    =       (
               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__		):
               snake_case       : Dict    =       CONFIG_MAPPING[decoder_type]
               snake_case       : List[Any]    =       config_class.from_dict(UpperCamelCase__		)

       snake_case       : Optional[int]    =       backbone_config
       snake_case       : Optional[Any]    =       decoder_config
       # main feature dimension for the model
       snake_case       : List[str]    =       fpn_feature_size
       snake_case       : Tuple    =       mask_feature_size
       # initializer
       snake_case       : str    =       init_std
       snake_case       : str    =       init_xavier_std
       # Hungarian matcher && loss
       snake_case       : List[Any]    =       cross_entropy_weight
       snake_case       : int    =       dice_weight
       snake_case       : str    =       mask_weight
       snake_case       : Any    =       use_auxiliary_loss
       snake_case       : Any    =       no_object_weight
       snake_case       : List[Any]    =       output_auxiliary_logits

       snake_case       : Optional[int]    =       self.decoder_config.encoder_attention_heads
       snake_case       : Tuple    =       self.decoder_config.num_hidden_layers
       super().__init__(**UpperCamelCase__		)







   @classmethod
   def 					lowerCamelCase						(      cls		,      UpperCamelCase__		,      UpperCamelCase__		,      **UpperCamelCase__		)      ->						Optional[int]:

       '''simple docstring'''




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







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

       '''simple docstring'''




       snake_case       : int    =       copy.deepcopy(self.__dict__		)
       snake_case       : List[str]    =       self.backbone_config.to_dict()
       snake_case       : Union[str, Any]    =       self.decoder_config.to_dict()
       snake_case       : Optional[int]    =       self.__class__.model_type
       return output





