





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


a__:      List[str]									=		logging.get_logger(__name__)

a__:      Tuple									=		{
    '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     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	'''dinat'''

 __SCREAMING_SNAKE_CASE							       =	{
     '''num_attention_heads''': '''num_heads''',
     '''num_hidden_layers''': '''num_layers''',
 }



 def __init__(							self,__lowerCamelCase=4,__lowerCamelCase=3,__lowerCamelCase=64,__lowerCamelCase=[3, 4, 6, 5],__lowerCamelCase=[2, 4, 8, 16],__lowerCamelCase=7,__lowerCamelCase=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]],__lowerCamelCase=3.0,__lowerCamelCase=True,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.1,__lowerCamelCase="gelu",__lowerCamelCase=0.02,__lowerCamelCase=1E-5,__lowerCamelCase=0.0,__lowerCamelCase=None,__lowerCamelCase=None,**__lowerCamelCase,):
        super().__init__(**__lowerCamelCase )

        A__											=						patch_size
        A__											=						num_channels
        A__											=						embed_dim
        A__											=						depths
        A__											=						len(__lowerCamelCase )
        A__											=						num_heads
        A__											=						kernel_size
        A__											=						dilations
        A__											=						mlp_ratio
        A__											=						qkv_bias
        A__											=						hidden_dropout_prob
        A__											=						attention_probs_dropout_prob
        A__											=						drop_path_rate
        A__											=						hidden_act
        A__											=						layer_norm_eps
        A__											=						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
        A__											=						int(embed_dim * 2 ** (len(__lowerCamelCase ) - 1) )
        A__											=						layer_scale_init_value
        A__											=						['''stem'''] + [f"stage{idx}" for idx in range(1,len(__lowerCamelCase ) + 1 )]
        A__					,	A__											=						get_aligned_output_features_output_indices(
            out_features=__lowerCamelCase,out_indices=__lowerCamelCase,stage_names=self.stage_names )












import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DDIMParallelScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''eta''', 0.0), ('''num_inference_steps''', 50))



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''clip_sample''': True,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0

        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter

        scheduler.set_timesteps(__lowerCamelCase )

        for t in scheduler.timesteps:
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        for timesteps in [100, 500, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(steps_offset=1 )
        A__											=						scheduler_class(**__lowerCamelCase )
        scheduler.set_timesteps(5 )
        assert torch.equal(scheduler.timesteps,torch.LongTensor([801, 601, 401, 201, 1] ) )



 def 				UpperCamelCase			(							self ):
        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 				UpperCamelCase			(							self ):
        for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                      self.check_over_configs(
                          thresholding=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,)



 def 				UpperCamelCase			(							self ):
        for t in [1, 10, 49]:
               self.check_over_forward(time_step=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, num_inference_steps in zip([1, 10, 50],[10, 50, 500] ):
               self.check_over_forward(time_step=__lowerCamelCase,num_inference_steps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for t, eta in zip([1, 10, 49],[0.0, 0.5, 1.0] ):
               self.check_over_forward(time_step=__lowerCamelCase,eta=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,400 ) - 0.14771 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,960 ) - 0.32460 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,0 ) - 0.0 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,486 ) - 0.00979 ) ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,998 ) - 0.02 ) ) < 1E-5



 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config()
        A__											=						scheduler_class(**__lowerCamelCase )

        A__					,	A__											=						10, 0.0
        scheduler.set_timesteps(__lowerCamelCase )

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

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

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

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 1147.7904 ) < 1E-2
        assert abs(result_mean.item() - 0.4982 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 172.0067 ) < 1E-2
        assert abs(result_mean.item() - 0.223967 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )

        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 52.5302 ) < 1E-2
        assert abs(result_mean.item() - 0.0684 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.8295 ) < 1E-2
        assert abs(result_mean.item() - 0.1951 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        # We specify different beta, so that the first alpha is 0.99
        A__											=						self.full_loop(set_alpha_to_one=__lowerCamelCase,beta_start=0.01 )
        A__											=						torch.sum(torch.abs(__lowerCamelCase ) )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_sum.item() - 149.0784 ) < 1E-2
        assert abs(result_mean.item() - 0.1941 ) < 1E-3





