




import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


UpperCAmelCase_		:					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")
UpperCAmelCase_		:					int          =  parser.parse_args()


UpperCAmelCase_		:					Union[str, Any]          =  "cpu"
UpperCAmelCase_		:					int          =  "a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

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

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

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

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

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

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








import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


SCREAMING_SNAKE_CASE							    =							{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class     __UpperCAmelCase							(       nn.Module							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  torchvision.models.resnetaaa(pretrained=__A    )
        __a					=  list(model.children()    )[:-2]
        __a					=  nn.Sequential(*__A    )
        __a					=  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )


     def 	snake_case_  (	self  ,       __A    ):
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        __a					=  self.pool(self.model(__A    )    )
        __a					=  torch.flatten(__A  ,       start_dim=2    )
        __a					=  out.transpose(1  ,       2    ).contiguous()
        return out  # BxNx2048







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        __a					=  [json.loads(__A    ) for l in open(__A    )]
        __a					=  os.path.dirname(__A    )
        __a					=  tokenizer
        __a					=  labels
        __a					=  len(__A    )
        __a					=  max_seq_length

        __a					=  transforms


     def __len__(	self    ):
        return len(self.data    )


     def __getitem__(	self  ,       __A    ):
        __a					=  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]  ,       add_special_tokens=__A    )    )
        __a	,    __a	,    __a					=  sentence[0], sentence[1:-1], sentence[-1]
        __a					=  sentence[: self.max_seq_length]

        __a					=  torch.zeros(self.n_classes    )
        __a					=  1

        __a					=  Image.open(os.path.join(self.data_dir  ,       self.data[index]["""img"""]    )    ).convert("""RGB"""    )
        __a					=  self.transforms(__A    )

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }


     def 	snake_case_  (	self    ):
        __a					=  Counter()
        for row in self.data:
           label_freqs.update(row["""label"""]    )
        return label_freqs







def  a   (lowerCAmelCase__   ):
   __a					=  [len(row["""sentence"""]   ) for row in batch]
   __a	,    __a					=  len(lowerCAmelCase__   ), max(lowerCAmelCase__   )

   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )
   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )

   for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase__		,				lowerCAmelCase__   )   ):
      __a					=  input_row["""sentence"""]
      __a					=  1

   __a					=  torch.stack([row["""image"""] for row in batch]   )
   __a					=  torch.stack([row["""label"""] for row in batch]   )
   __a					=  torch.stack([row["""image_start_token"""] for row in batch]   )
   __a					=  torch.stack([row["""image_end_token"""] for row in batch]   )

   return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def  a   ():
   return [
       "Crime",
       "Drama",
       "Thriller",
       "Action",
       "Comedy",
       "Romance",
       "Documentary",
       "Short",
       "Mystery",
       "History",
       "Family",
       "Adventure",
       "Fantasy",
       "Sci-Fi",
       "Western",
       "Horror",
       "Sport",
       "War",
       "Music",
       "Musical",
       "Animation",
       "Biography",
       "Film-Noir",
   ]




def  a   ():
   return transforms.Compose(
       [
           transforms.Resize(256   ),
           transforms.CenterCrop(224   ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]		,				std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]		,				),
       ]   )



