




from typing import TYPE_CHECKING

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


_lowercase		:				Any      		={
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				List[Any]      		=["Pix2StructImageProcessor"]


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				Optional[Any]      		=[
		    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "Pix2StructPreTrainedModel",
		    "Pix2StructForConditionalGeneration",
		    "Pix2StructVisionModel",
		    "Pix2StructTextModel",
		]

if TYPE_CHECKING:
		from .configuration_pixastruct import (
		    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    PixaStructConfig,
		    PixaStructTextConfig,
		    PixaStructVisionConfig,
		)
		from .processing_pixastruct import PixaStructProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_pixastruct import PixaStructImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pixastruct import (
				    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PixaStructForConditionalGeneration,
				    PixaStructPreTrainedModel,
				    PixaStructTextModel,
				    PixaStructVisionModel,
				)

else:
		import sys

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









import torch

from diffusers import StableDiffusionPipeline


_lowercase		:				Optional[int]      		="path-to-your-trained-model"
_lowercase		:				Union[str, Any]      		=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

_lowercase		:				Any      		="A photo of sks dog in a bucket"
_lowercase		:				Any      		=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")




