from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowercase_     :   int 				=				logging.get_logger(__name__)


if is_vision_available():
      import PIL






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      ["pixel_values"]
    def __init__(  self , lowerCAmelCase = True , lowerCAmelCase = None , lowerCAmelCase = PILImageResampling.BICUBIC , lowerCAmelCase = True , lowerCAmelCase = None , lowerCAmelCase = True , lowerCAmelCase = 1 / 255 , lowerCAmelCase = True , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = True , **lowerCAmelCase , )       ->					None:
     super().__init__(**lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Dict=      size if size is not None else {'''shortest_edge''': 224}
     SCREAMING_SNAKE_CASE__:   List[Any]=      get_size_dict(lowerCAmelCase , default_to_square=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[str]=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     SCREAMING_SNAKE_CASE__:   str=      get_size_dict(lowerCAmelCase , default_to_square=lowerCAmelCase , param_name='''crop_size'''   )

     SCREAMING_SNAKE_CASE__:   Tuple=      do_resize
     SCREAMING_SNAKE_CASE__:   Any=      size
     SCREAMING_SNAKE_CASE__:   Any=      resample
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      do_center_crop
     SCREAMING_SNAKE_CASE__:   Optional[int]=      crop_size
     SCREAMING_SNAKE_CASE__:   List[Any]=      do_rescale
     SCREAMING_SNAKE_CASE__:   str=      rescale_factor
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      do_normalize
     SCREAMING_SNAKE_CASE__:   Optional[int]=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     SCREAMING_SNAKE_CASE__:   List[str]=      image_std if image_std is not None else OPENAI_CLIP_STD
     SCREAMING_SNAKE_CASE__:   List[Any]=      do_convert_rgb
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = PILImageResampling.BICUBIC , lowerCAmelCase = None , **lowerCAmelCase , )       ->					np.ndarray:
     SCREAMING_SNAKE_CASE__:   List[str]=      get_size_dict(lowerCAmelCase , default_to_square=lowerCAmelCase   )
     if "shortest_edge" not in size:
      raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
     SCREAMING_SNAKE_CASE__:   str=      get_resize_output_image_size(lowerCAmelCase , size=size['''shortest_edge'''] , default_to_square=lowerCAmelCase   )
     return resize(lowerCAmelCase , size=lowerCAmelCase , resample=lowerCAmelCase , data_format=lowerCAmelCase , **lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , **lowerCAmelCase , )       ->					np.ndarray:
     SCREAMING_SNAKE_CASE__:   Any=      get_size_dict(lowerCAmelCase   )
     if "height" not in size or "width" not in size:
      raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'   )
     return center_crop(lowerCAmelCase , size=(size['''height'''], size['''width''']) , data_format=lowerCAmelCase , **lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , **lowerCAmelCase , )       ->					str:
     return rescale(lowerCAmelCase , scale=lowerCAmelCase , data_format=lowerCAmelCase , **lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = None , **lowerCAmelCase , )       ->					np.ndarray:
     return normalize(lowerCAmelCase , mean=lowerCAmelCase , std=lowerCAmelCase , data_format=lowerCAmelCase , **lowerCAmelCase   )



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase = ChannelDimension.FIRST , **lowerCAmelCase , )       ->					PIL.Image.Image:
     SCREAMING_SNAKE_CASE__:   List[Any]=      do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__:   int=      size if size is not None else self.size
     SCREAMING_SNAKE_CASE__:   Any=      get_size_dict(lowerCAmelCase , param_name='''size''' , default_to_square=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__:   int=      do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__:   int=      crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__:   int=      get_size_dict(lowerCAmelCase , param_name='''crop_size''' , default_to_square=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[int]=      do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE__:   Optional[int]=      image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE__:   Tuple=      image_std if image_std is not None else self.image_std
     SCREAMING_SNAKE_CASE__:   List[str]=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     SCREAMING_SNAKE_CASE__:   List[Any]=      make_list_of_images(lowerCAmelCase   )

     if not valid_images(lowerCAmelCase   ):
      raise ValueError(
          '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
          '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

     if do_resize and size is None:
      raise ValueError('''Size must be specified if do_resize is True.'''   )

     if do_center_crop and crop_size is None:
      raise ValueError('''Crop size must be specified if do_center_crop is True.'''   )

     if do_rescale and rescale_factor is None:
      raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

     if do_normalize and (image_mean is None or image_std is None):
      raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      [convert_to_rgb(lowerCAmelCase   ) for image in images]

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__:   str=      [to_numpy_array(lowerCAmelCase   ) for image in images]

     if do_resize:
      SCREAMING_SNAKE_CASE__:   str=      [self.resize(image=lowerCAmelCase , size=lowerCAmelCase , resample=lowerCAmelCase   ) for image in images]

     if do_center_crop:
      SCREAMING_SNAKE_CASE__:   int=      [self.center_crop(image=lowerCAmelCase , size=lowerCAmelCase   ) for image in images]

     if do_rescale:
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      [self.rescale(image=lowerCAmelCase , scale=lowerCAmelCase   ) for image in images]

     if do_normalize:
      SCREAMING_SNAKE_CASE__:   List[str]=      [self.normalize(image=lowerCAmelCase , mean=lowerCAmelCase , std=lowerCAmelCase   ) for image in images]

     SCREAMING_SNAKE_CASE__:   int=      [to_channel_dimension_format(lowerCAmelCase , lowerCAmelCase   ) for image in images]

     SCREAMING_SNAKE_CASE__:   str=      {'''pixel_values''': images}
     return BatchFeature(data=lowerCAmelCase , tensor_type=lowerCAmelCase   )


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() = }''')

