
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 		__magic_name__   (						_a    ):
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42





		def __init__(							self    :	str						,  lowerCamelCase__    :	Dict						,  lowerCamelCase__    :	Tuple				):
							super().__init__()

							lowerCAmelCase		:					Union[str, Any]  	=     PNDMScheduler.from_config(scheduler.config				)

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





		@torch.no_grad()
		def __call__(							self    :	List[str]						,  lowerCamelCase__    :	List[str] = 1						,  lowerCamelCase__    :	Dict = 5_0						,  lowerCamelCase__    :	Dict = None						,  lowerCamelCase__    :	str = "pil"						,  lowerCamelCase__    :	List[Any] = True						,  **lowerCamelCase__    :	Optional[int]						,  ):
							lowerCAmelCase		:					str  	=     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				):
												lowerCAmelCase		:					List[str]  	=     self.unet(lowerCamelCase__						,  lowerCamelCase__				).sample

												lowerCAmelCase		:					Tuple  	=     self.scheduler.step(lowerCamelCase__						,  lowerCamelCase__						,  lowerCamelCase__				).prev_sample

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

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=lowerCamelCase__				)


from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE			:   List[str]         =     {'configuration_vilt': ['VILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViltConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Any         =     ['ViltFeatureExtractor']
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     ['ViltImageProcessor']
		__SCREAMING_SNAKE_CASE			:   str         =     ['ViltProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Dict         =     [
		    'VILT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViltForImageAndTextRetrieval',
		    'ViltForImagesAndTextClassification',
		    'ViltForTokenClassification',
		    'ViltForMaskedLM',
		    'ViltForQuestionAnswering',
		    'ViltLayer',
		    'ViltModel',
		    'ViltPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     _LazyModule(__name__, globals()['__file__'], _import_structure)
