

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      (			)    ->      Dict:





      """simple docstring"""



      _lowercase	       =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.'      ,   )
      _lowercase	       =parser.parse_args()
      return args
def      a      (			A__	:   List[Any]      ,   A__	:   str      ,   A__	:   Any     )    ->      Dict:





      """simple docstring"""



      if not len(A__     ) == 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(A__     ):
            grid.paste(A__      ,   box=(i % cols * w, i // cols * h)     )
      return grid
def      a      (			A__	:   str      ,   A__	:   Dict="robotic cat with wings"      ,   A__	:   Tuple=7.5      ,   A__	:   Any=50      ,   A__	:   str=1      ,   A__	:   List[Any]=42      ,   )    ->      int:





      """simple docstring"""



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


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

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





import os
def      a      (			A__	:   str = "input.txt"     )    ->      int:





      """simple docstring"""



      with open(os.path.join(os.path.dirname(A__     )      ,   A__     )     ) as input_file:
            _lowercase	       =[
                [int(A__     ) for element in line.split(','     )]
                for line in input_file.readlines()
            ]

      _lowercase	       =len(A__     )
      _lowercase	       =len(matrix[0]     )

      _lowercase	       =[[-1 for _ in range(A__     )] for _ in range(A__     )]
      for i in range(A__     ):
            _lowercase	       =matrix[i][0]

      for j in range(1      ,   A__     ):
            for i in range(A__     ):
                  _lowercase	       =minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1      ,   A__     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i - 1][j] + matrix[i][j]     )

            for i in range(rows - 2      ,   -1      ,   -1     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i + 1][j] + matrix[i][j]     )

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums     )


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


