


"""simple docstring"""


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


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

__lowerCamelCase      :str	    =		{
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class 			A__	(						__lowercase ,     __lowercase):


     """simple docstring"""
     snake_case__	:					Any													='''nat'''

     snake_case__	:					List[str]													={
         '''num_attention_heads''': '''num_heads''',
         '''num_hidden_layers''': '''num_layers''',
     }

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

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








"""simple docstring"""


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													=(KDPMaDiscreteScheduler,)
     snake_case__	:					Tuple													=10

     def 		a__						(	self:						List[Any]     ,		**__a:						Optional[int]	)->    Union[str, Any]:
           lowerCamelCase					:					int        =	{
               """num_train_timesteps""": 1_100,
               """beta_start""": 0.00_01,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
           }

           config.update(**__a	)
           return config

     def 		a__						(	self:						Union[str, Any]	)->    Any:
           for timesteps in [10, 50, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=__a	)

     def 		a__						(	self:						str	)->    int:
           for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]     ,		[0.00_02, 0.0_02, 0.02]	):
                 self.check_over_configs(beta_start=__a     ,		beta_end=__a	)

     def 		a__						(	self:						int	)->    Union[str, Any]:
           for schedule in ["linear", "scaled_linear"]:
                 self.check_over_configs(beta_schedule=__a	)

     def 		a__						(	self:						List[Any]	)->    List[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__a	)

     def 		a__						(	self:						Union[str, Any]	)->    int:
           lowerCamelCase					:					List[str]        =	self.scheduler_classes[0]
           lowerCamelCase					:					Union[str, Any]        =	self.get_scheduler_config(prediction_type="""v_prediction"""	)
           lowerCamelCase					:					List[str]        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCamelCase					:					Dict        =	self.dummy_model()
           lowerCamelCase					:					List[Any]        =	self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase					:					List[Any]        =	sample.to(__a	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase					:					Optional[Any]        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[int]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					Tuple        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					Optional[Any]        =	output.prev_sample

           lowerCamelCase					:					List[str]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Tuple        =	torch.mean(torch.abs(__a	)	)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.6_9_3_4e-0_7	) < 1e-2
                 assert abs(result_mean.item() - 6.1_1_1_2e-1_0	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7	) < 1e-2
                 assert abs(result_mean.item() - 0.00_02	) < 1e-3

     def 		a__						(	self:						Any	)->    Any:
           if torch_device == "mps":
                 return
           lowerCamelCase					:					Dict        =	self.scheduler_classes[0]
           lowerCamelCase					:					Dict        =	self.get_scheduler_config()
           lowerCamelCase					:					int        =	scheduler_class(**__a	)

           scheduler.set_timesteps(self.num_inference_steps	)

           lowerCamelCase					:					List[Any]        =	self.dummy_model()
           lowerCamelCase					:					Optional[Any]        =	self.dummy_sample_deter * scheduler.init_noise_sigma
           lowerCamelCase					:					Optional[int]        =	sample.to(__a	)

           for i, t in enumerate(scheduler.timesteps	):
                 lowerCamelCase					:					Dict        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[Any]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					Tuple        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					str        =	output.prev_sample

           lowerCamelCase					:					Tuple        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					Tuple        =	torch.mean(torch.abs(__a	)	)

           if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3







     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           if torch_device == "mps":
                 return
           lowerCamelCase					:					Any        =	self.scheduler_classes[0]
           lowerCamelCase					:					Union[str, Any]        =	self.get_scheduler_config()
           lowerCamelCase					:					Optional[Any]        =	scheduler_class(**__a	)

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

           lowerCamelCase					:					Union[str, Any]        =	self.dummy_model()
           lowerCamelCase					:					List[str]        =	self.dummy_sample_deter.to(__a	) * scheduler.init_noise_sigma

           for t in scheduler.timesteps:
                 lowerCamelCase					:					Union[str, Any]        =	scheduler.scale_model_input(__a     ,		__a	)

                 lowerCamelCase					:					Optional[int]        =	model(__a     ,		__a	)

                 lowerCamelCase					:					int        =	scheduler.step(__a     ,		__a     ,		__a	)
                 lowerCamelCase					:					int        =	output.prev_sample

           lowerCamelCase					:					Union[str, Any]        =	torch.sum(torch.abs(__a	)	)
           lowerCamelCase					:					int        =	torch.mean(torch.abs(__a	)	)

           if str(__a	).startswith("""cpu"""	):
                 # The following sum varies between 148 and 156 on mps. Why?
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3
           else:
                 # CUDA
                 assert abs(result_sum.item() - 20.41_25	) < 1e-2
                 assert abs(result_mean.item() - 0.02_66	) < 1e-3




