'''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


_lowerCAmelCase  :Any  				=    {
    """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
}

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






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Dict							=						"maskformer"
    snake_case__							:   Optional[int]							=						{"hidden_size": "mask_feature_size"}
    snake_case__							:   List[Any]							=						["resnet", "swin"]
    snake_case__							:   Dict							=						["detr"]






    def __init__(       self    ,							lowercase__ = 256    ,							lowercase__ = 256    ,							lowercase__ = 0.1    ,							lowercase__ = False    ,							lowercase__ = None    ,							lowercase__ = None    ,							lowercase__ = 0.0_2    ,							lowercase__ = 1.0    ,							lowercase__ = 1.0    ,							lowercase__ = 1.0    ,							lowercase__ = 2_0.0    ,							lowercase__ = None    ,							**lowercase__    ,							)							-> str:
         if backbone_config is None:
              # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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__ ):
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       backbone_config.pop('model_type' )
              SCREAMING_SNAKE_CASE					:			str    	=       CONFIG_MAPPING[backbone_model_type]
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       DetrConfig()
         else:
              # verify that the decoder is supported
              SCREAMING_SNAKE_CASE					:			List[str]    	=       (
                  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__ ):
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       CONFIG_MAPPING[decoder_type]
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       config_class.from_dict(lowercase__ )

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

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.decoder_config.encoder_attention_heads
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.decoder_config.num_hidden_layers
         super().__init__(**lowercase__ )






    @classmethod
    def       _UpperCamelCase    (       cls    ,							lowercase__    ,							lowercase__    ,							**lowercase__ )							-> str:
         return cls(
             backbone_config=lowercase__    ,							decoder_config=lowercase__    ,							**lowercase__    ,							)






    def       _UpperCamelCase    (       self )							-> Dict[str, any]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       copy.deepcopy(self.__dict__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.backbone_config.to_dict()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.decoder_config.to_dict()
         SCREAMING_SNAKE_CASE					:			int    	=       self.__class__.model_type
         return output




'''simple docstring'''





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						(DDPMParallelScheduler,)






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       {
             'num_train_timesteps': 1_000,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**lowercase__ )
         return config






    def       _UpperCamelCase    (       self )							-> int:
         for timesteps in [1, 5, 100, 1_000]:
              self.check_over_configs(num_train_timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]    ,							[0.0_0_2, 0.0_2, 0.2, 2] ):
              self.check_over_configs(beta_start=lowercase__    ,							beta_end=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase__ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         self.check_over_configs(thresholding=lowercase__ )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=lowercase__    ,							prediction_type=lowercase__    ,							sample_max_value=lowercase__    ,							)






    def       _UpperCamelCase    (       self )							-> Dict:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         for t in [0, 500, 999]:
              self.check_over_forward(time_step=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0_9_7_9 ) ) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.0_2 ) ) < 1E-5






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			str    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.dummy_sample_deter + 0.1
         SCREAMING_SNAKE_CASE					:			int    	=       self.dummy_sample_deter - 0.1

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       samplea.shape[0]
         SCREAMING_SNAKE_CASE					:			str    	=       torch.stack([samplea, samplea, samplea]    ,							dim=0 )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.arange(lowercase__ )[0:3, None].repeat(1    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model(samples.flatten(0    ,							1 )    ,							timesteps.flatten(0    ,							1 ) )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler.batch_step_no_noise(lowercase__    ,							timesteps.flatten(0    ,							1 )    ,							samples.flatten(0    ,							1 ) )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 1_1_5_3.1_8_3_3 ) < 1E-2
         assert abs(result_mean.item() - 0.5_0_0_5 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			int    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Dict    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[str]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_5_8.9_6_0_6 ) < 1E-2
         assert abs(result_mean.item() - 0.3_3_7_2 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_scheduler_config(prediction_type='v_prediction' )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       len(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.dummy_model()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.dummy_sample_deter
         SCREAMING_SNAKE_CASE					:			int    	=       torch.manual_seed(0 )

         for t in reversed(range(lowercase__ ) ):
              # 1. predict noise residual
              SCREAMING_SNAKE_CASE					:			Tuple    	=       model(lowercase__    ,							lowercase__ )

              # 2. predict previous mean of sample x_t-1
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							generator=lowercase__ ).prev_sample

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       pred_prev_sample

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.sum(torch.abs(lowercase__ ) )
         SCREAMING_SNAKE_CASE					:			str    	=       torch.mean(torch.abs(lowercase__ ) )

         assert abs(result_sum.item() - 2_0_2.0_2_9_6 ) < 1E-2
         assert abs(result_mean.item() - 0.2_6_3_1 ) < 1E-3






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       scheduler.timesteps

         for i, timestep in enumerate(lowercase__ ):
              if i == len(lowercase__ ) - 1:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       -1
              else:
                   SCREAMING_SNAKE_CASE					:			Any    	=       timesteps[i + 1]

              SCREAMING_SNAKE_CASE					:			str    	=       scheduler.previous_timestep(lowercase__ )
              SCREAMING_SNAKE_CASE					:			str    	=       prev_t.item()

              self.assertEqual(lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       [100, 87, 50, 51, 0]

         with self.assertRaises(lowercase__    ,							msg='`custom_timesteps` must be in descending order.' ):
              scheduler.set_timesteps(timesteps=lowercase__ )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       [100, 87, 50, 1, 0]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       len(lowercase__ )

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






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.get_scheduler_config()
         SCREAMING_SNAKE_CASE					:			Tuple    	=       scheduler_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			Any    	=       [scheduler.config.num_train_timesteps]

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



