





import os



def A_     (		A__ = "matrix.txt"    )  ->		int:
       with open(os.path.join(os.path.dirname(A__    )		,				A__    )    ) as in_file:
              a__						:     int			=    in_file.read()

       a__						:     str			=    [[int(A__    ) for cell in row.split(','    )] for row in data.strip().splitlines()]
       a__						:     List[Any]			=    [[0 for cell in row] for row in grid]
       a__						:     Any			=    len(grid[0]    )

       a__						:     List[Any]			=    [[0 for i in range(A__    )] for j in range(A__    )]
       a__						:     Union[str, Any]			=    grid[0][0]
       for i in range(1		,				A__    ):
              a__						:     List[Any]			=    grid[0][i] + dp[0][i - 1]
       for i in range(1		,				A__    ):
              a__						:     Dict			=    grid[i][0] + dp[i - 1][0]

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

       return dp[-1][-1]


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









import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        NystromformerForMaskedLM,
        NystromformerForMultipleChoice,
        NystromformerForQuestionAnswering,
        NystromformerForSequenceClassification,
        NystromformerForTokenClassification,
        NystromformerModel,
    )
    from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=7 ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=99 ,  lowercase=32 ,  lowercase=5 ,  lowercase=4 ,  lowercase=37 ,  lowercase="gelu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=512 ,  lowercase=16 ,  lowercase=2 ,  lowercase=0.02 ,  lowercase=3 ,  lowercase=4 ,  lowercase=None ,  )      ->						List[Any]:







          '''simple docstring'''

          a__						:     Any			=    parent
          a__						:     int			=    batch_size
          a__						:     Dict			=    seq_length
          a__						:     Tuple			=    is_training
          a__						:     Any			=    use_input_mask
          a__						:     Optional[Any]			=    use_token_type_ids
          a__						:     Dict			=    use_labels
          a__						:     Optional[int]			=    vocab_size
          a__						:     List[Any]			=    hidden_size
          a__						:     int			=    num_hidden_layers
          a__						:     Optional[Any]			=    num_attention_heads
          a__						:     str			=    intermediate_size
          a__						:     Optional[int]			=    hidden_act
          a__						:     Dict			=    hidden_dropout_prob
          a__						:     Optional[int]			=    attention_probs_dropout_prob
          a__						:     Tuple			=    max_position_embeddings
          a__						:     Dict			=    type_vocab_size
          a__						:     Any			=    type_sequence_label_size
          a__						:     List[str]			=    initializer_range
          a__						:     List[str]			=    num_labels
          a__						:     Optional[Any]			=    num_choices
          a__						:     str			=    scope

   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

          a__						:     Tuple			=    None
          if self.use_input_mask:
                 a__						:     Optional[int]			=    random_attention_mask([self.batch_size, self.seq_length])

          a__						:     Any			=    None
          if self.use_token_type_ids:
                 a__						:     List[Any]			=    ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size)

          a__						:     str			=    None
          a__						:     List[Any]			=    None
          a__						:     List[str]			=    None
          if self.use_labels:
                 a__						:     Any			=    ids_tensor([self.batch_size] ,  self.type_sequence_label_size)
                 a__						:     str			=    ids_tensor([self.batch_size, self.seq_length] ,  self.num_labels)
                 a__						:     str			=    ids_tensor([self.batch_size] ,  self.num_choices)

          a__						:     Union[str, Any]			=    self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          return NystromformerConfig(
              vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  is_decoder=lowercase ,  initializer_range=self.initializer_range ,  )

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    NystromformerModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     List[Any]			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase)
          a__						:     int			=    model(lowercase ,  token_type_ids=lowercase)
          a__						:     Optional[Any]			=    model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     List[str]			=    NystromformerForMaskedLM(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     int			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     Any			=    NystromformerForQuestionAnswering(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     str			=    model(
              lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  start_positions=lowercase ,  end_positions=lowercase ,  )
          self.parent.assertEqual(result.start_logits.shape ,  (self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape ,  (self.batch_size, self.seq_length))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     int			=    self.num_labels
          a__						:     Optional[Any]			=    NystromformerForSequenceClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     Tuple			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          a__						:     Tuple			=    self.num_labels
          a__						:     int			=    NystromformerForTokenClassification(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     str			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.num_labels))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

          a__						:     Optional[int]			=    self.num_choices
          a__						:     Tuple			=    NystromformerForMultipleChoice(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     Optional[Any]			=    input_ids.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
          a__						:     Tuple			=    token_type_ids.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
          a__						:     str			=    input_mask.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
          a__						:     Optional[int]			=    model(
              lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase ,  )
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_choices))

   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     List[Any]			=    self.prepare_config_and_inputs()
          (
              (
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							
          )						:     str			=    config_and_inputs
          a__						:     Any			=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict





@require_torch
class 			A__   (							__UpperCAmelCase			,  __UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Any	  =  (
       (
           NystromformerModel,
           NystromformerForMaskedLM,
           NystromformerForMultipleChoice,
           NystromformerForQuestionAnswering,
           NystromformerForSequenceClassification,
           NystromformerForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __A       :    str	  =  (
       {
           '''feature-extraction''': NystromformerModel,
           '''fill-mask''': NystromformerForMaskedLM,
           '''question-answering''': NystromformerForQuestionAnswering,
           '''text-classification''': NystromformerForSequenceClassification,
           '''token-classification''': NystromformerForTokenClassification,
           '''zero-shot''': NystromformerForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __A       :    Optional[Any]	  =  False
   __A       :    Tuple	  =  False

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     int			=    NystromformerModelTester(self)
          a__						:     Any			=    ConfigTester(self ,  config_class=lowercase ,  hidden_size=37)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     str			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Tuple			=    self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                 a__						:     Optional[Any]			=    type
                 self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowercase)

   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*lowercase)

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowercase)

   @slow
   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:     int			=    NystromformerModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)





@require_torch
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   @slow
   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     List[str]			=    NystromformerModel.from_pretrained('uw-madison/nystromformer-512')
          a__						:     Tuple			=    torch.tensor([[0, 1, 2, 3, 4, 5]])

          with torch.no_grad():
                 a__						:     List[Any]			=    model(lowercase)[0]

          a__						:     str			=    torch.Size((1, 6, 768))
          self.assertEqual(output.shape ,  lowercase)

          a__						:     str			=    torch.tensor(
              [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]])

          self.assertTrue(torch.allclose(output[:, :3, :3] ,  lowercase ,  atol=1e-4))

   @slow
   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Any			=    'the [MASK] of Belgium is Brussels'

          a__						:     List[str]			=    AutoTokenizer.from_pretrained('uw-madison/nystromformer-512')
          a__						:     Optional[int]			=    NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512')

          a__						:     List[Any]			=    tokenizer(lowercase ,  return_tensors='pt')

          with torch.no_grad():
                 a__						:     Union[str, Any]			=    model(encoding.input_ids).logits

          a__						:     str			=    token_logits[:, 2, :].argmax(-1)[0]

          self.assertEqual(tokenizer.decode(lowercase) ,  'capital')


