




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_						=logging.get_logger(__name__)


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: List[Any]										=["""pixel_values"""]



				def __init__( self       , UpperCAmelCase_ = True       , UpperCAmelCase_ = 32       , UpperCAmelCase_=PILImageResampling.BILINEAR       , UpperCAmelCase_ = True       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		do_resize
											lowerCAmelCase   =		do_rescale
											lowerCAmelCase   =		size_divisor
											lowerCAmelCase   =		resample
											super().__init__(**UpperCamelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = None       , **UpperCAmelCase_						):
											lowerCAmelCase   =		get_image_size(UpperCamelCase_						)
											# Rounds the height and width down to the closest multiple of size_divisor
											lowerCAmelCase   =		height // size_divisor * size_divisor
											lowerCAmelCase   =		width // size_divisor * size_divisor
											lowerCAmelCase   =		resize(UpperCamelCase_       , (new_h, new_w)       , resample=UpperCamelCase_       , data_format=UpperCamelCase_       , **UpperCamelCase_						)
											return image



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = None       , **UpperCAmelCase_						):
											return rescale(image=UpperCamelCase_       , scale=UpperCamelCase_       , data_format=UpperCamelCase_       , **UpperCamelCase_						)






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_=None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = ChannelDimension.FIRST       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		do_resize if do_resize is not None else self.do_resize
											lowerCAmelCase   =		do_rescale if do_rescale is not None else self.do_rescale
											lowerCAmelCase   =		size_divisor if size_divisor is not None else self.size_divisor
											lowerCAmelCase   =		resample if resample is not None else self.resample

											if do_resize and size_divisor is None:
																		raise ValueError('''size_divisor is required for resizing'''						)

											lowerCAmelCase   =		make_list_of_images(UpperCamelCase_						)

											if not valid_images(UpperCamelCase_						):
																		raise ValueError('''Invalid image(s)'''						)

											# All transformations expect numpy arrays.
											lowerCAmelCase   =		[to_numpy_array(UpperCamelCase_						) for img in images]

											if do_resize:
																		lowerCAmelCase   =		[self.resize(UpperCamelCase_       , size_divisor=UpperCamelCase_       , resample=UpperCamelCase_						) for image in images]

											if do_rescale:
																		lowerCAmelCase   =		[self.rescale(UpperCamelCase_       , scale=1 / 2_55						) for image in images]

											lowerCAmelCase   =		[to_channel_dimension_format(UpperCamelCase_       , UpperCamelCase_						) for image in images]

											lowerCAmelCase   =		{'pixel_values': images}
											return BatchFeature(data=UpperCamelCase_       , tensor_type=UpperCamelCase_						)





import torch

from diffusers import StableDiffusionPipeline


UpperCAmelCase_						="""path-to-your-trained-model"""
UpperCAmelCase_						=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

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

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



