
"""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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_snake_case          =						logging.get_logger(__name__)
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Tuple							=					["pixel_values"]


				def __init__(					self	:			Optional[int]				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Optional[Dict[str, int]] = None				,     UpperCAmelCase_	:			PILImageResampling = PILImageResampling.BILINEAR				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Dict[str, int] = None				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Union[int, float] = 1 / 255				,     UpperCAmelCase_	:			bool = True				,     UpperCAmelCase_	:			Optional[Union[float, List[float]]] = None				,     UpperCAmelCase_	:			Optional[Union[float, List[float]]] = None				,     **UpperCAmelCase_	:			Optional[Any]				,     )		->  None:







											"""simple docstring"""




											super().__init__(**UpperCAmelCase_  )
											_lowerCAmelCase  	=			size if size is not None else {'shortest_edge': 256}
											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_				,     default_to_square=UpperCAmelCase_  )
											_lowerCAmelCase  	=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_  )
											_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_STANDARD_MEAN
											_lowerCAmelCase  	=			image_std if image_std is not None else IMAGENET_STANDARD_STD


				def 				__lowerCamelCase	(					self	:			int				,     UpperCAmelCase_	:			np.ndarray				,     UpperCAmelCase_	:			Dict[str, int]				,     UpperCAmelCase_	:			PILImageResampling = PILImageResampling.BICUBIC				,     UpperCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None				,     **UpperCAmelCase_	:			Optional[Any]				,     )		->  np.ndarray:







											"""simple docstring"""




											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_				,     default_to_square=UpperCAmelCase_  )
											if "shortest_edge" not in size:
																		raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
											_lowerCAmelCase  	=			get_resize_output_image_size(UpperCAmelCase_				,     size=size['shortest_edge']				,     default_to_square=UpperCAmelCase_  )
											return resize(UpperCAmelCase_				,     size=UpperCAmelCase_				,     resample=UpperCAmelCase_				,     data_format=UpperCAmelCase_				,     **UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			str				,     UpperCAmelCase_	:			np.ndarray				,     UpperCAmelCase_	:			Dict[str, int]				,     UpperCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None				,     **UpperCAmelCase_	:			str				,     )		->  np.ndarray:







											"""simple docstring"""




											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_  )
											return center_crop(UpperCAmelCase_				,     size=(size['height'], size['width'])				,     data_format=UpperCAmelCase_				,     **UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			np.ndarray				,     UpperCAmelCase_	:			float				,     UpperCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None				,     **UpperCAmelCase_	:			List[str]  )		->  np.ndarray:







											"""simple docstring"""




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


				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			np.ndarray				,     UpperCAmelCase_	:			Union[float, List[float]]				,     UpperCAmelCase_	:			Union[float, List[float]]				,     UpperCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None				,     **UpperCAmelCase_	:			str				,     )		->  np.ndarray:







											"""simple docstring"""




											return normalize(UpperCAmelCase_				,     mean=UpperCAmelCase_				,     std=UpperCAmelCase_				,     data_format=UpperCAmelCase_				,     **UpperCAmelCase_  )







				def 				__lowerCamelCase	(					self	:			List[Any]				,     UpperCAmelCase_	:			ImageInput				,     UpperCAmelCase_	:			Optional[bool] = None				,     UpperCAmelCase_	:			Dict[str, int] = None				,     UpperCAmelCase_	:			PILImageResampling = None				,     UpperCAmelCase_	:			bool = None				,     UpperCAmelCase_	:			Dict[str, int] = None				,     UpperCAmelCase_	:			Optional[bool] = None				,     UpperCAmelCase_	:			Optional[float] = None				,     UpperCAmelCase_	:			Optional[bool] = None				,     UpperCAmelCase_	:			Optional[Union[float, List[float]]] = None				,     UpperCAmelCase_	:			Optional[Union[float, List[float]]] = None				,     UpperCAmelCase_	:			Optional[Union[str, TensorType]] = None				,     UpperCAmelCase_	:			Union[str, ChannelDimension] = ChannelDimension.FIRST				,     **UpperCAmelCase_	:			Union[str, Any]				,     )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			do_resize if do_resize is not None else self.do_resize
											_lowerCAmelCase  	=			size if size is not None else self.size
											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_				,     default_to_square=UpperCAmelCase_  )
											_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  	=			crop_size if crop_size is not None else self.crop_size
											_lowerCAmelCase  	=			get_size_dict(UpperCAmelCase_  )
											_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  	=			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_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(UpperCAmelCase_  ) for image in images]

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

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

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

											if do_normalize:
																		_lowerCAmelCase  	=			[self.normalize(image=UpperCAmelCase_				,     mean=UpperCAmelCase_				,     std=UpperCAmelCase_  ) 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_  )








"""simple docstring"""



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_snake_case          =						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''')
_snake_case          =						parser.parse_args()


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

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

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

# optimize with ipex
_snake_case          =						torch.randn(2, 4, 6_4, 6_4)
_snake_case          =						torch.rand(1) * 9_9_9
_snake_case          =						torch.randn(2, 7_7, 7_6_8)
_snake_case          =						(sample, timestep, encoder_hidden_status)
try:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
	_snake_case          =						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_snake_case          =						6_6_6
_snake_case          =						torch.Generator(device).manual_seed(seed)
_snake_case          =						{'''generator''': generator}
if args.steps is not None:
	_snake_case          =						args.steps

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

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






