


import argparse
import os
import sys
import urllib.request
import zipfile


SCREAMING_SNAKE_CASE : Optional[Any]          =    ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
SCREAMING_SNAKE_CASE : List[str]          =    {
    "CoLA": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4",
    "SST": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8",
    "MRPC": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc",
    "QQP": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5",
    "STS": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5",
    "MNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce",
    "SNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df",
    "QNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601",
    "RTE": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb",
    "WNLI": "https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf",
    "diagnostic": "https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D",
}

SCREAMING_SNAKE_CASE : Tuple          =    "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
SCREAMING_SNAKE_CASE : Dict          =    "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Union[str, Any]	,	SCREAMING_SNAKE_CASE_				: Optional[Any]      ):






							"""simple docstring"""



							print(F"""Downloading and extracting {task}..."""      )
							a_       :   List[str]			=	F"""{task}.zip"""
							urllib.request.urlretrieve(TASKaPATH[task]	,	SCREAMING_SNAKE_CASE__      )
							with zipfile.ZipFile(SCREAMING_SNAKE_CASE__      ) as zip_ref:
														zip_ref.extractall(SCREAMING_SNAKE_CASE__      )
							os.remove(SCREAMING_SNAKE_CASE__      )
							print("""\tCompleted!"""      )





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: Tuple      ):






							"""simple docstring"""



							print("""Processing MRPC..."""      )
							a_       :   Optional[Any]			=	os.path.join(SCREAMING_SNAKE_CASE__	,	"""MRPC"""      )
							if not os.path.isdir(SCREAMING_SNAKE_CASE__      ):
														os.mkdir(SCREAMING_SNAKE_CASE__      )
							if path_to_data:
														a_       :   int			=	os.path.join(SCREAMING_SNAKE_CASE__	,	"""msr_paraphrase_train.txt"""      )
														a_       :   Tuple			=	os.path.join(SCREAMING_SNAKE_CASE__	,	"""msr_paraphrase_test.txt"""      )
							else:
														print("""Local MRPC data not specified, downloading data from %s""" % MRPC_TRAIN      )
														a_       :   Optional[int]			=	os.path.join(SCREAMING_SNAKE_CASE__	,	"""msr_paraphrase_train.txt"""      )
														a_       :   Dict			=	os.path.join(SCREAMING_SNAKE_CASE__	,	"""msr_paraphrase_test.txt"""      )
														urllib.request.urlretrieve(SCREAMING_SNAKE_CASE__	,	SCREAMING_SNAKE_CASE__      )
														urllib.request.urlretrieve(SCREAMING_SNAKE_CASE__	,	SCREAMING_SNAKE_CASE__      )
							if not os.path.isfile(SCREAMING_SNAKE_CASE__      ):
														raise ValueError(F"""Train data not found at {mrpc_train_file}"""      )
							if not os.path.isfile(SCREAMING_SNAKE_CASE__      ):
														raise ValueError(F"""Test data not found at {mrpc_test_file}"""      )
							urllib.request.urlretrieve(TASKaPATH["""MRPC"""]	,	os.path.join(SCREAMING_SNAKE_CASE__	,	"""dev_ids.tsv"""      )      )

							a_       :   str			=	[]
							with open(os.path.join(SCREAMING_SNAKE_CASE__	,	"""dev_ids.tsv"""      )	,	encoding="""utf8"""      ) as ids_fh:
														for row in ids_fh:
																					dev_ids.append(row.strip().split("""\t"""      )      )

							with open(SCREAMING_SNAKE_CASE__	,	encoding="""utf8"""      ) as data_fh, open(
							    os.path.join(SCREAMING_SNAKE_CASE__	,	"""train.tsv"""      )	,	"""w"""	,	encoding="""utf8"""      ) as train_fh, open(os.path.join(SCREAMING_SNAKE_CASE__	,	"""dev.tsv"""      )	,	"""w"""	,	encoding="""utf8"""      ) as dev_fh:
														a_       :   str			=	data_fh.readline()
														train_fh.write(SCREAMING_SNAKE_CASE__      )
														dev_fh.write(SCREAMING_SNAKE_CASE__      )
														for row in data_fh:
																					a_       :   int			=	row.strip().split("""\t"""      )
																					if [ida, ida] in dev_ids:
																												dev_fh.write("""%s\t%s\t%s\t%s\t%s\n""" % (label, ida, ida, sa, sa)      )
																					else:
																												train_fh.write("""%s\t%s\t%s\t%s\t%s\n""" % (label, ida, ida, sa, sa)      )

							with open(SCREAMING_SNAKE_CASE__	,	encoding="""utf8"""      ) as data_fh, open(
							    os.path.join(SCREAMING_SNAKE_CASE__	,	"""test.tsv"""      )	,	"""w"""	,	encoding="""utf8"""      ) as test_fh:
														a_       :   Tuple			=	data_fh.readline()
														test_fh.write("""index\t#1 ID\t#2 ID\t#1 String\t#2 String\n"""      )
														for idx, row in enumerate(SCREAMING_SNAKE_CASE__      ):
																					a_       :   Optional[Any]			=	row.strip().split("""\t"""      )
																					test_fh.write("""%d\t%s\t%s\t%s\t%s\n""" % (idx, ida, ida, sa, sa)      )
							print("""\tCompleted!"""      )





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[Any]      ):






							"""simple docstring"""



							print("""Downloading and extracting diagnostic..."""      )
							if not os.path.isdir(os.path.join(SCREAMING_SNAKE_CASE__	,	"""diagnostic"""      )      ):
														os.mkdir(os.path.join(SCREAMING_SNAKE_CASE__	,	"""diagnostic"""      )      )
							a_       :   Tuple			=	os.path.join(SCREAMING_SNAKE_CASE__	,	"""diagnostic"""	,	"""diagnostic.tsv"""      )
							urllib.request.urlretrieve(TASKaPATH["""diagnostic"""]	,	SCREAMING_SNAKE_CASE__      )
							print("""\tCompleted!"""      )
							return





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[Any]      ):






							"""simple docstring"""



							a_       :   Tuple			=	task_names.split(""","""      )
							if "all" in task_names:
														a_       :   int			=	TASKS
							else:
														a_       :   Any			=	[]
														for task_name in task_names:
																					if task_name not in TASKS:
																												raise ValueError(F"""Task {task_name} not found!"""      )
																					tasks.append(SCREAMING_SNAKE_CASE__      )
							return tasks





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Dict      ):






							"""simple docstring"""



							a_       :   int			=	argparse.ArgumentParser()
							parser.add_argument("""--data_dir"""	,	help="""directory to save data to"""	,	type=SCREAMING_SNAKE_CASE__	,	default="""glue_data"""      )
							parser.add_argument(
							    """--tasks"""	,	help="""tasks to download data for as a comma separated string"""	,	type=SCREAMING_SNAKE_CASE__	,	default="""all"""      )
							parser.add_argument(
							    """--path_to_mrpc"""	,	help="""path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt"""	,	type=SCREAMING_SNAKE_CASE__	,	default=""""""	,	)
							a_       :   Optional[int]			=	parser.parse_args(SCREAMING_SNAKE_CASE__      )

							if not os.path.isdir(args.data_dir      ):
														os.mkdir(args.data_dir      )
							a_       :   List[Any]			=	get_tasks(args.tasks      )

							for task in tasks:
														if task == "MRPC":
																					format_mrpc(args.data_dir	,	args.path_to_mrpc      )
														elif task == "diagnostic":
																					download_diagnostic(args.data_dir      )
														else:
																					download_and_extract(SCREAMING_SNAKE_CASE__	,	args.data_dir      )


if __name__ == "__main__":
	sys.exit(main(sys.argv[1:]))



import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


SCREAMING_SNAKE_CASE : int          =    datasets.utils.logging.get_logger(__name__)





@dataclass
class 					snake_case__  ( datasets.BuilderConfig       ):
	UpperCAmelCase   :							Optional[datasets.Features]         = None





class 					snake_case__  ( datasets.ArrowBasedBuilder       ):
	UpperCAmelCase   :							Optional[int]         = ArrowConfig





	def     A				(  self							)		->			str:
								"""simple docstring"""






								return datasets.DatasetInfo(features=self.config.features							)





	def     A				(  self				,    UpperCamelCase_							)		->			str:
								"""simple docstring"""






								if not self.config.data_files:
															raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
								a_       :   List[str]			=	dl_manager.download_and_extract(self.config.data_files							)
								if isinstance(UpperCamelCase_				,    (str, list, tuple)							):
															a_       :   Dict			=	data_files
															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   Optional[Any]			=	[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={"""files""": files}							)]
								a_       :   Any			=	[]
								for split_name, files in data_files.items():
															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   Dict			=	[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																						for file in itertools.chain.from_iterable(UpperCamelCase_							):
																													with open(UpperCamelCase_				,    """rb"""							) as f:
																																				a_       :   List[str]			=	datasets.Features.from_arrow_schema(pa.ipc.open_stream(UpperCamelCase_							).schema							)
																													break
															splits.append(datasets.SplitGenerator(name=UpperCamelCase_				,    gen_kwargs={"""files""": files}							)							)
								return splits





	def     A				(  self				,    UpperCamelCase_							)		->			pa.Table:
								"""simple docstring"""






								if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															a_       :   int			=	table_cast(UpperCamelCase_				,    self.info.features.arrow_schema							)
								return pa_table





	def     A				(  self				,    UpperCamelCase_							)		->			Optional[Any]:
								"""simple docstring"""






								for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_							)							):
															with open(UpperCamelCase_				,    """rb"""							) as f:
																						try:
																													for batch_idx, record_batch in enumerate(pa.ipc.open_stream(UpperCamelCase_							)							):
																																				a_       :   Tuple			=	pa.Table.from_batches([record_batch]							)
																																				# Uncomment for debugging (will print the Arrow table size and elements)
																																				# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																				# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																				yield f"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase_							)
																						except ValueError as e:
																													logger.error(f"""Failed to read file '{file}' with error {type(UpperCamelCase_							)}: {e}"""							)
																													raise


