

'''simple docstring'''



import os







def    a__       (  a__ = "matrix.txt"						):




     """simple docstring"""


     with open(os.path.join(os.path.dirname(a__						)      ,    a__						)						) as in_file:
          __SCREAMING_SNAKE_CASE     =      in_file.read()

     __SCREAMING_SNAKE_CASE     =      [[int(a__						) for cell in row.split(""","""						)] for row in data.strip().splitlines()]
     __SCREAMING_SNAKE_CASE     =      [[0 for cell in row] for row in grid]
     __SCREAMING_SNAKE_CASE     =      len(grid[0]						)

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

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

     return dp[-1][-1]


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









'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 						lowerCAmelCase__   (      a ,					a ,					a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      StableDiffusionInpaintPipeline
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__  					=      frozenset(
     []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowerCAmelCase__  					=      frozenset([]					)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[Any]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      UNetaDConditionModel(
          block_out_channels=(32, 64)					,							layers_per_block=2					,							sample_size=32					,							in_channels=9					,							out_channels=4					,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,							cross_attention_dim=32					,							attention_head_dim=(2, 4)					,							use_linear_projection=__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE			)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      AutoencoderKL(
          block_out_channels=[32, 64]					,							in_channels=3					,							out_channels=3					,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					,							latent_channels=4					,							sample_size=128					,							)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      CLIPTextConfig(
          bos_token_id=0					,							eos_token_id=2					,							hidden_size=32					,							intermediate_size=37					,							layer_norm_eps=1E-05					,							num_attention_heads=4					,							num_hidden_layers=5					,							pad_token_id=1					,							vocab_size=1_000					,							hidden_act="""gelu"""					,							projection_dim=512					,							)
      __SCREAMING_SNAKE_CASE     =      CLIPTextModel(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

      __SCREAMING_SNAKE_CASE     =      {
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components
 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[Any]=0			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      floats_tensor((1, 3, 32, 32)					,							rng=random.Random(__SCREAMING_SNAKE_CASE			)			).to(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      image.cpu().permute(0					,							2					,							3					,							1			)[0]
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE			)			).convert("""RGB"""			).resize((64, 64)			)
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(image + 4			)			).convert("""RGB"""			).resize((64, 64)			)
      if str(__SCREAMING_SNAKE_CASE			).startswith("""mps"""			):
           __SCREAMING_SNAKE_CASE     =      torch.manual_seed(__SCREAMING_SNAKE_CASE			)
      else:
           __SCREAMING_SNAKE_CASE     =      torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": init_image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """output_type""": """numpy""",
      }
      return inputs
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE     =      self.get_dummy_components()
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline(**__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe.to(__SCREAMING_SNAKE_CASE			)
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe(**__SCREAMING_SNAKE_CASE			).images
      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]			)

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


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> str:


      """simple docstring"""




      super().test_inference_batch_single_identical(expected_max_diff=3E-3			)







@slow
@require_torch_gpu
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> str:


      """simple docstring"""




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE			)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 9E-3
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							safety_checker=__SCREAMING_SNAKE_CASE					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 5E-1


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Any:


      """simple docstring"""




      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					,							subfolder="""scheduler"""			)
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing(1			)
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=2					,							output_type="""np"""					,							)

      __SCREAMING_SNAKE_CASE     =      torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






