
from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	def __init__(	self  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      Dict	)	->					Tuple:
					super().__init__()
					self.register_modules(unet=SCREAMING_SNAKE_CASE_	,							scheduler=SCREAMING_SNAKE_CASE_	)




	@torch.no_grad()
	def __call__(	self  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      int = 1	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[torch.Generator, List[torch.Generator]]] = None	,							SCREAMING_SNAKE_CASE_  :      int = 1_0_0_0	,							SCREAMING_SNAKE_CASE_  :      Optional[str] = "pil"	,							SCREAMING_SNAKE_CASE_  :      bool = True	,							)	->					Union[ImagePipelineOutput, Tuple]:
					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size	,							SCREAMING_SNAKE_CASE_	):
									lowercase_					  = (
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									lowercase_					  = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if self.device.type == "mps":
									# randn does not work reproducibly on mps
									lowercase_					  = randn_tensor(SCREAMING_SNAKE_CASE_	,							generator=SCREAMING_SNAKE_CASE_	)
									lowercase_					  = image.to(self.device	)
					else:
									lowercase_					  = randn_tensor(SCREAMING_SNAKE_CASE_	,							generator=SCREAMING_SNAKE_CASE_	,							device=self.device	)

					# set step values
					self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_	)

					for t in self.progress_bar(self.scheduler.timesteps	):
									# 1. predict noise model_output
									lowercase_					  = self.unet(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	).sample

									# 2. compute previous image: x_t -> x_t-1
									lowercase_					  = self.scheduler.step(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							generator=SCREAMING_SNAKE_CASE_	).prev_sample

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

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_	)






from typing import TYPE_CHECKING

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


__a        = {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'NystromformerForMaskedLM',
						    'NystromformerForMultipleChoice',
						    'NystromformerForQuestionAnswering',
						    'NystromformerForSequenceClassification',
						    'NystromformerForTokenClassification',
						    'NystromformerLayer',
						    'NystromformerModel',
						    'NystromformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nystromformer import (
												    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NystromformerForMaskedLM,
												    NystromformerForMultipleChoice,
												    NystromformerForQuestionAnswering,
												    NystromformerForSequenceClassification,
												    NystromformerForTokenClassification,
												    NystromformerLayer,
												    NystromformerModel,
												    NystromformerPreTrainedModel,
												)


else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




