





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_ (       )     ->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:      Optional[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."	,    )
  lowerCamelCase__:      Union[str, Any]			  =parser.parse_args()
  return args





def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Tuple:







  """simple docstring"""





  if not len(__a		) == rows * cols:
    raise ValueError("The specified number of rows and columns are not correct."		)

  lowerCamelCase__  ,					lowerCamelCase__:      Optional[int]			  =imgs[0].size
  lowerCamelCase__:      Tuple			  =Image.new("RGB"	,    size=(cols * w, rows * h)		)
  lowerCamelCase__  ,					lowerCamelCase__:      Tuple			  =grid.size

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





def      lowerCAmelCase_ (       __a	,    __a="robotic cat with wings"	,    __a=7.5	,    __a=50	,    __a=1	,    __a=42	,    )     ->		str:







  """simple docstring"""





  lowerCamelCase__:      List[str]			  =torch.Generator(pipeline.device		).manual_seed(__a		)
  lowerCamelCase__:      List[Any]			  =pipeline(
      __a	,    guidance_scale=__a	,    num_inference_steps=__a	,    generator=__a	,    num_images_per_prompt=__a	,    ).images
  lowerCamelCase__:      List[str]			  =int(math.sqrt(__a		)		)
  lowerCamelCase__:      Dict			  =image_grid(__a	,    rows=_rows	,    cols=num_images_per_prompt // _rows		)
  return grid, images


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

__A									=				StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__A									=				lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
     __A									=				load(args.pretrained_model_name_or_path, model=unet)
     unet.eval()
     setattr(pipeline, "unet", unet)
else:
     __A									=				unet.to(torch.device("cuda", args.cuda_id))
__A									=				pipeline.to(unet.device)
__A					,	__A									=				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()))))
__A									=				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)))


from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

UpperCamelCase  							=						TypeVar('''T''')



class   snake_case_       (		Generic[T]       ):
   __A	:		deque[T]  # Cache store of keys
   __A	:		set[T]  # References of the keys in cache
   __A	:		int         =				10  # Maximum capacity of cache






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     int  )					->						None:
       lowercase__ :   int    					=				deque()
       lowercase__ :   str    					=				set()
       if not n:
           lowercase__ :   str    					=				sys.maxsize
       elif n < 0:
           raise ValueError("n should be an integer greater than 0."  )
       else:
           lowercase__ :   List[Any]    					=				n






   def       __UpperCamelCase     (      self				:     Dict	,						lowercase_				:     T  )					->						None:
       if x not in self.key_reference:
           if len(self.dq_store  ) == LRUCache._MAX_CAPACITY:
               lowercase__ :   Dict    					=				self.dq_store.pop()
               self.key_reference.remove(lowercase_  )
       else:
           self.dq_store.remove(lowercase_  )

       self.dq_store.appendleft(lowercase_  )
       self.key_reference.add(lowercase_  )






   def       __UpperCamelCase     (      self				:     Dict  )					->						None:
       for k in self.dq_store:
           print(lowercase_  )







   def __repr__(      self				:     Optional[int]  )					->						str:
       return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store  )}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase  							=						LRUCache(4)
      lru_cache.refer('''A''')
      lru_cache.refer(2)
      lru_cache.refer(3)
      lru_cache.refer('''A''')
      lru_cache.refer(4)
      lru_cache.refer(5)
      lru_cache.display()

      print(lru_cache)
      assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"



