
'''simple docstring'''





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


A							     = ["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
A							     = {
    "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",
}

A							     = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
A							     = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"
def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				int      ):
      print(F'''Downloading and extracting {task}...'''      )
      snake_case_    							=				F'''{task}.zip'''
      urllib.request.urlretrieve(TASKaPATH[task]			,       _a      )
      with zipfile.ZipFile(_a      ) as zip_ref:
            zip_ref.extractall(_a      )
      os.remove(_a      )
      print("\tCompleted!"      )
def 	snake_case_							(				_a  :				Optional[int]			,       _a  :				Dict      ):
      print("Processing MRPC..."      )
      snake_case_    							=				os.path.join(_a			,       "MRPC"      )
      if not os.path.isdir(_a      ):
            os.mkdir(_a      )
      if path_to_data:
            snake_case_    							=				os.path.join(_a			,       "msr_paraphrase_train.txt"      )
            snake_case_    							=				os.path.join(_a			,       "msr_paraphrase_test.txt"      )
      else:
            print("Local MRPC data not specified, downloading data from %s" % MRPC_TRAIN      )
            snake_case_    							=				os.path.join(_a			,       "msr_paraphrase_train.txt"      )
            snake_case_    							=				os.path.join(_a			,       "msr_paraphrase_test.txt"      )
            urllib.request.urlretrieve(_a			,       _a      )
            urllib.request.urlretrieve(_a			,       _a      )
      if not os.path.isfile(_a      ):
            raise ValueError(F'''Train data not found at {mrpc_train_file}'''      )
      if not os.path.isfile(_a      ):
            raise ValueError(F'''Test data not found at {mrpc_test_file}'''      )
      urllib.request.urlretrieve(TASKaPATH["MRPC"]			,       os.path.join(_a			,       "dev_ids.tsv"      )      )

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

      with open(_a			,       encoding="utf8"      ) as data_fh, open(
          os.path.join(_a			,       "train.tsv"      )			,       "w"			,       encoding="utf8"      ) as train_fh, open(os.path.join(_a			,       "dev.tsv"      )			,       "w"			,       encoding="utf8"      ) as dev_fh:
            snake_case_    							=				data_fh.readline()
            train_fh.write(_a      )
            dev_fh.write(_a      )
            for row in data_fh:
                  snake_case_							,						snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    							=				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(_a			,       encoding="utf8"      ) as data_fh, open(
          os.path.join(_a			,       "test.tsv"      )			,       "w"			,       encoding="utf8"      ) as test_fh:
            snake_case_    							=				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(_a      ):
                  snake_case_							,						snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    							=				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 	snake_case_							(				_a  :				List[str]      ):
      print("Downloading and extracting diagnostic..."      )
      if not os.path.isdir(os.path.join(_a			,       "diagnostic"      )      ):
            os.mkdir(os.path.join(_a			,       "diagnostic"      )      )
      snake_case_    							=				os.path.join(_a			,       "diagnostic"			,       "diagnostic.tsv"      )
      urllib.request.urlretrieve(TASKaPATH["diagnostic"]			,       _a      )
      print("\tCompleted!"      )
      return
def 	snake_case_							(				_a  :				Optional[int]      ):
      snake_case_    							=				task_names.split(","      )
      if "all" in task_names:
            snake_case_    							=				TASKS
      else:
            snake_case_    							=				[]
            for task_name in task_names:
                  if task_name not in TASKS:
                        raise ValueError(F'''Task {task_name} not found!'''      )
                  tasks.append(_a      )
      return tasks
def 	snake_case_							(				_a  :				Union[str, Any]      ):
      snake_case_    							=				argparse.ArgumentParser()
      parser.add_argument("--data_dir"			,       help="directory to save data to"			,       type=_a			,       default="glue_data"      )
      parser.add_argument(
          "--tasks"			,       help="tasks to download data for as a comma separated string"			,       type=_a			,       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=_a			,       default=""			,       )
      snake_case_    							=				parser.parse_args(_a      )

      if not os.path.isdir(args.data_dir      ):
            os.mkdir(args.data_dir      )
      snake_case_    							=				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(_a			,       args.data_dir      )


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



'''simple docstring'''





import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
     from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


A							     = get_tests_dir("fixtures/test_sentencepiece.model")



@require_torch
@require_torchaudio
@require_sentencepiece
class 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			List[str]     ) ->							List[str]:
        snake_case_    							=				tempfile.mkdtemp()

        snake_case_    							=				["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
        snake_case_    							=				dict(zip(lowerCAmelCase_						,			range(len(lowerCAmelCase_     )     )     )     )
        snake_case_    							=				Path(self.tmpdirname     )
        save_json(lowerCAmelCase_						,			save_dir / VOCAB_FILES_NAMES["vocab_file"]     )
        if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
              copyfile(lowerCAmelCase_						,			save_dir / VOCAB_FILES_NAMES["spm_file"]     )

        snake_case_    							=				SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
        tokenizer.save_pretrained(self.tmpdirname     )

        snake_case_    							=				{
            "feature_size": 24,
            "num_mel_bins": 24,
            "padding_value": 0.0,
            "sampling_rate": 1_60_00,
            "return_attention_mask": False,
            "do_normalize": True,
        }
        save_json(lowerCAmelCase_						,			save_dir / FEATURE_EXTRACTOR_NAME     )

  def 		_lowerCamelCase						(self	:			Dict						,			**lowerCAmelCase_	:			Any     ) ->							Union[str, Any]:
        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[str]						,			**lowerCAmelCase_	:			List[str]     ) ->							Any:
        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			int     ) ->							str:
        shutil.rmtree(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							Optional[Any]:
        snake_case_    							=				self.get_tokenizer()
        snake_case_    							=				self.get_feature_extractor()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        processor.save_pretrained(self.tmpdirname     )
        snake_case_    							=				SpeechaTextProcessor.from_pretrained(self.tmpdirname     )

        self.assertEqual(processor.tokenizer.get_vocab()						,			tokenizer.get_vocab()     )
        self.assertIsInstance(processor.tokenizer						,			lowerCAmelCase_     )

        self.assertEqual(processor.feature_extractor.to_json_string()						,			feature_extractor.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor						,			lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			str     ) ->							int:
        snake_case_    							=				SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()						,			feature_extractor=self.get_feature_extractor()     )
        processor.save_pretrained(self.tmpdirname     )

        snake_case_    							=				self.get_tokenizer(bos_token="(BOS)"						,			eos_token="(EOS)"     )
        snake_case_    							=				self.get_feature_extractor(do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

        snake_case_    							=				SpeechaTextProcessor.from_pretrained(
            self.tmpdirname						,			bos_token="(BOS)"						,			eos_token="(EOS)"						,			do_normalize=lowerCAmelCase_						,			padding_value=1.0     )

        self.assertEqual(processor.tokenizer.get_vocab()						,			tokenizer_add_kwargs.get_vocab()     )
        self.assertIsInstance(processor.tokenizer						,			lowerCAmelCase_     )

        self.assertEqual(processor.feature_extractor.to_json_string()						,			feature_extractor_add_kwargs.to_json_string()     )
        self.assertIsInstance(processor.feature_extractor						,			lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Optional[Any]:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        snake_case_    							=				floats_list((3, 10_00)     )

        snake_case_    							=				feature_extractor(lowerCAmelCase_						,			return_tensors="np"     )
        snake_case_    							=				processor(lowerCAmelCase_						,			return_tensors="np"     )

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()						,			input_processor[key].sum()						,			delta=1E-2     )

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							int:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        snake_case_    							=				"This is a test string"

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        snake_case_    							=				tokenizer(lowerCAmelCase_     )

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]						,			encoded_processor[key]     )

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							int:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        snake_case_    							=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case_    							=				processor.batch_decode(lowerCAmelCase_     )
        snake_case_    							=				tokenizer.batch_decode(lowerCAmelCase_     )

        self.assertListEqual(lowerCAmelCase_						,			lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			int     ) ->							int:
        snake_case_    							=				self.get_feature_extractor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				SpeechaTextProcessor(tokenizer=lowerCAmelCase_						,			feature_extractor=lowerCAmelCase_     )

        self.assertListEqual(
            processor.model_input_names						,			feature_extractor.model_input_names						,			msg="`processor` and `feature_extractor` model input names do not match"						,			)

