





"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

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




class __magic_name__   (       _UpperCamelCase		):
       UpperCamelCase    :						Union[str, Any]      =	42
       UpperCamelCase    :						Tuple      =	42
       def __init__(     self   ,  __magic_name__   ,  __magic_name__	):





         """simple docstring"""


         super().__init__()
         self.register_modules(unet=__lowerCAmelCase   ,  scheduler=__lowerCAmelCase	)
       @torch.no_grad()
       def __call__(     self   ,  __magic_name__ = 1   ,  __magic_name__ = 2_0_0_0   ,  __magic_name__ = None   ,  __magic_name__ = "pil"   ,  __magic_name__ = True   ,  **__magic_name__   ,  ):





         """simple docstring"""


         _lowerCAmelCase	      =					self.unet.config.sample_size
         _lowerCAmelCase	      =					(batch_size, 3, img_size, img_size)

         _lowerCAmelCase	      =					self.unet

         _lowerCAmelCase	      =					randn_tensor(__lowerCAmelCase   ,  generator=__lowerCAmelCase	) * self.scheduler.init_noise_sigma
         _lowerCAmelCase	      =					sample.to(self.device	)

         self.scheduler.set_timesteps(__lowerCAmelCase	)
         self.scheduler.set_sigmas(__lowerCAmelCase	)

         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps	)	):
           _lowerCAmelCase	      =					self.scheduler.sigmas[i] * torch.ones(shape[0]   ,  device=self.device	)

           # correction step
           for _ in range(self.scheduler.config.correct_steps	):
             _lowerCAmelCase	      =					self.unet(__lowerCAmelCase   ,  __lowerCAmelCase	).sample
             _lowerCAmelCase	      =					self.scheduler.step_correct(__lowerCAmelCase   ,  __lowerCAmelCase   ,  generator=__lowerCAmelCase	).prev_sample

           # prediction step
           _lowerCAmelCase	      =					model(__lowerCAmelCase   ,  __lowerCAmelCase	).sample
           _lowerCAmelCase	      =					self.scheduler.step_pred(__lowerCAmelCase   ,  __lowerCAmelCase   ,  __lowerCAmelCase   ,  generator=__lowerCAmelCase	)

           _lowerCAmelCase	      =					output.prev_sample, output.prev_sample_mean

         _lowerCAmelCase	      =					sample_mean.clamp(0   ,  1	)
         _lowerCAmelCase	      =					sample.cpu().permute(0   ,  2   ,  3   ,  1	).numpy()
         if output_type == "pil":
           _lowerCAmelCase	      =					self.numpy_to_pil(__lowerCAmelCase	)

         if not return_dict:
           return (sample,)

         return ImagePipelineOutput(images=__lowerCAmelCase	)











from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :      Dict				=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      str				=		[
					    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CanineForMultipleChoice""",
					    """CanineForQuestionAnswering""",
					    """CanineForSequenceClassification""",
					    """CanineForTokenClassification""",
					    """CanineLayer""",
					    """CanineModel""",
					    """CaninePreTrainedModel""",
					    """load_tf_weights_in_canine""",
					]


if TYPE_CHECKING:
					from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
					from .tokenization_canine import CanineTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_canine import (
										    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CanineForMultipleChoice,
										    CanineForQuestionAnswering,
										    CanineForSequenceClassification,
										    CanineForTokenClassification,
										    CanineLayer,
										    CanineModel,
										    CaninePreTrainedModel,
										    load_tf_weights_in_canine,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






