





'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 					UpperCAmelCase	(     a_  ):
      _lowercase:     Tuple			=  (IPNDMScheduler,)
      _lowercase:     Any			=  (('''num_inference_steps''', 50),)




      def 							lowercase__       (     self     :					Union[str, Any]     ,     **__snake_case     :					List[str]    )							-> Optional[Any]:
            _lowerCAmelCase					=				{"""num_train_timesteps""": 10_00}
            config.update(**__SCREAMING_SNAKE_CASE    )
            return config




      def 							lowercase__       (     self     :					List[str]     ,     __snake_case     :					Any=0     ,     **__snake_case     :					List[str]    )							-> Tuple:
            _lowerCAmelCase					=				dict(self.forward_default_kwargs    )
            _lowerCAmelCase					=				kwargs.pop("""num_inference_steps"""     ,     __SCREAMING_SNAKE_CASE    )
            _lowerCAmelCase					=				self.dummy_sample
            _lowerCAmelCase					=				0.1 * sample
            _lowerCAmelCase					=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

                  _lowerCAmelCase					=				scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample
                  _lowerCAmelCase					=				new_scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample

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

                  _lowerCAmelCase					=				scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample
                  _lowerCAmelCase					=				new_scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample

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




      def 							lowercase__       (     self     :					Any    )							-> Union[str, Any]:
            pass




      def 							lowercase__       (     self     :					Any     ,     __snake_case     :					Tuple=0     ,     **__snake_case     :					Optional[int]    )							-> Optional[int]:
            _lowerCAmelCase					=				dict(self.forward_default_kwargs    )
            _lowerCAmelCase					=				kwargs.pop("""num_inference_steps"""     ,     __SCREAMING_SNAKE_CASE    )
            _lowerCAmelCase					=				self.dummy_sample
            _lowerCAmelCase					=				0.1 * sample
            _lowerCAmelCase					=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

            for scheduler_class in self.scheduler_classes:
                  _lowerCAmelCase					=				self.get_scheduler_config()
                  _lowerCAmelCase					=				scheduler_class(**__SCREAMING_SNAKE_CASE    )
                  scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    )

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

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

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

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

                  _lowerCAmelCase					=				scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample
                  _lowerCAmelCase					=				new_scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample

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

                  _lowerCAmelCase					=				scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample
                  _lowerCAmelCase					=				new_scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample

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




      def 							lowercase__       (     self     :					List[Any]     ,     **__snake_case     :					Optional[Any]    )							-> Any:
            _lowerCAmelCase					=				self.scheduler_classes[0]
            _lowerCAmelCase					=				self.get_scheduler_config(**__SCREAMING_SNAKE_CASE    )
            _lowerCAmelCase					=				scheduler_class(**__SCREAMING_SNAKE_CASE    )

            _lowerCAmelCase					=				10
            _lowerCAmelCase					=				self.dummy_model()
            _lowerCAmelCase					=				self.dummy_sample_deter
            scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    )

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

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

            return sample




      def 							lowercase__       (     self     :					int    )							-> Optional[Any]:
            _lowerCAmelCase					=				dict(self.forward_default_kwargs    )

            _lowerCAmelCase					=				kwargs.pop("""num_inference_steps"""     ,     __SCREAMING_SNAKE_CASE    )

            for scheduler_class in self.scheduler_classes:
                  _lowerCAmelCase					=				self.get_scheduler_config()
                  _lowerCAmelCase					=				scheduler_class(**__SCREAMING_SNAKE_CASE    )

                  _lowerCAmelCase					=				self.dummy_sample
                  _lowerCAmelCase					=				0.1 * sample

                  if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE     ,     """set_timesteps"""    ):
                        scheduler.set_timesteps(__SCREAMING_SNAKE_CASE    )
                  elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE     ,     """set_timesteps"""    ):
                        _lowerCAmelCase					=				num_inference_steps

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

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

                  _lowerCAmelCase					=				scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample
                  _lowerCAmelCase					=				scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample

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

                  _lowerCAmelCase					=				scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample
                  _lowerCAmelCase					=				scheduler.step(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE    ).prev_sample

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




      def 							lowercase__       (     self     :					str    )							-> Any:
            for timesteps in [1_00, 10_00]:
                  self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE     ,     time_step=__SCREAMING_SNAKE_CASE    )




      def 							lowercase__       (     self     :					Dict    )							-> List[str]:
            for t, num_inference_steps in zip([1, 5, 10]     ,     [10, 50, 1_00]    ):
                  self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE     ,     time_step=__SCREAMING_SNAKE_CASE    )




      def 							lowercase__       (     self     :					Any    )							-> int:
            _lowerCAmelCase					=				self.full_loop()
            _lowerCAmelCase					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE    )    )

            assert abs(result_mean.item() - 2_54_05_29    ) < 10







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


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """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_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

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

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

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __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 Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


