




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


_lowercase		:				Tuple	       =		logging.get_logger(__name__)

_lowercase		:				Any	       =		{
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''distilbert'''
			UpperCamelCase__       = {
			    '''hidden_size''': '''dim''',
			    '''num_attention_heads''': '''n_heads''',
			    '''num_hidden_layers''': '''n_layers''',
			}







			def __init__(			self       :						List[Any]       ,    lowercase_       :						str=30522       ,    lowercase_       :						List[Any]=512       ,    lowercase_       :						Any=False       ,    lowercase_       :						List[str]=6       ,    lowercase_       :						str=12       ,    lowercase_       :						Optional[Any]=768       ,    lowercase_       :						Any=4 * 768       ,    lowercase_       :						Optional[int]=0.1       ,    lowercase_       :						int=0.1       ,    lowercase_       :						Any="gelu"       ,    lowercase_       :						Tuple=0.02       ,    lowercase_       :						List[str]=0.1       ,    lowercase_       :						Dict=0.2       ,    lowercase_       :						str=0       ,    **lowercase_       :						List[str]       ,    ):
										lowercase_      :    Dict       		= vocab_size
										lowercase_      :    str       		= max_position_embeddings
										lowercase_      :    Optional[Any]       		= sinusoidal_pos_embds
										lowercase_      :    Optional[Any]       		= n_layers
										lowercase_      :    List[Any]       		= n_heads
										lowercase_      :    int       		= dim
										lowercase_      :    str       		= hidden_dim
										lowercase_      :    Optional[Any]       		= dropout
										lowercase_      :    Any       		= attention_dropout
										lowercase_      :    Dict       		= activation
										lowercase_      :    Tuple       		= initializer_range
										lowercase_      :    str       		= qa_dropout
										lowercase_      :    List[str]       		= seq_classif_dropout
										super().__init__(**lowercase_       ,    pad_token_id=lowercase_					)





class 					__magic_name__		(					_UpperCAmelCase):







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]					):
										if self.task == "multiple-choice":
																	lowercase_      :    Any       		= {0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
																	lowercase_      :    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		:				Dict	       =		parser.parse_args()


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

_lowercase		:				Any	       =		"path-to-your-trained-model"
_lowercase		:				str	       =		StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
						_lowercase		:				Optional[int]	       =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_lowercase		:				Any	       =		pipe.to(device)

# to channels last
_lowercase		:				Union[str, Any]	       =		pipe.unet.to(memory_format=torch.channels_last)
_lowercase		:				List[Any]	       =		pipe.vae.to(memory_format=torch.channels_last)
_lowercase		:				Union[str, Any]	       =		pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
						_lowercase		:				Tuple	       =		pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
_lowercase		:				int	       =		666
_lowercase		:				Any	       =		torch.Generator(device).manual_seed(seed)
_lowercase		:				int	       =		{"generator": generator}
if args.steps is not None:
						_lowercase		:				Optional[int]	       =		args.steps

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

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





