




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


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"visual_bert"

     def __init__(  self		,     _a=3_0522		,     _a=768		,     _a=512		,     _a=12		,     _a=12		,     _a=3072		,     _a="gelu"		,     _a=0.1		,     _a=0.1		,     _a=512		,     _a=2		,     _a=0.02		,     _a=1e-12		,     _a=False		,     _a=True		,     _a=1		,     _a=0		,     _a=2		,     **_a		,     )						->							Tuple:
           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     eos_token_id=_a		,     **_a				)

           _A							:     int			=						vocab_size
           _A							:     Dict			=						max_position_embeddings
           _A							:     Optional[Any]			=						hidden_size
           _A							:     List[Any]			=						visual_embedding_dim
           _A							:     Optional[Any]			=						num_hidden_layers
           _A							:     Tuple			=						num_attention_heads
           _A							:     str			=						intermediate_size
           _A							:     Dict			=						hidden_act
           _A							:     Union[str, Any]			=						hidden_dropout_prob
           _A							:     Optional[Any]			=						attention_probs_dropout_prob
           _A							:     Optional[int]			=						initializer_range
           _A							:     List[Any]			=						type_vocab_size
           _A							:     int			=						layer_norm_eps
           _A							:     Optional[int]			=						bypass_transformer
           _A							:     List[Any]			=						special_visual_initialize





import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_snake_case             =     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''')
_snake_case             =     parser.parse_args()


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

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

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

# optimize with ipex
_snake_case             =     torch.randn(2, 4, 64, 64)
_snake_case             =     torch.rand(1) * 9_99
_snake_case             =     torch.randn(2, 77, 7_68)
_snake_case             =     (sample, timestep, encoder_hidden_status)
try:
   _snake_case             =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   _snake_case             =     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case             =     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case             =     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   _snake_case             =     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_snake_case             =     6_66
_snake_case             =     torch.Generator(device).manual_seed(seed)
_snake_case             =     {'''generator''': generator}
if args.steps is not None:
   _snake_case             =     args.steps

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

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



