






"""simple docstring"""







import argparse
import os
import re


a			:    Union[str, Any]				 =		"""src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a			:    List[Any]				 =		re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
a			:    Union[str, Any]				 =		re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")





def 						lowercase__(A ,		A = False						)    ->Tuple:




						"""simple docstring"""



						with open(_snake_case ,		"r" ,		encoding="utf-8"						) as f:
												lowercase__    :				str=	f.read()

						lowercase__    :				List[Any]=	content.split("\n"						)
						lowercase__    :				str=	[]
						lowercase__    :				List[Any]=	0
						while line_idx < len(_snake_case						):
												if _re_intro_mapping.search(lines[line_idx]						) is not None:
																		lowercase__    :				List[str]=	len(re.search(R"^(\s*)\S" ,		lines[line_idx]						).groups()[0]						) + 8
																		# Start of a new mapping!
																		while not lines[line_idx].startswith(" " * indent + "("						):
																								new_lines.append(lines[line_idx]						)
																								line_idx += 1

																		lowercase__    :				str=	[]
																		while lines[line_idx].strip() != "]":
																								# Blocks either fit in one line or not
																								if lines[line_idx].strip() == "(":
																														lowercase__    :				Optional[Any]=	line_idx
																														while not lines[line_idx].startswith(" " * indent + ")"						):
																																				line_idx += 1
																														blocks.append("\n".join(lines[start_idx : line_idx + 1]						)						)
																								else:
																														blocks.append(lines[line_idx]						)
																								line_idx += 1

																		# Sort blocks by their identifiers
																		lowercase__    :				Optional[int]=	sorted(_snake_case ,		key=lambda A						: _re_identifier.search(_snake_case						).groups()[0]						)
																		new_lines += blocks
												else:
																		new_lines.append(lines[line_idx]						)
																		line_idx += 1

						if overwrite:
												with open(_snake_case ,		"w" ,		encoding="utf-8"						) as f:
																		f.write("\n".join(_snake_case						)						)
						elif "\n".join(_snake_case						) != content:
												return True





def 						lowercase__(A = False						)    ->int:




						"""simple docstring"""



						lowercase__    :				Union[str, Any]=	[os.path.join(_snake_case ,		_snake_case						) for f in os.listdir(_snake_case						) if f.endswith(".py"						)]
						lowercase__    :				Optional[Any]=	[sort_auto_mapping(_snake_case ,		overwrite=_snake_case						) for fname in fnames]

						if not overwrite and any(_snake_case						):
												lowercase__    :				List[str]=	[f for f, d in zip(_snake_case ,		_snake_case						) if d]
												raise ValueError(
												    f'''The following files have auto mappings that need sorting: {', '.join(_snake_case						)}. Run `make style` to fix'''
												    " this."						)


if __name__ == "__main__":
			a			:    Union[str, Any]				 =		argparse.ArgumentParser()
			parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
			a			:    Optional[int]				 =		parser.parse_args()

			sort_all_auto_mappings(not args.check_only)














"""simple docstring"""







from pathlib import Path

import fire
from tqdm import tqdm





def 						lowercase__(A="ro" ,		A="en" ,		A="wmt16" ,		A=None						)    ->None:




						"""simple docstring"""



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

						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
												lowercase__    :				Any=	"val" if split == "validation" else split
												lowercase__    :				List[Any]=	save_dir.joinpath(f'''{fn}.source'''						)
												lowercase__    :				Optional[Any]=	save_dir.joinpath(f'''{fn}.target'''						)
												lowercase__    :				Optional[int]=	src_path.open("w+"						)
												lowercase__    :				Any=	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]						):
																		lowercase__    :				int=	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)






