


'''simple docstring'''
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 					lowerCAmelCase_     (    UpperCAmelCase  ):
       __UpperCAmelCase				=(HeunDiscreteScheduler,)
       __UpperCAmelCase				=10






       def      UpperCamelCase	(   self ,    **_UpperCamelCase      )->			Optional[Any]:
            _A				=       {
                'num_train_timesteps': 1100,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
            }

            config.update(**_UpperCamelCase      )
            return config






       def      UpperCamelCase	(   self      )->			List[Any]:
            for timesteps in [10, 50, 100, 1000]:
                 self.check_over_configs(num_train_timesteps=_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			List[Any]:
            for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] ,    [0.0002, 0.002, 0.02]      ):
                 self.check_over_configs(beta_start=_UpperCamelCase ,    beta_end=_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			Any:
            for schedule in ["linear", "scaled_linear", "exp"]:
                 self.check_over_configs(beta_schedule=_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			str:
            for clip_sample_range in [1.0, 2.0, 3.0]:
                 self.check_over_configs(clip_sample_range=_UpperCamelCase ,    clip_sample=_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			int:
            for prediction_type in ["epsilon", "v_prediction", "sample"]:
                 self.check_over_configs(prediction_type=_UpperCamelCase      )






       def      UpperCamelCase	(   self      )->			Union[str, Any]:
            _A				=       self.scheduler_classes[0]
            _A				=       self.get_scheduler_config()
            _A				=       scheduler_class(**_UpperCamelCase      )

            scheduler.set_timesteps(self.num_inference_steps      )

            _A				=       self.dummy_model()
            _A				=       self.dummy_sample_deter * scheduler.init_noise_sigma
            _A				=       sample.to(_UpperCamelCase      )

            for i, t in enumerate(scheduler.timesteps      ):
                 _A				=       scheduler.scale_model_input(_UpperCamelCase ,    _UpperCamelCase      )

                 _A				=       model(_UpperCamelCase ,    _UpperCamelCase      )

                 _A				=       scheduler.step(_UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )
                 _A				=       output.prev_sample

            _A				=       torch.sum(torch.abs(_UpperCamelCase      )      )
            _A				=       torch.mean(torch.abs(_UpperCamelCase      )      )

            if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 0.1233      ) < 1e-2
                 assert abs(result_mean.item() - 0.0002      ) < 1e-3
            else:
                 # CUDA
                 assert abs(result_sum.item() - 0.1233      ) < 1e-2
                 assert abs(result_mean.item() - 0.0002      ) < 1e-3






       def      UpperCamelCase	(   self      )->			Dict:
            _A				=       self.scheduler_classes[0]
            _A				=       self.get_scheduler_config(prediction_type='v_prediction'      )
            _A				=       scheduler_class(**_UpperCamelCase      )

            scheduler.set_timesteps(self.num_inference_steps      )

            _A				=       self.dummy_model()
            _A				=       self.dummy_sample_deter * scheduler.init_noise_sigma
            _A				=       sample.to(_UpperCamelCase      )

            for i, t in enumerate(scheduler.timesteps      ):
                 _A				=       scheduler.scale_model_input(_UpperCamelCase ,    _UpperCamelCase      )

                 _A				=       model(_UpperCamelCase ,    _UpperCamelCase      )

                 _A				=       scheduler.step(_UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )
                 _A				=       output.prev_sample

            _A				=       torch.sum(torch.abs(_UpperCamelCase      )      )
            _A				=       torch.mean(torch.abs(_UpperCamelCase      )      )

            if torch_device in ["cpu", "mps"]:
                 assert abs(result_sum.item() - 4.69_34e-07      ) < 1e-2
                 assert abs(result_mean.item() - 6.11_12e-10      ) < 1e-3
            else:
                 # CUDA
                 assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07      ) < 1e-2
                 assert abs(result_mean.item() - 0.0002      ) < 1e-3






       def      UpperCamelCase	(   self      )->			int:
            _A				=       self.scheduler_classes[0]
            _A				=       self.get_scheduler_config()
            _A				=       scheduler_class(**_UpperCamelCase      )

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

            _A				=       self.dummy_model()
            _A				=       self.dummy_sample_deter.to(_UpperCamelCase      ) * scheduler.init_noise_sigma

            for t in scheduler.timesteps:
                 _A				=       scheduler.scale_model_input(_UpperCamelCase ,    _UpperCamelCase      )

                 _A				=       model(_UpperCamelCase ,    _UpperCamelCase      )

                 _A				=       scheduler.step(_UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )
                 _A				=       output.prev_sample

            _A				=       torch.sum(torch.abs(_UpperCamelCase      )      )
            _A				=       torch.mean(torch.abs(_UpperCamelCase      )      )

            if str(_UpperCamelCase      ).startswith('cpu'      ):
                 # The following sum varies between 148 and 156 on mps. Why?
                 assert abs(result_sum.item() - 0.1233      ) < 1e-2
                 assert abs(result_mean.item() - 0.0002      ) < 1e-3
            elif str(_UpperCamelCase      ).startswith('mps'      ):
                 # Larger tolerance on mps
                 assert abs(result_mean.item() - 0.0002      ) < 1e-2
            else:
                 # CUDA
                 assert abs(result_sum.item() - 0.1233      ) < 1e-2
                 assert abs(result_mean.item() - 0.0002      ) < 1e-3






       def      UpperCamelCase	(   self      )->			Union[str, Any]:
            _A				=       self.scheduler_classes[0]
            _A				=       self.get_scheduler_config()
            _A				=       scheduler_class(**_UpperCamelCase ,    use_karras_sigmas=_UpperCamelCase      )

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

            _A				=       self.dummy_model()
            _A				=       self.dummy_sample_deter.to(_UpperCamelCase      ) * scheduler.init_noise_sigma
            _A				=       sample.to(_UpperCamelCase      )

            for t in scheduler.timesteps:
                 _A				=       scheduler.scale_model_input(_UpperCamelCase ,    _UpperCamelCase      )

                 _A				=       model(_UpperCamelCase ,    _UpperCamelCase      )

                 _A				=       scheduler.step(_UpperCamelCase ,    _UpperCamelCase ,    _UpperCamelCase      )
                 _A				=       output.prev_sample

            _A				=       torch.sum(torch.abs(_UpperCamelCase      )      )
            _A				=       torch.mean(torch.abs(_UpperCamelCase      )      )

            assert abs(result_sum.item() - 0.0_0015      ) < 1e-2
            assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95e-07      ) < 1e-2









'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase  =				{
    """configuration_convnextv2""": [
        """CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConvNextV2Config""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase  =				[
        """CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvNextV2ForImageClassification""",
        """ConvNextV2Model""",
        """ConvNextV2PreTrainedModel""",
        """ConvNextV2Backbone""",
    ]


if TYPE_CHECKING:
    from .configuration_convnextva import (
        CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ConvNextVaConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnextva import (
            CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextVaBackbone,
            ConvNextVaForImageClassification,
            ConvNextVaModel,
            ConvNextVaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase  =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)





