






import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowerCamelCase			:					Tuple         =	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''')
lowerCamelCase			:					Tuple         =	parser.parse_args()


lowerCamelCase			:					List[Any]         =	'''cpu'''
lowerCamelCase			:					Any         =	'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

lowerCamelCase			:					Optional[int]         =	'''path-to-your-trained-model'''
lowerCamelCase			:					Dict         =	StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
      lowerCamelCase			:					Dict         =	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowerCamelCase			:					Tuple         =	pipe.to(device)

# to channels last
lowerCamelCase			:					Dict         =	pipe.unet.to(memory_format=torch.channels_last)
lowerCamelCase			:					List[str]         =	pipe.vae.to(memory_format=torch.channels_last)
lowerCamelCase			:					Any         =	pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
      lowerCamelCase			:					Optional[int]         =	pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
lowerCamelCase			:					List[str]         =	666
lowerCamelCase			:					Optional[Any]         =	torch.Generator(device).manual_seed(seed)
lowerCamelCase			:					Union[str, Any]         =	{'''generator''': generator}
if args.steps is not None:
      lowerCamelCase			:					Tuple         =	args.steps

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

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














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


lowerCamelCase			:					List[str]         =	logging.get_logger(__name__)

lowerCamelCase			:					Dict         =	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""biogpt"""


      def __init__(	self					,						__UpperCamelCase=4_2_3_8_4					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=2_4					,						__UpperCamelCase=1_6					,						__UpperCamelCase=4_0_9_6					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Optional[Any]:
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					scale_embedding
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					layerdrop
           __lowerCAmelCase								=					activation_dropout
           super().__init__(pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase	)






