



'''simple docstring'''





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						(DPMSolverSDEScheduler,)
    lowercase   =						10




    def lowerCamelCase			(       self   :	Dict					,	**snake_case_   :	Optional[int]		):
          snake_case__    :       List[Any]       		= {
              """num_train_timesteps""": 1_100,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """noise_sampler_seed""": 0,
          }

          config.update(**snake_case_		)
          return config




    def lowerCamelCase			(       self   :	int		):
          for timesteps in [10, 50, 100, 1_000]:
                self.check_over_configs(num_train_timesteps=snake_case_		)




    def lowerCamelCase			(       self   :	Any		):
          for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,	[0.0002, 0.002, 0.02]		):
                self.check_over_configs(beta_start=snake_case_					,	beta_end=snake_case_		)




    def lowerCamelCase			(       self   :	List[str]		):
          for schedule in ["linear", "scaled_linear"]:
                self.check_over_configs(beta_schedule=snake_case_		)




    def lowerCamelCase			(       self   :	List[Any]		):
          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=snake_case_		)




    def lowerCamelCase			(       self   :	Any		):
          snake_case__    :       str       		= self.scheduler_classes[0]
          snake_case__    :       str       		= self.get_scheduler_config()
          snake_case__    :       Tuple       		= scheduler_class(**snake_case_		)

          scheduler.set_timesteps(self.num_inference_steps		)

          snake_case__    :       Tuple       		= self.dummy_model()
          snake_case__    :       List[Any]       		= self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__    :       int       		= sample.to(snake_case_		)

          for i, t in enumerate(scheduler.timesteps		):
                snake_case__    :       Dict       		= scheduler.scale_model_input(snake_case_					,	snake_case_		)

                snake_case__    :       Optional[int]       		= model(snake_case_					,	snake_case_		)

                snake_case__    :       str       		= scheduler.step(snake_case_					,	snake_case_					,	snake_case_		)
                snake_case__    :       Any       		= output.prev_sample

          snake_case__    :       Dict       		= torch.sum(torch.abs(snake_case_		)		)
          snake_case__    :       Optional[int]       		= torch.mean(torch.abs(snake_case_		)		)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 167.47821044921875		) < 1E-2
                assert abs(result_mean.item() - 0.2178705964565277		) < 1E-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 171.59352111816406		) < 1E-2
                assert abs(result_mean.item() - 0.22342906892299652		) < 1E-3
          else:
                assert abs(result_sum.item() - 162.52383422851562		) < 1E-2
                assert abs(result_mean.item() - 0.211619570851326		) < 1E-3




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Any       		= self.scheduler_classes[0]
          snake_case__    :       Optional[int]       		= self.get_scheduler_config(prediction_type="""v_prediction"""		)
          snake_case__    :       List[Any]       		= scheduler_class(**snake_case_		)

          scheduler.set_timesteps(self.num_inference_steps		)

          snake_case__    :       Dict       		= self.dummy_model()
          snake_case__    :       Optional[int]       		= self.dummy_sample_deter * scheduler.init_noise_sigma
          snake_case__    :       int       		= sample.to(snake_case_		)

          for i, t in enumerate(scheduler.timesteps		):
                snake_case__    :       Optional[int]       		= scheduler.scale_model_input(snake_case_					,	snake_case_		)

                snake_case__    :       Any       		= model(snake_case_					,	snake_case_		)

                snake_case__    :       Dict       		= scheduler.step(snake_case_					,	snake_case_					,	snake_case_		)
                snake_case__    :       Optional[int]       		= output.prev_sample

          snake_case__    :       str       		= torch.sum(torch.abs(snake_case_		)		)
          snake_case__    :       Tuple       		= torch.mean(torch.abs(snake_case_		)		)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 124.77149200439453		) < 1E-2
                assert abs(result_mean.item() - 0.16226289014816284		) < 1E-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 128.1663360595703		) < 1E-2
                assert abs(result_mean.item() - 0.16688326001167297		) < 1E-3
          else:
                assert abs(result_sum.item() - 119.8487548828125		) < 1E-2
                assert abs(result_mean.item() - 0.1560530662536621		) < 1E-3




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       int       		= self.scheduler_classes[0]
          snake_case__    :       List[str]       		= self.get_scheduler_config()
          snake_case__    :       Any       		= scheduler_class(**snake_case_		)

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

          snake_case__    :       Optional[int]       		= self.dummy_model()
          snake_case__    :       List[Any]       		= self.dummy_sample_deter.to(snake_case_		) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                snake_case__    :       Tuple       		= scheduler.scale_model_input(snake_case_					,	snake_case_		)

                snake_case__    :       str       		= model(snake_case_					,	snake_case_		)

                snake_case__    :       Dict       		= scheduler.step(snake_case_					,	snake_case_					,	snake_case_		)
                snake_case__    :       Tuple       		= output.prev_sample

          snake_case__    :       List[str]       		= torch.sum(torch.abs(snake_case_		)		)
          snake_case__    :       Any       		= torch.mean(torch.abs(snake_case_		)		)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 167.46957397460938		) < 1E-2
                assert abs(result_mean.item() - 0.21805934607982635		) < 1E-3
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 171.59353637695312		) < 1E-2
                assert abs(result_mean.item() - 0.22342908382415771		) < 1E-3
          else:
                assert abs(result_sum.item() - 162.52383422851562		) < 1E-2
                assert abs(result_mean.item() - 0.211619570851326		) < 1E-3




    def lowerCamelCase			(       self   :	Optional[int]		):
          snake_case__    :       Union[str, Any]       		= self.scheduler_classes[0]
          snake_case__    :       int       		= self.get_scheduler_config()
          snake_case__    :       int       		= scheduler_class(**snake_case_					,	use_karras_sigmas=snake_case_		)

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

          snake_case__    :       Optional[Any]       		= self.dummy_model()
          snake_case__    :       Any       		= self.dummy_sample_deter.to(snake_case_		) * scheduler.init_noise_sigma
          snake_case__    :       str       		= sample.to(snake_case_		)

          for t in scheduler.timesteps:
                snake_case__    :       Any       		= scheduler.scale_model_input(snake_case_					,	snake_case_		)

                snake_case__    :       Optional[Any]       		= model(snake_case_					,	snake_case_		)

                snake_case__    :       Any       		= scheduler.step(snake_case_					,	snake_case_					,	snake_case_		)
                snake_case__    :       Optional[int]       		= output.prev_sample

          snake_case__    :       Optional[Any]       		= torch.sum(torch.abs(snake_case_		)		)
          snake_case__    :       Dict       		= torch.mean(torch.abs(snake_case_		)		)

          if torch_device in ["mps"]:
                assert abs(result_sum.item() - 176.66974135742188		) < 1E-2
                assert abs(result_mean.item() - 0.23003872730981811		) < 1E-2
          elif torch_device in ["cuda"]:
                assert abs(result_sum.item() - 177.63653564453125		) < 1E-2
                assert abs(result_mean.item() - 0.23003872730981811		) < 1E-2
          else:
                assert abs(result_sum.item() - 170.3135223388672		) < 1E-2
                assert abs(result_mean.item() - 0.23003872730981811		) < 1E-2





'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}







class        UpperCAmelCase_			(			_a						,						_a      ):
    """simple docstring"""







    lowercase   =						"dinat"

    lowercase   =						{
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }




    def __init__(       self   :	Optional[int]					,	snake_case_   :	Optional[Any]=4					,	snake_case_   :	List[str]=3					,	snake_case_   :	Tuple=64					,	snake_case_   :	List[Any]=[3, 4, 6, 5]					,	snake_case_   :	List[Any]=[2, 4, 8, 16]					,	snake_case_   :	List[str]=7					,	snake_case_   :	List[str]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]					,	snake_case_   :	List[Any]=3.0					,	snake_case_   :	Dict=True					,	snake_case_   :	int=0.0					,	snake_case_   :	Optional[int]=0.0					,	snake_case_   :	str=0.1					,	snake_case_   :	Optional[Any]="gelu"					,	snake_case_   :	Union[str, Any]=0.02					,	snake_case_   :	Optional[int]=1E-5					,	snake_case_   :	str=0.0					,	snake_case_   :	Dict=None					,	snake_case_   :	Dict=None					,	**snake_case_   :	Dict					,	):
          super().__init__(**snake_case_		)

          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Union[str, Any]       		= num_channels
          snake_case__    :       int       		= embed_dim
          snake_case__    :       Dict       		= depths
          snake_case__    :       Dict       		= len(snake_case_		)
          snake_case__    :       Optional[int]       		= num_heads
          snake_case__    :       Any       		= kernel_size
          snake_case__    :       str       		= dilations
          snake_case__    :       Any       		= mlp_ratio
          snake_case__    :       int       		= qkv_bias
          snake_case__    :       int       		= hidden_dropout_prob
          snake_case__    :       int       		= attention_probs_dropout_prob
          snake_case__    :       Optional[Any]       		= drop_path_rate
          snake_case__    :       int       		= hidden_act
          snake_case__    :       Tuple       		= layer_norm_eps
          snake_case__    :       Any       		= initializer_range
          # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          snake_case__    :       Optional[Any]       		= int(embed_dim * 2 ** (len(snake_case_		) - 1)		)
          snake_case__    :       int       		= layer_scale_init_value
          snake_case__    :       Dict       		= ["""stem"""] + [f"stage{idx}" for idx in range(1					,	len(snake_case_		) + 1		)]
          snake_case__		,							snake_case__    :       Tuple       		= get_aligned_output_features_output_indices(
              out_features=snake_case_					,	out_indices=snake_case_					,	stage_names=self.stage_names		)
