





"""simple docstring"""
import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel



def      __lowerCamelCase   (							)			->      str:
      __SCREAMING_SNAKE_CASE   :Any						=  argparse.ArgumentParser()
      parser.add_argument(
          '''-m''' ,   '''--pretrained_model_name_or_path''' ,   type=a_ ,   default=a_ ,   required=a_ ,   help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,   )
      parser.add_argument(
          '''-c''' ,   '''--caption''' ,   type=a_ ,   default='''robotic cat with wings''' ,   help='''Text used to generate images.''' ,   )
      parser.add_argument(
          '''-n''' ,   '''--images_num''' ,   type=a_ ,   default=4 ,   help='''How much images to generate.''' ,   )
      parser.add_argument(
          '''-s''' ,   '''--seed''' ,   type=a_ ,   default=42 ,   help='''Seed for random process.''' ,   )
      parser.add_argument(
          '''-ci''' ,   '''--cuda_id''' ,   type=a_ ,   default=0 ,   help='''cuda_id.''' ,   )
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  parser.parse_args()
      return args



def      __lowerCamelCase   (							a_     :		Tuple ,   a_     :		str ,   a_     :		str							)			->      Optional[Any]:
      if not len(a_							) == rows * cols:
            raise ValueError('''The specified number of rows and columns are not correct.'''							)

      __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :int						=  imgs[0].size
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  Image.new('''RGB''' ,   size=(cols * w, rows * h)							)
      __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :str						=  grid.size

      for i, img in enumerate(a_							):
            grid.paste(a_ ,   box=(i % cols * w, i // cols * h)							)
      return grid



def      __lowerCamelCase   (							a_     :		str ,   a_     :		int="robotic cat with wings" ,   a_     :		List[Any]=7.5 ,   a_     :		Any=50 ,   a_     :		Optional[Any]=1 ,   a_     :		Optional[Any]=42 ,   )			->      Union[str, Any]:
      __SCREAMING_SNAKE_CASE   :str						=  torch.Generator(pipeline.device							).manual_seed(a_							)
      __SCREAMING_SNAKE_CASE   :str						=  pipeline(
          a_ ,   guidance_scale=a_ ,   num_inference_steps=a_ ,   generator=a_ ,   num_images_per_prompt=a_ ,   ).images
      __SCREAMING_SNAKE_CASE   :List[Any]						=  int(math.sqrt(a_							)							)
      __SCREAMING_SNAKE_CASE   :Tuple						=  image_grid(a_ ,   rows=_rows ,   cols=num_images_per_prompt // _rows							)
      return grid, images


lowerCamelCase_								=		parse_args()
# Load models and create wrapper for stable diffusion
lowerCamelCase_								=		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
lowerCamelCase_								=		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
lowerCamelCase_								=		AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
lowerCamelCase_								=		UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

lowerCamelCase_								=		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCamelCase_								=		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
  lowerCamelCase_								=		load(args.pretrained_model_name_or_path, model=unet)
  unet.eval()
  setattr(pipeline, "unet", unet)
else:
  lowerCamelCase_								=		unet.to(torch.device("cuda", args.cuda_id))
lowerCamelCase_								=		pipeline.to(unet.device)
lowerCamelCase_			,     lowerCamelCase_								=		generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
lowerCamelCase_								=		os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
  image.save(os.path.join(dirname, "{}.png".format(idx + 1)))






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  '''llama'''
       SCREAMING_SNAKE_CASE_   :      Optional[int]							=  ['''past_key_values''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3_20_00		,SCREAMING_SNAKE_CASE__=40_96		,SCREAMING_SNAKE_CASE__=1_10_08		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__="silu"		,SCREAMING_SNAKE_CASE__=20_48		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-6		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  vocab_size
             __SCREAMING_SNAKE_CASE   :int						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :List[str]						=  hidden_size
             __SCREAMING_SNAKE_CASE   :Tuple						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[Any]						=  num_attention_heads

             # for backward compatibility
             if num_key_value_heads is None:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  num_attention_heads

             __SCREAMING_SNAKE_CASE   :str						=  num_key_value_heads
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :List[str]						=  initializer_range
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  rms_norm_eps
             __SCREAMING_SNAKE_CASE   :Dict						=  pretraining_tp
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  use_cache
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  rope_scaling
             self._rope_scaling_validation()

             super().__init__(
                 pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,tie_word_embeddings=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__		,)




       def     _UpperCamelCase     (						self							)			->      List[Any]:





             """simple docstring"""






             if self.rope_scaling is None:
                   return

             if not isinstance(self.rope_scaling		,SCREAMING_SNAKE_CASE__							) or len(self.rope_scaling							) != 2:
                   raise ValueError(
                       '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                       f'''got {self.rope_scaling}'''							)
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.rope_scaling.get('''type'''		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.rope_scaling.get('''factor'''		,SCREAMING_SNAKE_CASE__							)
             if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
                   raise ValueError(
                       f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''							)
             if rope_scaling_factor is None or not isinstance(SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							) or rope_scaling_factor <= 1.0:
                   raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''							)