


'''simple docstring'''
import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCamelCase__    					=      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''')
UpperCamelCase__    					=      parser.parse_args()


UpperCamelCase__    					=      '''cpu'''
UpperCamelCase__    					=      '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

UpperCamelCase__    					=      '''path-to-your-trained-model'''
UpperCamelCase__    					=      StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
  UpperCamelCase__    					=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
UpperCamelCase__    					=      pipe.to(device)

# to channels last
UpperCamelCase__    					=      pipe.unet.to(memory_format=torch.channels_last)
UpperCamelCase__    					=      pipe.vae.to(memory_format=torch.channels_last)
UpperCamelCase__    					=      pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  UpperCamelCase__    					=      pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
UpperCamelCase__    					=      torch.randn(2, 4, 6_4, 6_4)
UpperCamelCase__    					=      torch.rand(1) * 9_9_9
UpperCamelCase__    					=      torch.randn(2, 7_7, 7_6_8)
UpperCamelCase__    					=      (sample, timestep, encoder_hidden_status)
try:
  UpperCamelCase__    					=      ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
  UpperCamelCase__    					=      ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase__    					=      ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
UpperCamelCase__    					=      ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
  UpperCamelCase__    					=      ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
UpperCamelCase__    					=      6_6_6
UpperCamelCase__    					=      torch.Generator(device).manual_seed(seed)
UpperCamelCase__    					=      {'''generator''': generator}
if args.steps is not None:
  UpperCamelCase__    					=      args.steps

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

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



"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__A						   =			logging.get_logger(__name__)

__A						   =			{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  """beit"""

				def __init__(	self							:				List[Any]     ,  UpperCamelCase__							:				List[str]=8_1_9_2     ,  UpperCamelCase__							:				Dict=7_6_8     ,  UpperCamelCase__							:				List[str]=1_2     ,  UpperCamelCase__							:				Union[str, Any]=1_2     ,  UpperCamelCase__							:				Dict=3_0_7_2     ,  UpperCamelCase__							:				Optional[int]="gelu"     ,  UpperCamelCase__							:				Tuple=0.0     ,  UpperCamelCase__							:				Tuple=0.0     ,  UpperCamelCase__							:				Union[str, Any]=0.02     ,  UpperCamelCase__							:				Optional[Any]=1e-12     ,  UpperCamelCase__							:				str=2_2_4     ,  UpperCamelCase__							:				str=1_6     ,  UpperCamelCase__							:				Union[str, Any]=3     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				int=False     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				Dict=0.1     ,  UpperCamelCase__							:				List[Any]=0.1     ,  UpperCamelCase__							:				Any=True     ,  UpperCamelCase__							:				Optional[Any]=[3, 5, 7, 1_1]     ,  UpperCamelCase__							:				Optional[Any]=[1, 2, 3, 6]     ,  UpperCamelCase__							:				Optional[Any]=True     ,  UpperCamelCase__							:				Tuple=0.4     ,  UpperCamelCase__							:				Optional[Any]=2_5_6     ,  UpperCamelCase__							:				Optional[int]=1     ,  UpperCamelCase__							:				Optional[int]=False     ,  UpperCamelCase__							:				Optional[Any]=2_5_5     ,  **UpperCamelCase__							:				Optional[int]     ,  )->   int:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase:       str          =			vocab_size
						__lowerCAmelCase:       List[Any]          =			hidden_size
						__lowerCAmelCase:       str          =			num_hidden_layers
						__lowerCAmelCase:       Tuple          =			num_attention_heads
						__lowerCAmelCase:       Union[str, Any]          =			intermediate_size
						__lowerCAmelCase:       List[Any]          =			hidden_act
						__lowerCAmelCase:       Optional[Any]          =			hidden_dropout_prob
						__lowerCAmelCase:       List[Any]          =			attention_probs_dropout_prob
						__lowerCAmelCase:       str          =			initializer_range
						__lowerCAmelCase:       Optional[Any]          =			layer_norm_eps

						__lowerCAmelCase:       Any          =			image_size
						__lowerCAmelCase:       Any          =			patch_size
						__lowerCAmelCase:       Union[str, Any]          =			num_channels
						__lowerCAmelCase:       Tuple          =			use_mask_token
						__lowerCAmelCase:       Optional[Any]          =			use_absolute_position_embeddings
						__lowerCAmelCase:       List[Any]          =			use_relative_position_bias
						__lowerCAmelCase:       Optional[Any]          =			use_shared_relative_position_bias
						__lowerCAmelCase:       List[str]          =			layer_scale_init_value
						__lowerCAmelCase:       str          =			drop_path_rate
						__lowerCAmelCase:       str          =			use_mean_pooling
						# decode head attributes (semantic segmentation)
						__lowerCAmelCase:       Optional[Any]          =			out_indices
						__lowerCAmelCase:       Union[str, Any]          =			pool_scales
						# auxiliary head attributes (semantic segmentation)
						__lowerCAmelCase:       List[str]          =			use_auxiliary_head
						__lowerCAmelCase:       Union[str, Any]          =			auxiliary_loss_weight
						__lowerCAmelCase:       Optional[int]          =			auxiliary_channels
						__lowerCAmelCase:       Dict          =			auxiliary_num_convs
						__lowerCAmelCase:       List[Any]          =			auxiliary_concat_input
						__lowerCAmelCase:       str          =			semantic_loss_ignore_index





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  version.parse("""1.11"""  )

				@property
				def 							lowercase_     (	self							:				str)->   Mapping[str, Mapping[int, str]]:



						'''simple docstring'''


						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						    ])




				@property
				def 							lowercase_     (	self							:				Any)->   float:



						'''simple docstring'''


						return 1e-4


