

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      A_	(a_      ):
	UpperCAmelCase__ =      4_2
	UpperCAmelCase__ =      4_2
	def __init__(   self							, _A							, _A		):






				'''simple docstring'''







				super().__init__()

				UpperCAmelCase       		=    PNDMScheduler.from_config(scheduler.config		)

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







	@torch.no_grad()
	def __call__(   self							, _A = 1							, _A = 5_0							, _A = None							, _A = "pil"							, _A = True							, **_A							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    randn_tensor(
				    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							, generator=_A							, device=self.device							, )

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

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

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

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)










from typing import TYPE_CHECKING

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


__A    :   Tuple          =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   List[str]          =       [
				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "MobileViTV2ForImageClassification",
				    "MobileViTV2ForSemanticSegmentation",
				    "MobileViTV2Model",
				    "MobileViTV2PreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				__A    :   Tuple          =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



