





def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







	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]
	lowerCamelCase__              =	grid[0]

	for row_n in range(1 ,len(__snake_case							)							):
		lowerCamelCase__              =	grid[row_n]
		lowerCamelCase__              =	fill_row(__snake_case ,__snake_case							)
		lowerCamelCase__              =	grid[row_n]

	return grid[-1][-1]





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   list:







	'''simple docstring'''







	current_row[0] += row_above[0]
	for cell_n in range(1 ,len(__snake_case							)							):
		current_row[cell_n] += min(current_row[cell_n - 1] ,row_above[cell_n]							)

	return current_row


if __name__ == "__main__":
			import doctest

			doctest.testmod()










import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def lowerCAmelCase__(__snake_case							)       ->   Union[str, Any]:







	'''simple docstring'''







	def wrapper(*__snake_case ,**__snake_case							):
		lowerCamelCase__              =	timeit.default_timer()
		lowerCamelCase__              =	func(*__snake_case ,**__snake_case							)
		lowerCamelCase__              =	timeit.default_timer() - starttime
		return delta

	lowerCamelCase__              =	func.__name__

	return wrapper





def lowerCAmelCase__(__snake_case ,__snake_case=100 ,__snake_case=None							)       ->   Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	[]
	lowerCamelCase__              =	seq_shapes or {}
	for i in range(__snake_case							):
		lowerCamelCase__              =	{}
		for col_id, (k, v) in enumerate(features.items()							):
			if isinstance(__snake_case ,_ArrayXD							):
				lowerCamelCase__              =	np.random.rand(*v.shape							).astype(v.dtype							)
			elif isinstance(__snake_case ,datasets.Value							):
				if v.dtype == "string":
					lowerCamelCase__              =	'''The small grey turtle was surprisingly fast when challenged.'''
				else:
					lowerCamelCase__              =	np.random.randint(10 ,size=1							).astype(v.dtype							).item()
			elif isinstance(__snake_case ,datasets.Sequence							):
				while isinstance(__snake_case ,datasets.Sequence							):
					lowerCamelCase__              =	v.feature
				lowerCamelCase__              =	seq_shapes[k]
				lowerCamelCase__              =	np.random.rand(*__snake_case							).astype(v.dtype							)
			lowerCamelCase__              =	data

		dummy_data.append((i, example)							)

	return dummy_data





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case=100 ,__snake_case=None							)       ->   str:







	'''simple docstring'''







	lowerCamelCase__              =	generate_examples(__snake_case ,num_examples=__snake_case ,seq_shapes=__snake_case							)

	with ArrowWriter(features=__snake_case ,path=__snake_case							) as writer:
		for key, record in dummy_data:
			lowerCamelCase__              =	features.encode_example(__snake_case							)
			writer.write(__snake_case							)

		lowerCamelCase__       ,			lowerCamelCase__              =	writer.finalize()

	if not num_final_examples == num_examples:
		raise ValueError(
		    F'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'							)

	lowerCamelCase__              =	datasets.Dataset.from_file(filename=__snake_case ,info=datasets.DatasetInfo(features=__snake_case							)							)

	return dataset



