




import os







def 		_A(       UpperCamelCase__							:			str = "matrix.txt"				)    -> int:
							'''simple docstring'''


							with open(os.path.join(os.path.dirname(UpperCamelCase__				)				,       UpperCamelCase__				)				) as in_file:
														__lowercase      =    in_file.read()

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

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

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

							return dp[-1][-1]


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












import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		['image_processor', 'tokenizer']
						UpperCamelCase_	:       Dict       	=		'OwlViTImageProcessor'
						UpperCamelCase_	:       List[str]       	=		('CLIPTokenizer', 'CLIPTokenizerFast')






						def __init__( self :     str				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     List[Any]=None				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    None
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																				    ''' instead.'''				,							lowerCamelCase__				,							)
																				__lowercase      =    kwargs.pop('''feature_extractor'''				)

													__lowercase      =    image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																				raise ValueError('''You need to specify an `image_processor`.'''				)
													if tokenizer is None:
																				raise ValueError('''You need to specify a `tokenizer`.'''				)

													super().__init__(lowerCamelCase__				,							lowerCamelCase__				)






						def __call__( self :     List[Any]				,							lowerCamelCase__ :     List[str]=None				,							lowerCamelCase__ :     int=None				,							lowerCamelCase__ :     Any=None				,							lowerCamelCase__ :     str="max_length"				,							lowerCamelCase__ :     Union[str, Any]="np"				,							**lowerCamelCase__ :     Dict				)    ->						Union[str, Any]:
													"""simple docstring"""


													if text is None and query_images is None and images is None:
																				raise ValueError(
																				    '''You have to specify at least one text or query image or image. All three cannot be none.'''				)

													if text is not None:
																				if isinstance(lowerCamelCase__				,							lowerCamelCase__				) or (isinstance(lowerCamelCase__				,							lowerCamelCase__				) and not isinstance(text[0]				,							lowerCamelCase__				)):
																											__lowercase      =    [self.tokenizer(lowerCamelCase__				,							padding=lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				)]

																				elif isinstance(lowerCamelCase__				,							lowerCamelCase__				) and isinstance(text[0]				,							lowerCamelCase__				):
																											__lowercase      =    []

																											# Maximum number of queries across batch
																											__lowercase      =    max([len(lowerCamelCase__				) for t in text]				)

																											# Pad all batch samples to max number of text queries
																											for t in text:
																																		if len(lowerCamelCase__				) != max_num_queries:
																																									__lowercase      =    t + [''' '''] * (max_num_queries - len(lowerCamelCase__				))

																																		__lowercase      =    self.tokenizer(lowerCamelCase__				,							padding=lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				)
																																		encodings.append(lowerCamelCase__				)
																				else:
																											raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''				)

																				if return_tensors == "np":
																											__lowercase      =    np.concatenate([encoding['''input_ids'''] for encoding in encodings]				,							axis=0				)
																											__lowercase      =    np.concatenate([encoding['''attention_mask'''] for encoding in encodings]				,							axis=0				)

																				elif return_tensors == "jax" and is_flax_available():
																											import jax.numpy as jnp

																											__lowercase      =    jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]				,							axis=0				)
																											__lowercase      =    jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]				,							axis=0				)

																				elif return_tensors == "pt" and is_torch_available():
																											import torch

																											__lowercase      =    torch.cat([encoding['''input_ids'''] for encoding in encodings]				,							dim=0				)
																											__lowercase      =    torch.cat([encoding['''attention_mask'''] for encoding in encodings]				,							dim=0				)

																				elif return_tensors == "tf" and is_tf_available():
																											import tensorflow as tf

																											__lowercase      =    tf.stack([encoding['''input_ids'''] for encoding in encodings]				,							axis=0				)
																											__lowercase      =    tf.stack([encoding['''attention_mask'''] for encoding in encodings]				,							axis=0				)

																				else:
																											raise ValueError('''Target return tensor type could not be returned'''				)

																				__lowercase      =    BatchEncoding()
																				__lowercase      =    input_ids
																				__lowercase      =    attention_mask

													if query_images is not None:
																				__lowercase      =    BatchEncoding()
																				__lowercase      =    self.image_processor(
																				    lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				).pixel_values
																				__lowercase      =    query_pixel_values

													if images is not None:
																				__lowercase      =    self.image_processor(lowerCamelCase__				,							return_tensors=lowerCamelCase__				,							**lowerCamelCase__				)

													if text is not None and images is not None:
																				__lowercase      =    image_features.pixel_values
																				return encoding
													elif query_images is not None and images is not None:
																				__lowercase      =    image_features.pixel_values
																				return encoding
													elif text is not None or query_images is not None:
																				return encoding
													else:
																				return BatchEncoding(data=dict(**lowerCamelCase__				)				,							tensor_type=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     int				,							*lowerCamelCase__ :     str				,							**lowerCamelCase__ :     Optional[int]				)    ->						Tuple:
													"""simple docstring"""


													return self.image_processor.post_process(*lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							*lowerCamelCase__ :     Tuple				,							**lowerCamelCase__ :     Tuple				)    ->						Union[str, Any]:
													"""simple docstring"""


													return self.image_processor.post_process_object_detection(*lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     List[str]				,							*lowerCamelCase__ :     List[str]				,							**lowerCamelCase__ :     Optional[Any]				)    ->						List[Any]:
													"""simple docstring"""


													return self.image_processor.post_process_image_guided_detection(*lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Optional[int]				,							*lowerCamelCase__ :     str				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													return self.tokenizer.batch_decode(*lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     str				,							*lowerCamelCase__ :     Optional[int]				,							**lowerCamelCase__ :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													return self.tokenizer.decode(*lowerCamelCase__				,							**lowerCamelCase__				)






						@property
						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													warnings.warn(
													    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,							lowerCamelCase__				,							)
													return self.image_processor_class






						@property
						def     UpperCAmelCase_      ( self :     str				)    ->						Any:
													"""simple docstring"""


													warnings.warn(
													    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,							lowerCamelCase__				,							)
													return self.image_processor






