





'''simple docstring'''


from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				int								=       ["pixel_values"]




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

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = PILImageResampling.BICUBIC      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
     if "shortest_edge" in size:
         _lowerCAmelCase						      =   int((256 / 224) * size["shortest_edge"]		)
         _lowerCAmelCase						      =   get_resize_output_image_size(_lowerCAmelCase      ,  size=_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
         _lowerCAmelCase						      =   {"height": output_size[0], "width": output_size[1]}
     if "height" not in size_dict or "width" not in size_dict:
         raise ValueError(
             f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''		)
     return resize(
         _lowerCAmelCase      ,  size=(size_dict["height"], size_dict["width"])      ,  resample=_lowerCAmelCase      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				np.ndarray:
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase		)
     if "height" not in size or "width" not in size:
         raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
     return center_crop(_lowerCAmelCase      ,  size=(size["height"], size["width"])      ,  data_format=_lowerCAmelCase      ,  **_lowerCAmelCase		)




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




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





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = ChannelDimension.FIRST      ,  **_lowerCAmelCase      ,  )     ->				BatchFeature:
     _lowerCAmelCase						      =   do_resize if do_resize is not None else self.do_resize
     _lowerCAmelCase						      =   resample if resample is not None else self.resample
     _lowerCAmelCase						      =   do_center_crop if do_center_crop is not None else self.do_center_crop
     _lowerCAmelCase						      =   do_rescale if do_rescale is not None else self.do_rescale
     _lowerCAmelCase						      =   rescale_factor if rescale_factor is not None else self.rescale_factor
     _lowerCAmelCase						      =   do_normalize if do_normalize is not None else self.do_normalize
     _lowerCAmelCase						      =   image_mean if image_mean is not None else self.image_mean
     _lowerCAmelCase						      =   image_std if image_std is not None else self.image_std

     _lowerCAmelCase						      =   size if size is not None else self.size
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  default_to_square=_lowerCAmelCase		)
     _lowerCAmelCase						      =   crop_size if crop_size is not None else self.crop_size
     _lowerCAmelCase						      =   get_size_dict(_lowerCAmelCase      ,  param_name="crop_size"		)

     _lowerCAmelCase						      =   make_list_of_images(_lowerCAmelCase		)

     if not valid_images(_lowerCAmelCase		):
         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."		)

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."		)

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

     if do_resize:
         _lowerCAmelCase						      =   [self.resize(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

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

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

     if do_normalize:
         _lowerCAmelCase						      =   [self.normalize(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     _lowerCAmelCase						      =   [to_channel_dimension_format(_lowerCAmelCase      ,  _lowerCAmelCase		) for image in images]

     _lowerCAmelCase						      =   {"pixel_values": images}
     return BatchFeature(data=_lowerCAmelCase      ,  tensor_type=_lowerCAmelCase		)









import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowerCAmelCase				:   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''')
_lowerCAmelCase				:   Tuple						       =						parser.parse_args()


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

_lowerCAmelCase				:   Union[str, Any]						       =						'''path-to-your-trained-model'''
_lowerCAmelCase				:   Tuple						       =						StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
 _lowerCAmelCase				:   List[str]						       =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_lowerCAmelCase				:   List[Any]						       =						pipe.to(device)

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

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

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

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

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