


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 				UpperCAmelCase_					(							)     ->			Optional[Any]:







 """simple docstring"""


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







 """simple docstring"""


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

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

 for i, img in enumerate(__snake_case						):
  grid.paste(__snake_case     ,						box=(i % cols * w, i // cols * h)						)
 return grid
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case="robotic cat with wings"     ,						__snake_case=7.5     ,						__snake_case=50     ,						__snake_case=1     ,						__snake_case=42     ,						)     ->			Optional[int]:







 """simple docstring"""


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


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

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

from collections.abc import Callable

UpperCAmelCase__   	=							list[list[float | int]]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Matrix:







 """simple docstring"""


 _lowercase							      =len(__snake_case						)
 _lowercase							      =[[0 for _ in range(size + 1						)] for _ in range(__snake_case						)]
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42

 for row in range(__snake_case						):
  for col in range(__snake_case						):
   _lowercase							      =matrix[row][col]

  _lowercase							      =vector[row][0]

 _lowercase							      =0
 _lowercase							      =0
 while row < size and col < size:
  # pivoting
  _lowercase							      =max((abs(augmented[rowa][col]						), rowa) for rowa in range(__snake_case     ,						__snake_case						)						)[
      1
  ]
  if augmented[pivot_row][col] == 0:
   col += 1
   continue
  else:
   _lowercase      ,    _lowercase							      =augmented[pivot_row], augmented[row]

  for rowa in range(row + 1     ,						__snake_case						):
   _lowercase							      =augmented[rowa][col] / augmented[row][col]
   _lowercase							      =0
   for cola in range(col + 1     ,						size + 1						):
    augmented[rowa][cola] -= augmented[row][cola] * ratio

  row += 1
  col += 1

 # back substitution
 for col in range(1     ,						__snake_case						):
  for row in range(__snake_case						):
   _lowercase							      =augmented[row][col] / augmented[col][col]
   for cola in range(__snake_case     ,						size + 1						):
    augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
 return [
     [round(augmented[row][size] / augmented[row][row]     ,						10						)] for row in range(__snake_case						)
 ]
def 				UpperCAmelCase_					(							__snake_case						)     ->			Callable[[int], int]:







 """simple docstring"""


 _lowercase							      =len(__snake_case						)
 _lowercase							      =[[0 for _ in range(__snake_case						)] for _ in range(__snake_case						)]
 _lowercase							      =[[0] for _ in range(__snake_case						)]
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42

 for x_val, y_val in enumerate(__snake_case						):
  for col in range(__snake_case						):
   _lowercase							      =(x_val + 1) ** (size - col - 1)
  _lowercase							      =y_val

 _lowercase							      =solve(__snake_case     ,						__snake_case						)

 def interpolated_func(__snake_case						) -> int:
  return sum(
      round(coeffs[x_val][0]						) * (var ** (size - x_val - 1))
      for x_val in range(__snake_case						)						)

 return interpolated_func
def 				UpperCAmelCase_					(							__snake_case						)     ->			int:







 """simple docstring"""


 return (
     1
     - variable
     + variable**2
     - variable**3
     + variable**4
     - variable**5
     + variable**6
     - variable**7
     + variable**8
     - variable**9
     + variable**10
 )
def 				UpperCAmelCase_					(							__snake_case = question_function     ,						__snake_case = 10						)     ->			int:







 """simple docstring"""


 _lowercase							      =[func(__snake_case						) for x_val in range(1     ,						order + 1						)]

 _lowercase							      =[
     interpolate(data_points[:max_coeff]						) for max_coeff in range(1     ,						order + 1						)
 ]

 _lowercase							      =0
 _lowercase							      =42
 _lowercase							      =42

 for poly in polynomials:
  _lowercase							      =1
  while func(__snake_case						) == poly(__snake_case						):
   x_val += 1

  ret += poly(__snake_case						)

 return ret


if __name__ == "__main__":
  print(f'''{solution() = }''')

