


"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_   :   List[Any]									=  logging.get_logger(__name__)






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  ["pixel_values"]


 def __init__(		self ,    __A = True ,    __A = None ,    __A = PILImageResampling.BICUBIC ,    __A = True ,    __A = True ,    __A = 1 / 255 ,    __A = None ,    __A = True ,    __A = None ,    __A = None ,    **__A ,    )					->     None:
     super().__init__(**__A						)
     a 		=size if size is not None else {'''height''': 224, '''width''': 224}
     a 		=get_size_dict(__A						)
     a 		=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     a 		=get_size_dict(__A ,    default_to_square=__A ,    param_name='''crop_size'''						)

     a 		=do_resize
     a 		=do_rescale
     a 		=do_normalize
     a 		=do_center_crop
     a 		=crop_size
     a 		=size
     a 		=resample
     a 		=rescale_factor
     a 		=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     a 		=image_std if image_std is not None else IMAGENET_DEFAULT_STD


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A = PILImageResampling.BILINEAR ,    __A = None ,    **__A ,    )					->     np.ndarray:
     a 		=get_size_dict(__A						)

     if "shortest_edge" in size:
         a 		=get_resize_output_image_size(__A ,    size=size['''shortest_edge'''] ,    default_to_square=__A						)
         # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
     elif "height" in size and "width" in size:
         a 		=(size['''height'''], size['''width'''])
     else:
         raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''						)
     return resize(__A ,    size=__A ,    resample=__A ,    data_format=__A ,    **__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A = None ,    **__A ,    )					->     np.ndarray:
     a 		=get_size_dict(__A						)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
     return center_crop(__A ,    size=(size['''height'''], size['''width''']) ,    data_format=__A ,    **__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A = None ,    **__A						)					->     np.ndarray:
     return rescale(__A ,    scale=__A ,    data_format=__A ,    **__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A = None ,    **__A ,    )					->     np.ndarray:
     return normalize(__A ,    mean=__A ,    std=__A ,    data_format=__A ,    **__A						)





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = ChannelDimension.FIRST ,    **__A ,    )					->     BatchFeature:
     a 		=do_resize if do_resize is not None else self.do_resize
     a 		=do_rescale if do_rescale is not None else self.do_rescale
     a 		=do_normalize if do_normalize is not None else self.do_normalize
     a 		=do_center_crop if do_center_crop is not None else self.do_center_crop
     a 		=crop_size if crop_size is not None else self.crop_size
     a 		=get_size_dict(__A ,    param_name='''crop_size''' ,    default_to_square=__A						)
     a 		=resample if resample is not None else self.resample
     a 		=rescale_factor if rescale_factor is not None else self.rescale_factor
     a 		=image_mean if image_mean is not None else self.image_mean
     a 		=image_std if image_std is not None else self.image_std

     a 		=size if size is not None else self.size
     a 		=get_size_dict(__A						)

     if not is_batched(__A						):
         a 		=[images]

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

     if do_resize and size is None:
         raise ValueError('''Size must be specified if do_resize is True.'''						)

     if do_center_crop and crop_size is None:
         raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

     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.
     a 		=[to_numpy_array(__A						) for image in images]

     if do_resize:
         a 		=[self.resize(image=__A ,    size=__A ,    resample=__A						) for image in images]

     if do_center_crop:
         a 		=[self.center_crop(image=__A ,    size=__A						) for image in images]

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

     if do_normalize:
         a 		=[self.normalize(image=__A ,    mean=__A ,    std=__A						) for image in images]

     a 		=[to_channel_dimension_format(__A ,    __A						) for image in images]

     a 		={'''pixel_values''': images}
     return BatchFeature(data=__A ,    tensor_type=__A						)

'''simple docstring'''







import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase    :							Optional[Any]      =					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")
lowercase    :							Tuple      =					parser.parse_args()


lowercase    :							Optional[int]      =					"cpu"
lowercase    :							Optional[Any]      =					"a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

lowercase    :							Optional[int]      =					"path-to-your-trained-model"
lowercase    :							List[str]      =					StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
					lowercase    :							str      =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase    :							Dict      =					pipe.to(device)

# to channels last
lowercase    :							Optional[Any]      =					pipe.unet.to(memory_format=torch.channels_last)
lowercase    :							int      =					pipe.vae.to(memory_format=torch.channels_last)
lowercase    :							Optional[Any]      =					pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					lowercase    :							Optional[int]      =					pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
lowercase    :							List[str]      =					666
lowercase    :							Tuple      =					torch.Generator(device).manual_seed(seed)
lowercase    :							Union[str, Any]      =					{"generator": generator}
if args.steps is not None:
					lowercase    :							Dict      =					args.steps

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

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

