




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


lowercase_      :							Dict    							=					["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
lowercase_      :							Any    							=					{
    "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",
}

lowercase_      :							Union[str, Any]    							=					"https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
lowercase_      :							List[str]    							=					"https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


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

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

						with open(UpperCAmelCase_      ,       encoding="utf8"		) as data_fh, open(
						    os.path.join(UpperCAmelCase_      ,       "train.tsv"		)      ,       "w"      ,       encoding="utf8"		) as train_fh, open(os.path.join(UpperCAmelCase_      ,       "dev.tsv"		)      ,       "w"      ,       encoding="utf8"		) as dev_fh:
												_UpperCAmelCase						=	data_fh.readline()
												train_fh.write(UpperCAmelCase_		)
												dev_fh.write(UpperCAmelCase_		)
												for row in data_fh:
																		_UpperCAmelCase						=	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(UpperCAmelCase_      ,       encoding="utf8"		) as data_fh, open(
						    os.path.join(UpperCAmelCase_      ,       "test.tsv"		)      ,       "w"      ,       encoding="utf8"		) as test_fh:
												_UpperCAmelCase						=	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(UpperCAmelCase_		):
																		_UpperCAmelCase						=	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 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	task_names.split(","		)
						if "all" in task_names:
												_UpperCAmelCase						=	TASKS
						else:
												_UpperCAmelCase						=	[]
												for task_name in task_names:
																		if task_name not in TASKS:
																								raise ValueError(f"""Task {task_name} not found!"""		)
																		tasks.append(UpperCAmelCase_		)
						return tasks


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	argparse.ArgumentParser()
						parser.add_argument("--data_dir"      ,       help="directory to save data to"      ,       type=UpperCAmelCase_      ,       default="glue_data"		)
						parser.add_argument(
						    "--tasks"      ,       help="tasks to download data for as a comma separated string"      ,       type=UpperCAmelCase_      ,       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=UpperCAmelCase_      ,       default=""      ,       )
						_UpperCAmelCase						=	parser.parse_args(UpperCAmelCase_		)

						if not os.path.isdir(args.data_dir		):
												os.mkdir(args.data_dir		)
						_UpperCAmelCase						=	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(UpperCAmelCase_      ,       args.data_dir		)


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





"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


_a	:	Union[str, Any]=					"Here is a text containing a lot of information: '''{text}'''.\n\nCan you answer this question about the text: '{question}'"

class 						UpperCamelCase     (       lowercase		):
				UpperCAmelCase							: str       =						"""google/flan-t5-base"""
				UpperCAmelCase							: int       =						(
				    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
				    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
				)
				UpperCAmelCase							: Dict       =						"""text_qa"""
				UpperCAmelCase							: Dict       =						AutoTokenizer
				UpperCAmelCase							: Optional[Any]       =						AutoModelForSeqaSeqLM

				UpperCAmelCase							: List[str]       =						["""text""", """text"""]
				UpperCAmelCase							: Any       =						["""text"""]



				def    _lowercase	(self							: Tuple      ,    _A							: str      ,    _A							: str)       ->      Tuple:
					__snake_case     :				List[Any] 						=					QA_PROMPT.format(text=_A      ,    question=_A)
					return self.pre_processor(_A      ,    return_tensors='pt')



				def    _lowercase	(self							: Tuple      ,    _A							: Optional[Any])       ->      List[Any]:
					__snake_case     :				List[Any] 						=					self.model.generate(**_A)

					__snake_case						,							__snake_case     :				Any 						=					inputs['input_ids'].shape
					__snake_case     :				Any 						=					output_ids.shape[0]

					return output_ids.reshape(_A      ,    out_b // in_b      ,    *output_ids.shape[1:])[0][0]






				def    _lowercase	(self							: Optional[int]      ,    _A							: int)       ->      str:
					return self.pre_processor.decode(_A      ,    skip_special_tokens=_A      ,    clean_up_tokenization_spaces=_A)





