



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__  :  Any								=					logging.get_logger(__name__)

A__  :  List[str]								=					{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''data2vec-text'''




							def __init__(       self     :       Optional[Any]				,				A_     :       Any=3_0_5_2_2				,				A_     :       str=7_6_8				,				A_     :       Tuple=1_2				,				A_     :       Tuple=1_2				,				A_     :       int=3_0_7_2				,				A_     :       Any="gelu"				,				A_     :       int=0.1				,				A_     :       Dict=0.1				,				A_     :       Union[str, Any]=5_1_2				,				A_     :       List[str]=2				,				A_     :       int=0.02				,				A_     :       Tuple=1e-12				,				A_     :       Dict=1				,				A_     :       str=0				,				A_     :       Any=2				,				A_     :       str="absolute"				,				A_     :       str=True				,				A_     :       List[Any]=None				,				**A_     :       List[Any]				,				):
									super().__init__(pad_token_id=A_				,				bos_token_id=A_				,				eos_token_id=A_				,				**A_)

									lowerCAmelCase_			:     str									= vocab_size
									lowerCAmelCase_			:     Optional[int]									= hidden_size
									lowerCAmelCase_			:     Optional[int]									= num_hidden_layers
									lowerCAmelCase_			:     Optional[int]									= num_attention_heads
									lowerCAmelCase_			:     Optional[Any]									= hidden_act
									lowerCAmelCase_			:     str									= intermediate_size
									lowerCAmelCase_			:     Dict									= hidden_dropout_prob
									lowerCAmelCase_			:     Union[str, Any]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Optional[Any]									= max_position_embeddings
									lowerCAmelCase_			:     Optional[Any]									= type_vocab_size
									lowerCAmelCase_			:     Optional[Any]									= initializer_range
									lowerCAmelCase_			:     List[str]									= layer_norm_eps
									lowerCAmelCase_			:     Any									= position_embedding_type
									lowerCAmelCase_			:     Any									= use_cache
									lowerCAmelCase_			:     List[str]									= classifier_dropout





class 				__snake_case						(     UpperCamelCase_						):




							@property
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									if self.task == "multiple-choice":
											lowerCAmelCase_			:     Optional[int]									= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
											lowerCAmelCase_			:     Union[str, Any]									= {0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ])







'''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")

