

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


SCREAMING_SNAKE_CASE		:Dict						=     {
    """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
}

SCREAMING_SNAKE_CASE		:Union[str, Any]						=     logging.get_logger(__name__)






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Optional[Any]										=      """maskformer"""
     UpperCamelCase_	:List[str]										=      {"""hidden_size""": """mask_feature_size"""}
     UpperCamelCase_	:int										=      ["""resnet""", """swin"""]
     UpperCamelCase_	:Optional[int]										=      ["""detr"""]






     def __init__(				self		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 0.1		,						_lowercase = False		,						_lowercase = None		,						_lowercase = None		,						_lowercase = 0.02		,						_lowercase = 1.0		,						_lowercase = 1.0		,						_lowercase = 1.0		,						_lowercase = 20.0		,						_lowercase = None		,						**_lowercase		,						)->     int:
         if backbone_config is None:
             # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
             UpperCamelCase_			  =						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(_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						backbone_config.pop("model_type"	)
             UpperCamelCase_			  =						CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase_			  =						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 MaskFormer. "
                 F"Supported model types: {','.join(self.backbones_supported	)}"	)

         if decoder_config is None:
             # fall back to https://huggingface.co/facebook/detr-resnet-50
             UpperCamelCase_			  =						DetrConfig()
         else:
             # verify that the decoder is supported
             UpperCamelCase_			  =						(
                 decoder_config.pop("model_type"	) if isinstance(_lowercase		,						_lowercase	) 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(_lowercase		,						_lowercase	):
                 UpperCamelCase_			  =						CONFIG_MAPPING[decoder_type]
                 UpperCamelCase_			  =						config_class.from_dict(_lowercase	)

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

         UpperCamelCase_			  =						self.decoder_config.encoder_attention_heads
         UpperCamelCase_			  =						self.decoder_config.num_hidden_layers
         super().__init__(**_lowercase	)






     @classmethod
     def 	UpperCAmelCase_				(				cls		,						_lowercase		,						_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return cls(
             backbone_config=_lowercase		,						decoder_config=_lowercase		,						**_lowercase		,						)






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



import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      (KDPMaDiscreteScheduler,)
     UpperCamelCase_	:str										=      1_0






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     str:
         UpperCamelCase_			  =						{
             "num_train_timesteps": 1_100,
             "beta_start": 0.0_001,
             "beta_end": 0.02,
             "beta_schedule": "linear",
         }

         config.update(**_lowercase	)
         return config






     def 	UpperCAmelCase_				(				self	)->     Union[str, Any]:
         for timesteps in [10, 50, 100, 1_000]:
             self.check_over_configs(num_train_timesteps=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     int:
         for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,						[0.0_002, 0.002, 0.02]	):
             self.check_over_configs(beta_start=_lowercase		,						beta_end=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     str:
         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Any:
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=_lowercase	)






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config(prediction_type="v_prediction"	)
         UpperCamelCase_			  =						scheduler_class(**_lowercase	)

         scheduler.set_timesteps(self.num_inference_steps	)

         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCamelCase_			  =						sample.to(_lowercase	)

         for i, t in enumerate(scheduler.timesteps	):
             UpperCamelCase_			  =						scheduler.scale_model_input(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						model(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						scheduler.step(_lowercase		,						_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						output.prev_sample

         UpperCamelCase_			  =						torch.sum(torch.abs(_lowercase	)	)
         UpperCamelCase_			  =						torch.mean(torch.abs(_lowercase	)	)

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 4.6_9_3_4e-0_7	) < 1e-2
             assert abs(result_mean.item() - 6.1_1_1_2e-1_0	) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7	) < 1e-2
             assert abs(result_mean.item() - 0.0_002	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Dict:
         if torch_device == "mps":
             return
         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config()
         UpperCamelCase_			  =						scheduler_class(**_lowercase	)

         scheduler.set_timesteps(self.num_inference_steps	)

         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter * scheduler.init_noise_sigma
         UpperCamelCase_			  =						sample.to(_lowercase	)

         for i, t in enumerate(scheduler.timesteps	):
             UpperCamelCase_			  =						scheduler.scale_model_input(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						model(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						scheduler.step(_lowercase		,						_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						output.prev_sample

         UpperCamelCase_			  =						torch.sum(torch.abs(_lowercase	)	)
         UpperCamelCase_			  =						torch.mean(torch.abs(_lowercase	)	)

         if torch_device in ["cpu", "mps"]:
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3






     def 	UpperCAmelCase_				(				self	)->     Optional[int]:
         if torch_device == "mps":
             return
         UpperCamelCase_			  =						self.scheduler_classes[0]
         UpperCamelCase_			  =						self.get_scheduler_config()
         UpperCamelCase_			  =						scheduler_class(**_lowercase	)

         scheduler.set_timesteps(self.num_inference_steps		,						device=_lowercase	)

         UpperCamelCase_			  =						self.dummy_model()
         UpperCamelCase_			  =						self.dummy_sample_deter.to(_lowercase	) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             UpperCamelCase_			  =						scheduler.scale_model_input(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						model(_lowercase		,						_lowercase	)

             UpperCamelCase_			  =						scheduler.step(_lowercase		,						_lowercase		,						_lowercase	)
             UpperCamelCase_			  =						output.prev_sample

         UpperCamelCase_			  =						torch.sum(torch.abs(_lowercase	)	)
         UpperCamelCase_			  =						torch.mean(torch.abs(_lowercase	)	)

         if str(_lowercase	).startswith("cpu"	):
             # The following sum varies between 148 and 156 on mps. Why?
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3
         else:
             # CUDA
             assert abs(result_sum.item() - 20.4_125	) < 1e-2
             assert abs(result_mean.item() - 0.0_266	) < 1e-3
