





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, 6_4, 6_4)
A_												=     torch.rand(1) * 9_9_9
A_												=     torch.randn(2, 7_7, 7_6_8)
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_												=     6_6_6
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")








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


A_												=     {
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "albert"





					def __init__( self	,  _lowerCAmelCase=3_0000	,  _lowerCAmelCase=128	,  _lowerCAmelCase=4096	,  _lowerCAmelCase=12	,  _lowerCAmelCase=1	,  _lowerCAmelCase=64	,  _lowerCAmelCase=1_6384	,  _lowerCAmelCase=1	,  _lowerCAmelCase="gelu_new"	,  _lowerCAmelCase=0	,  _lowerCAmelCase=0	,  _lowerCAmelCase=512	,  _lowerCAmelCase=2	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=1E-12	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase="absolute"	,  _lowerCAmelCase=0	,  _lowerCAmelCase=2	,  _lowerCAmelCase=3	,  **_lowerCAmelCase	,  ):
										super().__init__(pad_token_id=_lowerCAmelCase	,  bos_token_id=_lowerCAmelCase	,  eos_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	)

										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       embedding_size
										lowerCamelCase__       =       hidden_size
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_hidden_groups
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       inner_group_num
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       intermediate_size
										lowerCamelCase__       =       hidden_dropout_prob
										lowerCamelCase__       =       attention_probs_dropout_prob
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       type_vocab_size
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       layer_norm_eps
										lowerCamelCase__       =       classifier_dropout_prob
										lowerCamelCase__       =       position_embedding_type

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@property
					def 					__magic_name__ ( self	):
										if self.task == "multiple-choice":
															lowerCamelCase__       =       {0: "batch", 1: "choice", 2: "sequence"}
										else:
															lowerCamelCase__       =       {0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										        ("token_type_ids", dynamic_axis),
										    ]	)

