import os
def 		snake_case__	(		lowercase = "input.txt"					):

   with open(os.path.join(os.path.dirname(lowercase					)							,				lowercase					)					) as input_file:
      lowerCAmelCase_:							Dict       =	[
          [int(lowercase					) for element in line.split(","					)]
          for line in input_file.readlines()
      ]

   lowerCAmelCase_:							int       =	len(lowercase					)
   lowerCAmelCase_:							Any       =	len(matrix[0]					)

   lowerCAmelCase_:							List[Any]       =	[[-1 for _ in range(lowercase					)] for _ in range(lowercase					)]
   for i in range(lowercase					):
      lowerCAmelCase_:							Union[str, Any]       =	matrix[i][0]

   for j in range(1							,				lowercase					):
      for i in range(lowercase					):
         lowerCAmelCase_:							str       =	minimal_path_sums[i][j - 1] + matrix[i][j]

      for i in range(1							,				lowercase					):
         lowerCAmelCase_:							Optional[Any]       =	min(
             minimal_path_sums[i][j]							,				minimal_path_sums[i - 1][j] + matrix[i][j]					)

      for i in range(rows - 2							,				-1							,				-1					):
         lowerCAmelCase_:							Optional[int]       =	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() = }''')
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a  :      List[str]            =    False

class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    pass





@nightly
@require_torch_gpu
class 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''




    def      _a    (    self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							List[str]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCamelCase__ )
          lowerCAmelCase_:							int       =	VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__ )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	generator.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=2       ,						output_type="numpy" ).images

       assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"




    def      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	VersatileDiffusionTextToImagePipeline.from_pretrained(
           "shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							str       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							int       =	torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy" ).images

       lowerCAmelCase_:							List[Any]       =	image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCAmelCase_:							Optional[int]       =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2