


'''simple docstring'''



from pathlib import Path

import fire
from tqdm import tqdm







def     _UpperCamelCase      (		UpperCamelCase__="ro" ,      UpperCamelCase__="en" ,      UpperCamelCase__="wmt16" ,      UpperCamelCase__=None					):
			try:
						import datasets
			except (ModuleNotFoundError, ImportError):
						raise ImportError("""run pip install datasets"""					)
			UpperCAmelCase__  :					Union[str, Any]        =     f'''{src_lang}-{tgt_lang}'''
			print(f'''Converting {dataset}-{pair}'''					)
			UpperCAmelCase__  :					Optional[int]        =     datasets.load_dataset(UpperCamelCase__ ,      UpperCamelCase__					)
			if save_dir is None:
						UpperCAmelCase__  :					Union[str, Any]        =     f'''{dataset}-{pair}'''
			UpperCAmelCase__  :					List[Any]        =     Path(UpperCamelCase__					)
			save_dir.mkdir(exist_ok=UpperCamelCase__					)

			for split in ds.keys():
						print(f'''Splitting {split} with {ds[split].num_rows} records'''					)

						# to save to val.source, val.target like summary datasets
						UpperCAmelCase__  :					str        =     """val""" if split == """validation""" else split
						UpperCAmelCase__  :					str        =     save_dir.joinpath(f'''{fn}.source'''					)
						UpperCAmelCase__  :					Union[str, Any]        =     save_dir.joinpath(f'''{fn}.target'''					)
						UpperCAmelCase__  :					List[str]        =     src_path.open("""w+"""					)
						UpperCAmelCase__  :					Optional[int]        =     tgt_path.open("""w+"""					)

						# reader is the bottleneck so writing one record at a time doesn't slow things down
						for x in tqdm(ds[split]					):
									UpperCAmelCase__  :					str        =     x["""translation"""]
									src_fp.write(ex[src_lang] + """\n"""					)
									tgt_fp.write(ex[tgt_lang] + """\n"""					)

			print(f'''Saved {dataset} dataset to {save_dir}'''					)


if __name__ == "__main__":
		fire.Fire(download_wmt_dataset)



'''simple docstring'''



from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__A	=numpy.array([0, 0])
__A	=numpy.array([0.5, 0.8_6_6_0_2_5_4])
__A	=numpy.array([1, 0])
__A	=[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     initial_vectors
			for _ in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Dict        =     iteration_step(UpperCamelCase__					)
			return vectors







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[Any]        =     []
			for i, start_vector in enumerate(vectors[:-1]					):
						UpperCAmelCase__  :					Tuple        =     vectors[i + 1]
						new_vectors.append(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     end_vector - start_vector
						new_vectors.append(start_vector + difference_vector / 3					)
						new_vectors.append(
						    start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,      6_0					)					)
						new_vectors.append(start_vector + difference_vector * 2 / 3					)
			new_vectors.append(vectors[-1]					)
			return new_vectors







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     numpy.radians(UpperCamelCase__					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     numpy.cos(UpperCamelCase__					), numpy.sin(UpperCamelCase__					)
			UpperCAmelCase__  :					Dict        =     numpy.array(((c, -s), (s, c))					)
			return numpy.dot(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     plt.gca()
			axes.set_aspect("""equal"""					)

			# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
			# y-coordinates as inputs, which are constructed from the vector-list using
			# zip()
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     zip(*UpperCamelCase__					)
			plt.plot(UpperCamelCase__ ,      UpperCamelCase__					)
			plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__A	=iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)