"""simple docstring"""





def   lowerCamelCase_(__SCREAMING_SNAKE_CASE	)->     Optional[int]:

							if not grid or not grid[0]:
														raise TypeError("""The grid does not contain the appropriate information"""	)

							for cell_n in range(1  ,						len(grid[0]	)	):
														grid[0][cell_n] += grid[0][cell_n - 1]
							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     grid[0]

							for row_n in range(1  ,						len(lowerCAmelCase__	)	):
														_SCREAMING_SNAKE_CASE		:    Dict      	=     grid[row_n]
														_SCREAMING_SNAKE_CASE		:    Dict      	=     fill_row(lowerCAmelCase__  ,						lowerCAmelCase__	)
														_SCREAMING_SNAKE_CASE		:    str      	=     grid[row_n]

							return grid[-1][-1]



def   lowerCamelCase_(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE	)->     Optional[int]:
							current_row[0] += row_above[0]
							for cell_n in range(1  ,						len(lowerCAmelCase__	)	):
														current_row[cell_n] += min(current_row[cell_n - 1]  ,						row_above[cell_n]	)

							return current_row


if __name__ == "__main__":
			import doctest

			doctest.testmod()




"""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  __lowerCamelCase  ( unittest.TestCase			):




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''google/mt5-small'''   )

							lowerCamelCase_					=  tokenizer('''Hello there'''							,						return_tensors='''np'''   ).input_ids
							lowerCamelCase_					=  tokenizer('''Hi I am'''							,						return_tensors='''np'''   ).input_ids

							lowerCamelCase_					=  shift_tokens_right(UpperCAmelCase							,						model.config.pad_token_id							,						model.config.decoder_start_token_id   )

							lowerCamelCase_					=  model(UpperCAmelCase							,						decoder_input_ids=UpperCAmelCase   ).logits
							lowerCamelCase_					=  optax.softmax_cross_entropy(UpperCAmelCase							,						onehot(UpperCAmelCase							,						logits.shape[-1]   )   ).mean()

							lowerCamelCase_					=  -(labels.shape[-1] * loss.item())

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



