




'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 					lowerCAmelCase_     (    A__  ):
       __UpperCAmelCase				=42
       __UpperCAmelCase				=42






       def __init__(   self ,    _UpperCamelCase ,    _UpperCamelCase      )->			Dict:
            super().__init__()

            _A				=       PNDMScheduler.from_config(scheduler.config      )

            self.register_modules(unet=_lowerCAmelCase ,    scheduler=_lowerCAmelCase      )






       @torch.no_grad()
       def __call__(   self ,    _UpperCamelCase = 1 ,    _UpperCamelCase = 50 ,    _UpperCamelCase = None ,    _UpperCamelCase = "pil" ,    _UpperCamelCase = True ,    **_UpperCamelCase ,    )->			List[str]:
            _A				=       randn_tensor(
                (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,    generator=_lowerCAmelCase ,    device=self.device ,    )

            self.scheduler.set_timesteps(_lowerCAmelCase      )
            for t in self.progress_bar(self.scheduler.timesteps      ):
                 _A				=       self.unet(_lowerCAmelCase ,    _lowerCAmelCase      ).sample

                 _A				=       self.scheduler.step(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase      ).prev_sample

            _A				=       (image / 2 + 0.5).clamp(0 ,    1      )
            _A				=       image.cpu().permute(0 ,    2 ,    3 ,    1      ).numpy()
            if output_type == "pil":
                 _A				=       self.numpy_to_pil(_lowerCAmelCase      )

            if not return_dict:
                 return (image,)

            return ImagePipelineOutput(images=_lowerCAmelCase      )









'''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)





