






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


A											=     logging.get_logger(__name__)


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  ["""pixel_values"""]


			def __init__(    self ,				__UpperCamelCase = True ,				__UpperCamelCase = 1 / 2_55 ,				__UpperCamelCase = True ,				__UpperCamelCase = 8 ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase							)

				snake_case_      		= do_rescale
				snake_case_      		= rescale_factor
				snake_case_      		= do_pad
				snake_case_      		= pad_size


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = None ,				**__UpperCamelCase							):

				"""simple docstring"""


				return rescale(__UpperCamelCase ,				scale=__UpperCamelCase ,				data_format=__UpperCamelCase ,				**__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


				snake_case_      		= get_image_size(__UpperCamelCase							)
				snake_case_      		= (old_height // size + 1) * size - old_height
				snake_case_      		= (old_width // size + 1) * size - old_width

				return pad(__UpperCamelCase ,				((0, pad_height), (0, pad_width)) ,				mode='symmetric' ,				data_format=__UpperCamelCase							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = None ,				__UpperCamelCase = ChannelDimension.FIRST ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= do_rescale if do_rescale is not None else self.do_rescale
				snake_case_      		= rescale_factor if rescale_factor is not None else self.rescale_factor
				snake_case_      		= do_pad if do_pad is not None else self.do_pad
				snake_case_      		= pad_size if pad_size is not None else self.pad_size

				snake_case_      		= make_list_of_images(__UpperCamelCase							)

				if not valid_images(__UpperCamelCase							):
					raise ValueError(
					    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
					    'torch.Tensor, tf.Tensor or jax.ndarray.'							)

				if do_rescale and rescale_factor is None:
					raise ValueError('Rescale factor must be specified if do_rescale is True.'							)

				# All transformations expect numpy arrays.
				snake_case_      		= [to_numpy_array(__UpperCamelCase							) for image in images]

				if do_rescale:
					snake_case_      		= [self.rescale(image=__UpperCamelCase ,				scale=__UpperCamelCase							) for image in images]

				if do_pad:
					snake_case_      		= [self.pad(__UpperCamelCase ,				size=__UpperCamelCase							) for image in images]

				snake_case_      		= [to_channel_dimension_format(__UpperCamelCase ,				__UpperCamelCase							) for image in images]

				snake_case_      		= {'pixel_values': images}
				return BatchFeature(data=__UpperCamelCase ,				tensor_type=__UpperCamelCase							)



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A											=     argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A											=     parser.parse_args()


A											=     'cpu'
A											=     'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

A											=     'path-to-your-trained-model'
A											=     StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		A											=     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A											=     pipe.to(device)

# to channels last
A											=     pipe.unet.to(memory_format=torch.channels_last)
A											=     pipe.vae.to(memory_format=torch.channels_last)
A											=     pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		A											=     pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
A											=     torch.randn(2, 4, 64, 64)
A											=     torch.rand(1) * 999
A											=     torch.randn(2, 77, 768)
A											=     (sample, timestep, encoder_hidden_status)
try:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		A											=     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
A											=     666
A											=     torch.Generator(device).manual_seed(seed)
A											=     {'generator': generator}
if args.steps is not None:
		A											=     args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
		A											=     pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')
