


'''simple docstring'''
import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
__magic_name__ : List[str]					=object()

# For specifying empty leaf dict `{}`
__magic_name__ : Dict					=object()




def   __snake_case							(							lowerCamelCase_			:					Optional[int]						,	lowerCamelCase_			:					Optional[Any]    ):
				'''simple docstring'''




				__magic_name__							=			tuple((re.compile(x + "$"    ) for x in qs)    )
				for i in range(len(lowercase__    ) - len(lowercase__    ) + 1    ):
								__magic_name__							=			[x.match(lowercase__    ) for x, y in zip(lowercase__						,	ks[i:]    )]
								if matches and all(lowercase__    ):
												return True
				return False




def   __snake_case							(							lowerCamelCase_			:					List[Any]    ):
				'''simple docstring'''




				def replace(lowerCamelCase_			:					Dict						,	lowerCamelCase_			:					Any    ):
								for rule, replacement in rules:
												if _match(lowercase__						,	lowercase__    ):
																return replacement
								return val

				return replace




def   __snake_case							(							):
				'''simple docstring'''




				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P("mp"						,	lowercase__    )),
				    (("transformer", "wte", "embedding"), P("mp"						,	lowercase__    )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowercase__						,	"mp"    )),
				    (("attention", "out_proj", "kernel"), P("mp"						,	lowercase__    )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(lowercase__						,	"mp"    )),
				    (("mlp", "c_fc", "bias"), P("mp"    )),
				    (("mlp", "c_proj", "kernel"), P("mp"						,	lowercase__    )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]




def   __snake_case							(							lowerCamelCase_			:					Optional[Any]    ):
				'''simple docstring'''




				__magic_name__							=			_get_partition_rules()
				__magic_name__							=			_replacement_rules(lowercase__    )
				__magic_name__							=			{k: _unmatched for k in flatten_dict(lowercase__    )}
				__magic_name__							=			{k: replace(lowercase__						,	lowercase__    ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(lowercase__    )    )









"""simple docstring"""






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__			=		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__			=		parser.parse_args()


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

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

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

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

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

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

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