






'''simple docstring'''



import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
      import optax
      from flax.training.common_utils import onehot

      from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
      from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




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


       '''simple docstring'''

       lowercase       : Optional[int]						=FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
       lowercase       : Optional[int]						=AutoTokenizer.from_pretrained('''google/mt5-small'''		)

       lowercase       : Tuple						=tokenizer('''Hello there'''  ,   return_tensors='''np'''		).input_ids
       lowercase       : Union[str, Any]						=tokenizer('''Hi I am'''  ,   return_tensors='''np'''		).input_ids

       lowercase       : Optional[int]						=shift_tokens_right(UpperCAmelCase  ,   model.config.pad_token_id  ,   model.config.decoder_start_token_id		)

       lowercase       : Optional[int]						=model(UpperCAmelCase  ,   decoder_input_ids=UpperCAmelCase		).logits
       lowercase       : Union[str, Any]						=optax.softmax_cross_entropy(UpperCAmelCase  ,   onehot(UpperCAmelCase  ,   logits.shape[-1]		)		).mean()

       lowercase       : Tuple						=-(labels.shape[-1] * loss.item())

       lowercase       : Tuple						=-8_4.9_1_2_7
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1e-4		)











'''simple docstring'''



from __future__ import annotations


def 				lowercase_					(     __A			:							list[list[int]]      )							->   int:



 """simple docstring"""




 for i in range(1       ,   len(matrix[0]      )      ):
  matrix[0][i] += matrix[0][i - 1]

 # preprocessing the first column
 for i in range(1       ,   len(__A      )      ):
  matrix[i][0] += matrix[i - 1][0]

 # updating the path cost for current position
 for i in range(1       ,   len(__A      )      ):
  for j in range(1       ,   len(matrix[0]      )      ):
   matrix[i][j] += min(matrix[i - 1][j]       ,   matrix[i][j - 1]      )

 return matrix[-1][-1]


if __name__ == "__main__":
      import doctest

      doctest.testmod()



