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   __A		(			):



 """simple docstring"""


 __a       =  argparse.ArgumentParser()
 parser.add_argument(
     "-m"							,				"--pretrained_model_name_or_path"							,				type=_lowerCamelCase							,				default=_lowerCamelCase							,				required=_lowerCamelCase							,				help="Path to pretrained model or model identifier from huggingface.co/models."							,				)
 parser.add_argument(
     "-c"							,				"--caption"							,				type=_lowerCamelCase							,				default="robotic cat with wings"							,				help="Text used to generate images."							,				)
 parser.add_argument(
     "-n"							,				"--images_num"							,				type=_lowerCamelCase							,				default=4							,				help="How much images to generate."							,				)
 parser.add_argument(
     "-s"							,				"--seed"							,				type=_lowerCamelCase							,				default=42							,				help="Seed for random process."							,				)
 parser.add_argument(
     "-ci"							,				"--cuda_id"							,				type=_lowerCamelCase							,				default=0							,				help="cuda_id."							,				)
 __a       =  parser.parse_args()
 return args



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


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

 __a       =  imgs[0].size
 __a       =  Image.new("RGB"							,				size=(cols * w, rows * h)					)
 __a       =  grid.size

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



def   __A		(			_A							,				_A="robotic cat with wings"							,				_A=7.5							,				_A=50							,				_A=1							,				_A=42							,				):



 """simple docstring"""


 __a       =  torch.Generator(pipeline.device					).manual_seed(_lowerCamelCase					)
 __a       =  pipeline(
     _lowerCamelCase							,				guidance_scale=_lowerCamelCase							,				num_inference_steps=_lowerCamelCase							,				generator=_lowerCamelCase							,				num_images_per_prompt=_lowerCamelCase							,				).images
 __a       =  int(math.sqrt(_lowerCamelCase					)					)
 __a       =  image_grid(_lowerCamelCase							,				rows=_rows							,				cols=num_images_per_prompt // _rows					)
 return grid, images


SCREAMING_SNAKE_CASE :	str							     =		parse_args()
# Load models and create wrapper for stable diffusion
SCREAMING_SNAKE_CASE :	List[str]							     =		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
SCREAMING_SNAKE_CASE :	List[Any]							     =		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
SCREAMING_SNAKE_CASE :	Dict							     =		AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
SCREAMING_SNAKE_CASE :	List[Any]							     =		UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

SCREAMING_SNAKE_CASE :	Dict							     =		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE :	Tuple							     =		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
  SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		load(args.pretrained_model_name_or_path, model=unet)
  unet.eval()
  setattr(pipeline, """unet""", unet)
else:
  SCREAMING_SNAKE_CASE :	int							     =		unet.to(torch.device("""cuda""", args.cuda_id))
SCREAMING_SNAKE_CASE :	Optional[Any]							     =		pipeline.to(unet.device)
SCREAMING_SNAKE_CASE :	str							     =		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()))))
SCREAMING_SNAKE_CASE :	Optional[Any]							     =		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
class    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  order

   # a_{0} ... a_{k}
   __a       =  [1.0] + [0.0] * order
   # b_{0} ... b_{k}
   __a       =  [1.0] + [0.0] * order

   # x[n-1] ... x[n-k]
   __a       =  [0.0] * self.order
   # y[n-1] ... y[n-k]
   __a       =  [0.0] * self.order

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      list[float]     ,							__SCREAMING_SNAKE_CASE :      list[float]       ):
   if len(__SCREAMING_SNAKE_CASE       ) < self.order:
    __a       =  [1.0, *a_coeffs]

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected a_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected b_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   __a       =  a_coeffs
   __a       =  b_coeffs

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      float       ):
   __a       =  0.0

   # Start at index 1 and do index 0 at the end.
   for i in range(1     ,							self.order + 1       ):
    result += (
        self.b_coeffs[i] * self.input_history[i - 1]
        - self.a_coeffs[i] * self.output_history[i - 1]
    )

   __a       =  (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

   __a       =  self.input_history[:-1]
   __a       =  self.output_history[:-1]

   __a       =  sample
   __a       =  result

   return result


