
"""simple docstring"""







import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   (CMStochasticIterativeScheduler,)
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   10





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						**snake_case_   :							Union[str, Any]    ):
												__snake_case     			=       {
												    "num_train_timesteps": 201,
												    "sigma_min": 0.002,
												    "sigma_max": 80.0,
												}

												config.update(**snake_case_    )
												return config





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       10

												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       self.scheduler_classes[0](**snake_case_    )

												scheduler.set_timesteps(snake_case_    )

												__snake_case     			=       scheduler.timesteps[0]
												__snake_case     			=       scheduler.timesteps[1]

												__snake_case     			=       self.dummy_sample
												__snake_case     			=       0.1 * sample

												__snake_case     			=       scheduler.step(snake_case_							,						snake_case_							,						snake_case_    ).prev_sample
												__snake_case     			=       scheduler.step(snake_case_							,						snake_case_							,						snake_case_    ).prev_sample

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





					def 							lowerCAmelCase      (				self   :							int    ):
												for timesteps in [10, 50, 100, 1000]:
																			self.check_over_configs(num_train_timesteps=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												for clip_denoised in [True, False]:
																			self.check_over_configs(clip_denoised=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**snake_case_    )

												__snake_case     			=       1
												scheduler.set_timesteps(snake_case_    )
												__snake_case     			=       scheduler.timesteps

												__snake_case     			=       torch.manual_seed(0    )

												__snake_case     			=       self.dummy_model()
												__snake_case     			=       self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(snake_case_    ):
																			# 1. scale model input
																			__snake_case     			=       scheduler.scale_model_input(snake_case_							,						snake_case_    )

																			# 2. predict noise residual
																			__snake_case     			=       model(snake_case_							,						snake_case_    )

																			# 3. predict previous sample x_t-1
																			__snake_case     			=       scheduler.step(snake_case_							,						snake_case_							,						snake_case_							,						generator=snake_case_    ).prev_sample

																			__snake_case     			=       pred_prev_sample

												__snake_case     			=       torch.sum(torch.abs(snake_case_    )    )
												__snake_case     			=       torch.mean(torch.abs(snake_case_    )    )

												assert abs(result_sum.item() - 192.7614    ) < 1e-2
												assert abs(result_mean.item() - 0.2510    ) < 1e-3





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**snake_case_    )

												__snake_case     			=       [106, 0]
												scheduler.set_timesteps(timesteps=snake_case_    )
												__snake_case     			=       scheduler.timesteps

												__snake_case     			=       torch.manual_seed(0    )

												__snake_case     			=       self.dummy_model()
												__snake_case     			=       self.dummy_sample_deter * scheduler.init_noise_sigma

												for t in timesteps:
																			# 1. scale model input
																			__snake_case     			=       scheduler.scale_model_input(snake_case_							,						snake_case_    )

																			# 2. predict noise residual
																			__snake_case     			=       model(snake_case_							,						snake_case_    )

																			# 3. predict previous sample x_t-1
																			__snake_case     			=       scheduler.step(snake_case_							,						snake_case_							,						snake_case_							,						generator=snake_case_    ).prev_sample

																			__snake_case     			=       pred_prev_sample

												__snake_case     			=       torch.sum(torch.abs(snake_case_    )    )
												__snake_case     			=       torch.mean(torch.abs(snake_case_    )    )

												assert abs(result_sum.item() - 347.6357    ) < 1e-2
												assert abs(result_mean.item() - 0.4527    ) < 1e-3





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**snake_case_    )

												__snake_case     			=       [39, 30, 12, 15, 0]

												with self.assertRaises(snake_case_							,						msg="`timesteps` must be in descending order."    ):
																			scheduler.set_timesteps(timesteps=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**snake_case_    )

												__snake_case     			=       [39, 30, 12, 1, 0]
												__snake_case     			=       len(snake_case_    )

												with self.assertRaises(snake_case_							,						msg="Can only pass one of `num_inference_steps` or `timesteps`."    ):
																			scheduler.set_timesteps(num_inference_steps=snake_case_							,						timesteps=snake_case_    )





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**snake_case_    )

												__snake_case     			=       [scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    snake_case_							,						msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"							,						):
																			scheduler.set_timesteps(timesteps=snake_case_    )


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """GroupViTModel""",
		    """GroupViTPreTrainedModel""",
		    """GroupViTTextModel""",
		    """GroupViTVisionModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFGroupViTModel""",
		    """TFGroupViTPreTrainedModel""",
		    """TFGroupViTTextModel""",
		    """TFGroupViTVisionModel""",
		]

if TYPE_CHECKING:
		from .configuration_groupvit import (
		    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    GroupViTConfig,
		    GroupViTOnnxConfig,
		    GroupViTTextConfig,
		    GroupViTVisionConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_groupvit import (
				    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GroupViTModel,
				    GroupViTPreTrainedModel,
				    GroupViTTextModel,
				    GroupViTVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_groupvit import (
				    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFGroupViTModel,
				    TFGroupViTPreTrainedModel,
				    TFGroupViTTextModel,
				    TFGroupViTVisionModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
