





'''simple docstring'''


import copy
from typing import Dict, List, Optional

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


_SCREAMING_SNAKE_CASE									=				{
    "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
}

_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "mask2former"
 __lowerCamelCase    :				Optional[Any]								=       ["swin"]
 __lowerCamelCase    :				str								=       {"hidden_size": "hidden_dim"}




 def __init__(						self      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = 256      ,  _lowerCAmelCase = 256      ,  _lowerCAmelCase = 256      ,  _lowerCAmelCase = 1024      ,  _lowerCAmelCase = "relu"      ,  _lowerCAmelCase = 6      ,  _lowerCAmelCase = 10      ,  _lowerCAmelCase = 8      ,  _lowerCAmelCase = 0.0      ,  _lowerCAmelCase = 2048      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = 4      ,  _lowerCAmelCase = 255      ,  _lowerCAmelCase = 100      ,  _lowerCAmelCase = 0.1      ,  _lowerCAmelCase = 2.0      ,  _lowerCAmelCase = 5.0      ,  _lowerCAmelCase = 5.0      ,  _lowerCAmelCase = 12544      ,  _lowerCAmelCase = 3.0      ,  _lowerCAmelCase = 0.75      ,  _lowerCAmelCase = 0.02      ,  _lowerCAmelCase = 1.0      ,  _lowerCAmelCase = True      ,  _lowerCAmelCase = [4, 8, 16, 32]      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				List[Any]:
     if backbone_config is None:
         logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."		)
         _lowerCAmelCase						      =   CONFIG_MAPPING["swin"](
             image_size=224      ,  in_channels=3      ,  patch_size=4      ,  embed_dim=96      ,  depths=[2, 2, 18, 2]      ,  num_heads=[3, 6, 12, 24]      ,  window_size=7      ,  drop_path_rate=0.3      ,  use_absolute_embeddings=_lowerCAmelCase      ,  out_features=["stage1", "stage2", "stage3", "stage4"]      ,  )

     if isinstance(_lowerCAmelCase      ,  _lowerCAmelCase		):
         _lowerCAmelCase						      =   backbone_config.pop("model_type"		)
         _lowerCAmelCase						      =   CONFIG_MAPPING[backbone_model_type]
         _lowerCAmelCase						      =   config_class.from_dict(_lowerCAmelCase		)

     # 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		)}'''		)

     _lowerCAmelCase						      =   backbone_config
     _lowerCAmelCase						      =   feature_size
     _lowerCAmelCase						      =   mask_feature_size
     _lowerCAmelCase						      =   hidden_dim
     _lowerCAmelCase						      =   encoder_feedforward_dim
     _lowerCAmelCase						      =   activation_function
     _lowerCAmelCase						      =   encoder_layers
     _lowerCAmelCase						      =   decoder_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   dropout
     _lowerCAmelCase						      =   dim_feedforward
     _lowerCAmelCase						      =   pre_norm
     _lowerCAmelCase						      =   enforce_input_projection
     _lowerCAmelCase						      =   common_stride
     _lowerCAmelCase						      =   ignore_value
     _lowerCAmelCase						      =   num_queries
     _lowerCAmelCase						      =   no_object_weight
     _lowerCAmelCase						      =   class_weight
     _lowerCAmelCase						      =   mask_weight
     _lowerCAmelCase						      =   dice_weight
     _lowerCAmelCase						      =   train_num_points
     _lowerCAmelCase						      =   oversample_ratio
     _lowerCAmelCase						      =   importance_sample_ratio
     _lowerCAmelCase						      =   init_std
     _lowerCAmelCase						      =   init_xavier_std
     _lowerCAmelCase						      =   use_auxiliary_loss
     _lowerCAmelCase						      =   feature_strides
     _lowerCAmelCase						      =   output_auxiliary_logits
     _lowerCAmelCase						      =   decoder_layers

     super().__init__(**_lowerCAmelCase		)




 @classmethod
 def 				_snake_case (						cls      ,  _lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				Dict:
     return cls(
         backbone_config=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )





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









'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Any								=       (DDPMParallelScheduler,)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "variance_type": "fixed_small",
         "clip_sample": True,
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self		)     ->				List[Any]:
     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      ,  [0.002, 0.02, 0.2, 2]		):
         self.check_over_configs(beta_start=_lowerCAmelCase      ,  beta_end=_lowerCAmelCase		)




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




 def 				_snake_case (						self		)     ->				Optional[Any]:
     for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=_lowerCAmelCase		)




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




 def 				_snake_case (						self		)     ->				int:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     for t in [0, 500, 999]:
         self.check_over_forward(time_step=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.00979		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     _lowerCAmelCase						      =   self.dummy_sample_deter + 0.1
     _lowerCAmelCase						      =   self.dummy_sample_deter - 0.1

     _lowerCAmelCase						      =   samplea.shape[0]
     _lowerCAmelCase						      =   torch.stack([samplea, samplea, samplea]      ,  dim=0		)
     _lowerCAmelCase						      =   torch.arange(_lowerCAmelCase		)[0:3, None].repeat(1      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   model(samples.flatten(0      ,  1		)      ,  timesteps.flatten(0      ,  1		)		)
     _lowerCAmelCase						      =   scheduler.batch_step_no_noise(_lowerCAmelCase      ,  timesteps.flatten(0      ,  1		)      ,  samples.flatten(0      ,  1		)		)

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 1153.1833		) < 1E-2
     assert abs(result_mean.item() - 0.5005		) < 1E-3




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     _lowerCAmelCase						      =   torch.manual_seed(0		)

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

         # 2. predict previous mean of sample x_t-1
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  generator=_lowerCAmelCase		).prev_sample

         _lowerCAmelCase						      =   pred_prev_sample

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 258.9606		) < 1E-2
     assert abs(result_mean.item() - 0.3372		) < 1E-3




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     _lowerCAmelCase						      =   torch.manual_seed(0		)

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

         # 2. predict previous mean of sample x_t-1
         _lowerCAmelCase						      =   scheduler.step(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  generator=_lowerCAmelCase		).prev_sample

         _lowerCAmelCase						      =   pred_prev_sample

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 202.0296		) < 1E-2
     assert abs(result_mean.item() - 0.2631		) < 1E-3




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=_lowerCAmelCase		)

     _lowerCAmelCase						      =   scheduler.timesteps

     for i, timestep in enumerate(_lowerCAmelCase		):
         if i == len(_lowerCAmelCase		) - 1:
             _lowerCAmelCase						      =   -1
         else:
             _lowerCAmelCase						      =   timesteps[i + 1]

         _lowerCAmelCase						      =   scheduler.previous_timestep(_lowerCAmelCase		)
         _lowerCAmelCase						      =   prev_t.item()

         self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 51, 0]

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




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 1, 0]
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

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





 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [scheduler.config.num_train_timesteps]

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


