'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase		:				Any	       =		logging.get_logger(__name__)

_lowercase		:				str	       =		{
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''xglm'''
			UpperCamelCase__       = ['''past_key_values''']

			UpperCamelCase__       = {
			    '''num_attention_heads''': '''attention_heads''',
			    '''hidden_size''': '''d_model''',
			    '''num_hidden_layers''': '''num_layers''',
			}







			def __init__(			self       :						Union[str, Any]       ,    lowercase_       :						List[Any]=256008       ,    lowercase_       :						Any=2048       ,    lowercase_       :						Any=1024       ,    lowercase_       :						Any=4096       ,    lowercase_       :						Optional[Any]=24       ,    lowercase_       :						str=16       ,    lowercase_       :						Any="gelu"       ,    lowercase_       :						Tuple=0.1       ,    lowercase_       :						str=0.1       ,    lowercase_       :						Union[str, Any]=0.0       ,    lowercase_       :						Dict=0.0       ,    lowercase_       :						List[Any]=0.02       ,    lowercase_       :						Dict=True       ,    lowercase_       :						List[Any]=True       ,    lowercase_       :						List[str]=2       ,    lowercase_       :						int=1       ,    lowercase_       :						int=0       ,    lowercase_       :						List[str]=2       ,    **lowercase_       :						Tuple       ,    ):
										lowercase_      :    Dict       		= vocab_size
										lowercase_      :    Tuple       		= max_position_embeddings
										lowercase_      :    str       		= d_model
										lowercase_      :    Tuple       		= ffn_dim
										lowercase_      :    Dict       		= num_layers
										lowercase_      :    int       		= attention_heads
										lowercase_      :    Union[str, Any]       		= activation_function
										lowercase_      :    Any       		= dropout
										lowercase_      :    Any       		= attention_dropout
										lowercase_      :    int       		= activation_dropout
										lowercase_      :    Optional[int]       		= layerdrop
										lowercase_      :    List[str]       		= init_std
										lowercase_      :    Any       		= scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase_      :    Optional[int]       		= use_cache

										super().__init__(
										    pad_token_id=lowercase_       ,    bos_token_id=lowercase_       ,    eos_token_id=lowercase_       ,    decoder_start_token_id=lowercase_       ,    **lowercase_       ,    )






'''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")





