






"""simple docstring"""





import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class   _lowercase			(  lowerCAmelCase    ):
      _a :  List[str]										= (DDIMScheduler,)
      _a :  List[str]										= (('''eta''', 0.0), ('''num_inference_steps''', 50))


      def _UpperCamelCase    (     self					:						Any				, **a					:						Dict    ):





            """simple docstring"""







            __snake_case   :     List[str]	      ={
                '''num_train_timesteps''': 1_0_0_0,
                '''beta_start''': 0.0_0_0_1,
                '''beta_end''': 0.0_2,
                '''beta_schedule''': '''linear''',
                '''clip_sample''': True,
            }

            config.update(**a    )
            return config


      def _UpperCamelCase    (     self					:						Union[str, Any]				, **a					:						List[Any]    ):





            """simple docstring"""







            __snake_case   :     Dict	      =self.scheduler_classes[0]
            __snake_case   :     Optional[Any]	      =self.get_scheduler_config(**a    )
            __snake_case   :     List[str]	      =scheduler_class(**a    )

            __snake_case      ,					__snake_case   :     Optional[Any]	      =1_0, 0.0

            __snake_case   :     str	      =self.dummy_model()
            __snake_case   :     Optional[int]	      =self.dummy_sample_deter

            scheduler.set_timesteps(a    )

            for t in scheduler.timesteps:
                  __snake_case   :     Union[str, Any]	      =model(a				, a    )
                  __snake_case   :     Optional[int]	      =scheduler.step(a				, a				, a				, a    ).prev_sample

            return sample


      def _UpperCamelCase    (     self					:						List[Any]    ):





            """simple docstring"""







            for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                  self.check_over_configs(num_train_timesteps=a    )


      def _UpperCamelCase    (     self					:						Tuple    ):





            """simple docstring"""







            for steps_offset in [0, 1]:
                  self.check_over_configs(steps_offset=a    )

            __snake_case   :     Tuple	      =self.scheduler_classes[0]
            __snake_case   :     Optional[Any]	      =self.get_scheduler_config(steps_offset=1    )
            __snake_case   :     Union[str, Any]	      =scheduler_class(**a    )
            scheduler.set_timesteps(5    )
            assert torch.equal(scheduler.timesteps				, torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]    )    )


      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]				, [0.0_0_2, 0.0_2, 0.2, 2]    ):
                  self.check_over_configs(beta_start=a				, beta_end=a    )


      def _UpperCamelCase    (     self					:						Optional[int]    ):





            """simple docstring"""







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


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







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


      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=a    )


      def _UpperCamelCase    (     self					:						Any    ):





            """simple docstring"""







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


      def _UpperCamelCase    (     self					:						Optional[Any]    ):





            """simple docstring"""







            for rescale_betas_zero_snr in [True, False]:
                  self.check_over_configs(rescale_betas_zero_snr=a    )


      def _UpperCamelCase    (     self					:						Union[str, Any]    ):





            """simple docstring"""







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


      def _UpperCamelCase    (     self					:						str    ):





            """simple docstring"""







            for t in [1, 1_0, 4_9]:
                  self.check_over_forward(time_step=a    )


      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            for t, num_inference_steps in zip([1, 1_0, 5_0]				, [1_0, 5_0, 5_0_0]    ):
                  self.check_over_forward(time_step=a				, num_inference_steps=a    )


      def _UpperCamelCase    (     self					:						Dict    ):





            """simple docstring"""







            for t, eta in zip([1, 1_0, 4_9]				, [0.0, 0.5, 1.0]    ):
                  self.check_over_forward(time_step=a				, eta=a    )


      def _UpperCamelCase    (     self					:						Dict    ):





            """simple docstring"""







            __snake_case   :     Union[str, Any]	      =self.scheduler_classes[0]
            __snake_case   :     int	      =self.get_scheduler_config()
            __snake_case   :     Optional[Any]	      =scheduler_class(**a    )

            assert torch.sum(torch.abs(scheduler._get_variance(0				, 0    ) - 0.0    )    ) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_2_0				, 4_0_0    ) - 0.1_4_7_7_1    )    ) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_8_0				, 9_6_0    ) - 0.3_2_4_6_0    )    ) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(0				, 0    ) - 0.0    )    ) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_8_7				, 4_8_6    ) - 0.0_0_9_7_9    )    ) < 1e-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_9_9				, 9_9_8    ) - 0.0_2    )    ) < 1e-5


      def _UpperCamelCase    (     self					:						List[Any]    ):





            """simple docstring"""







            __snake_case   :     Dict	      =self.full_loop()

            __snake_case   :     Dict	      =torch.sum(torch.abs(a    )    )
            __snake_case   :     Any	      =torch.mean(torch.abs(a    )    )

            assert abs(result_sum.item() - 1_7_2.0_0_6_7    ) < 1e-2
            assert abs(result_mean.item() - 0.2_2_3_9_6_7    ) < 1e-3


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







            __snake_case   :     str	      =self.full_loop(prediction_type='''v_prediction'''    )

            __snake_case   :     List[str]	      =torch.sum(torch.abs(a    )    )
            __snake_case   :     Any	      =torch.mean(torch.abs(a    )    )

            assert abs(result_sum.item() - 5_2.5_3_0_2    ) < 1e-2
            assert abs(result_mean.item() - 0.0_6_8_4    ) < 1e-3


      def _UpperCamelCase    (     self					:						Optional[Any]    ):





            """simple docstring"""







            __snake_case   :     int	      =self.full_loop(set_alpha_to_one=a				, beta_start=0.0_1    )
            __snake_case   :     str	      =torch.sum(torch.abs(a    )    )
            __snake_case   :     int	      =torch.mean(torch.abs(a    )    )

            assert abs(result_sum.item() - 1_4_9.8_2_9_5    ) < 1e-2
            assert abs(result_mean.item() - 0.1_9_5_1    ) < 1e-3







      def _UpperCamelCase    (     self					:						Optional[Any]    ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      =self.full_loop(set_alpha_to_one=a				, beta_start=0.0_1    )
            __snake_case   :     str	      =torch.sum(torch.abs(a    )    )
            __snake_case   :     int	      =torch.mean(torch.abs(a    )    )

            assert abs(result_sum.item() - 1_4_9.0_7_8_4    ) < 1e-2
            assert abs(result_mean.item() - 0.1_9_4_1    ) < 1e-3










"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_						:					Tuple      				=			{"""configuration_detr""": ["""DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DetrConfig""", """DetrOnnxConfig"""]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Tuple      				=			["""DetrFeatureExtractor"""]
    UpperCamelCase_						:					int      				=			["""DetrImageProcessor"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Dict      				=			[
        """DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """DetrForObjectDetection""",
        """DetrForSegmentation""",
        """DetrModel""",
        """DetrPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_detr import DetrFeatureExtractor
        from .image_processing_detr import DetrImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_detr import (
            DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetrForObjectDetection,
            DetrForSegmentation,
            DetrModel,
            DetrPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					List[str]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


