






import copy
from typing import Dict, List, Optional

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


__a:     Tuple   					=					{
    '''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
}

__a:     Optional[Any]   					=					logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  '''mask2former'''
  _lowerCamelCase    =  ['''swin''']
  _lowerCamelCase    =  {'''hidden_size''': '''hidden_dim'''}


  def __init__(      self		:			Tuple       ,				lowerCamelCase		:			Optional[Dict] = None       ,				lowerCamelCase		:			int = 256       ,				lowerCamelCase		:			int = 256       ,				lowerCamelCase		:			int = 256       ,				lowerCamelCase		:			int = 1024       ,				lowerCamelCase		:			str = "relu"       ,				lowerCamelCase		:			int = 6       ,				lowerCamelCase		:			int = 10       ,				lowerCamelCase		:			int = 8       ,				lowerCamelCase		:			float = 0.0       ,				lowerCamelCase		:			int = 2048       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			int = 4       ,				lowerCamelCase		:			int = 255       ,				lowerCamelCase		:			int = 100       ,				lowerCamelCase		:			float = 0.1       ,				lowerCamelCase		:			float = 2.0       ,				lowerCamelCase		:			float = 5.0       ,				lowerCamelCase		:			float = 5.0       ,				lowerCamelCase		:			int = 1_2544       ,				lowerCamelCase		:			float = 3.0       ,				lowerCamelCase		:			float = 0.75       ,				lowerCamelCase		:			float = 0.02       ,				lowerCamelCase		:			float = 1.0       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			List[int] = [4, 8, 16, 32]       ,				lowerCamelCase		:			bool = None       ,				**lowerCamelCase		:			Optional[Any]       ,				)						->	Tuple:


         """simple docstring"""
         if backbone_config is None:
                logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""							)
                _UpperCAmelCase							=    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							):
                _UpperCAmelCase							=    backbone_config.pop("""model_type"""							)
                _UpperCAmelCase							=    CONFIG_MAPPING[backbone_model_type]
                _UpperCAmelCase							=    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							)}"""							)

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

         super().__init__(**lowerCamelCase							)


  @classmethod
  def  lowerCamelCase       (      cls		:			Union[str, Any]       ,				lowerCamelCase		:			PretrainedConfig       ,				**lowerCamelCase		:			Optional[Any]							)						->	Union[str, Any]:


         """simple docstring"""
         return cls(
             backbone_config=lowerCamelCase       ,				**lowerCamelCase       ,				)





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


         """simple docstring"""
         _UpperCAmelCase							=    copy.deepcopy(self.__dict__							)
         _UpperCAmelCase							=    self.backbone_config.to_dict()
         _UpperCAmelCase							=    self.__class__.model_type
         return output







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			(IPNDMScheduler,)
				lowercase__											=			(("""num_inference_steps""", 50),)




				def 							lowercase__  (	self		:       Union[str, Any],    **lowerCamelCase		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     {'''num_train_timesteps''': 1_000}
								config.update(**lowerCamelCase    )
								return config




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Any=0,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )
												# copy over dummy past residuals
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     scheduler_class.from_pretrained(lowerCamelCase    )
																new_scheduler.set_timesteps(lowerCamelCase    )
																# copy over dummy past residuals
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       Optional[int]=0,    **lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )
								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )
								lowercase__  					=     self.dummy_sample
								lowercase__  					=     0.1 * sample
								lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )
												scheduler.set_timesteps(lowerCamelCase    )

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase__  					=     dummy_past_residuals[:]

												if time_step is None:
																lowercase__  					=     scheduler.timesteps[len(scheduler.timesteps    ) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCamelCase    )
																lowercase__  					=     scheduler_class.from_pretrained(lowerCamelCase    )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(lowerCamelCase    )

																# copy over dummy past residual (must be after setting timesteps)
																lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     new_scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

												assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"




				def 							lowercase__  (	self		:       int,    **lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.scheduler_classes[0]
								lowercase__  					=     self.get_scheduler_config(**lowerCamelCase    )
								lowercase__  					=     scheduler_class(**lowerCamelCase    )

								lowercase__  					=     10
								lowercase__  					=     self.dummy_model()
								lowercase__  					=     self.dummy_sample_deter
								scheduler.set_timesteps(lowerCamelCase    )

								for i, t in enumerate(scheduler.timesteps    ):
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

								for i, t in enumerate(scheduler.timesteps    ):
												lowercase__  					=     model(lowerCamelCase,    lowerCamelCase    )
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    ).prev_sample

								return sample




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     dict(self.forward_default_kwargs    )

								lowercase__  					=     kwargs.pop('''num_inference_steps''',    lowerCamelCase    )

								for scheduler_class in self.scheduler_classes:
												lowercase__  					=     self.get_scheduler_config()
												lowercase__  					=     scheduler_class(**lowerCamelCase    )

												lowercase__  					=     self.dummy_sample
												lowercase__  					=     0.1 * sample

												if num_inference_steps is not None and hasattr(lowerCamelCase,    '''set_timesteps'''    ):
																scheduler.set_timesteps(lowerCamelCase    )
												elif num_inference_steps is not None and not hasattr(lowerCamelCase,    '''set_timesteps'''    ):
																lowercase__  					=     num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												lowercase__  					=     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												lowercase__  					=     dummy_past_residuals[:]

												lowercase__  					=     scheduler.timesteps[5]
												lowercase__  					=     scheduler.timesteps[6]

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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

												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample
												lowercase__  					=     scheduler.step(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    **lowerCamelCase    ).prev_sample

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




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								for timesteps in [100, 1_000]:
												self.check_over_configs(num_train_timesteps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								for t, num_inference_steps in zip([1, 5, 10],    [10, 50, 100]    ):
												self.check_over_forward(num_inference_steps=lowerCamelCase,    time_step=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.full_loop()
								lowercase__  					=     torch.mean(torch.abs(lowerCamelCase    )    )

								assert abs(result_mean.item() - 2_540_529    ) < 10




