import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     unittest.TestCase    ):
    __a  =      IFInpaintingPipeline
    __a  =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
    __a  =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    __a  =      PipelineTesterMixin.required_optional_params - {"latents"}
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     return self._get_dummy_components()
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=0   )       ->					Dict:
     if str(lowerCAmelCase   ).startswith('''mps'''   ):
      SCREAMING_SNAKE_CASE__:   Tuple=      torch.manual_seed(lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   int=      torch.Generator(device=lowerCAmelCase   ).manual_seed(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Tuple=      floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   str=      floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase   )   ).to(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Optional[int]=      {
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''mask_image''': mask_image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }

     return inputs
    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
    def 						UpperCamelCase_				(  self   )       ->					List[str]:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != '''cuda''' , reason='''float16 requires CUDA'''   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
     super().test_save_load_floataa(expected_max_diff=1e-1   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     self._test_save_load_local()



    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     self._test_inference_batch_single_identical(
         expected_max_diff=1e-2 , )


import os







def 			A__      (	snake_case_ :		str = "matrix.txt"      ):
 with open(os.path.join(os.path.dirname(snake_case_      )       ,				snake_case_      )      ) as in_file:
  SCREAMING_SNAKE_CASE__:   Dict=      in_file.read()

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

 SCREAMING_SNAKE_CASE__:   Dict=      [[0 for i in range(snake_case_      )] for j in range(snake_case_      )]
 SCREAMING_SNAKE_CASE__:   Optional[int]=      grid[0][0]
 for i in range(1       ,				snake_case_      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      grid[0][i] + dp[0][i - 1]
 for i in range(1       ,				snake_case_      ):
  SCREAMING_SNAKE_CASE__:   List[Any]=      grid[i][0] + dp[i - 1][0]

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

 return dp[-1][-1]


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

