






'''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  __UpperCAmelCase						(					)	->      List[str]:

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Optional[Any]     )	->      Optional[int]:

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

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

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						Optional[Any]="robotic cat with wings",       SCREAMING_SNAKE_CASE__:						Tuple=7.5,       SCREAMING_SNAKE_CASE__:						str=50,       SCREAMING_SNAKE_CASE__:						Tuple=1,       SCREAMING_SNAKE_CASE__:						Optional[int]=42,       )	->      Any:

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


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

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







'''simple docstring'''



import os



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str = "matrix.txt"     )	->      int:

   """simple docstring"""
   with open(os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE__     ),       SCREAMING_SNAKE_CASE__     )     ) as in_file:
      __a					  =     in_file.read()

   __a					  =     [[int(SCREAMING_SNAKE_CASE__     ) for cell in row.split(','     )] for row in data.strip().splitlines()]
   __a					  =     [[0 for cell in row] for row in grid]
   __a					  =     len(grid[0]     )

   __a					  =     [[0 for i in range(SCREAMING_SNAKE_CASE__     )] for j in range(SCREAMING_SNAKE_CASE__     )]
   __a					  =     grid[0][0]
   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     grid[0][i] + dp[0][i - 1]
   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      __a					  =     grid[i][0] + dp[i - 1][0]

   for i in range(1,       SCREAMING_SNAKE_CASE__     ):
      for j in range(1,       SCREAMING_SNAKE_CASE__     ):
         __a					  =     grid[i][j] + min(dp[i - 1][j],       dp[i][j - 1]     )

   return dp[-1][-1]


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