
'''simple docstring'''
import argparse
import os
import sys
import urllib.request
import zipfile


lowercase__		    =	["CoLA", "SST", "MRPC", "QQP", "STS", "MNLI", "SNLI", "QNLI", "RTE", "WNLI", "diagnostic"]
lowercase__		    =	{
    "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__		    =	"https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
lowercase__		    =	"https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Tuple:


		'''simple docstring'''


		print(F'Downloading and extracting {task}...'       )
		snake_case   :    List[Any]			       =		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 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__       )   ->				Dict:


		'''simple docstring'''


		print('''Processing MRPC...'''       )
		snake_case   :    str			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''MRPC'''       )
		if not os.path.isdir(SCREAMING_SNAKE_CASE__       ):
				os.mkdir(SCREAMING_SNAKE_CASE__       )
		if path_to_data:
				snake_case   :    Tuple			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''msr_paraphrase_train.txt'''       )
				snake_case   :    List[str]			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''msr_paraphrase_test.txt'''       )
		else:
				print('''Local MRPC data not specified, downloading data from %s''' % MRPC_TRAIN       )
				snake_case   :    int			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''msr_paraphrase_train.txt'''       )
				snake_case   :    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'''       )       )

		snake_case   :    int			       =		[]
		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:
				snake_case   :    Optional[int]			       =		data_fh.readline()
				train_fh.write(SCREAMING_SNAKE_CASE__       )
				dev_fh.write(SCREAMING_SNAKE_CASE__       )
				for row in data_fh:
						snake_case       ,snake_case       ,snake_case       ,snake_case       ,snake_case   :    List[Any]			       =		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:
				snake_case   :    List[str]			       =		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__       ):
						snake_case       ,snake_case       ,snake_case       ,snake_case       ,snake_case   :    Dict			       =		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 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				Optional[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'''       )       )
		snake_case   :    Any			       =		os.path.join(SCREAMING_SNAKE_CASE__      ,    '''diagnostic'''      ,    '''diagnostic.tsv'''       )
		urllib.request.urlretrieve(TASKaPATH['''diagnostic''']      ,    SCREAMING_SNAKE_CASE__       )
		print('''\tCompleted!'''       )
		return




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[str]:


		'''simple docstring'''


		snake_case   :    int			       =		task_names.split(''','''       )
		if "all" in task_names:
				snake_case   :    int			       =		TASKS
		else:
				snake_case   :    List[str]			       =		[]
				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 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    List[Any]			       =		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=''''''      ,    )
		snake_case   :    List[Any]			       =		parser.parse_args(SCREAMING_SNAKE_CASE__       )

		if not os.path.isdir(args.data_dir       ):
				os.mkdir(args.data_dir       )
		snake_case   :    Tuple			       =		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 os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


_UpperCamelCase			       =			namedtuple('''ExampleNamedTuple''', '''a b c''')



class     lowercase						(       unittest.TestCase					):



 '''simple docstring'''


 def   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				torch.randn(5       ,       2 )
  UpperCAmelCase__							=				torch.device('cuda' ) if torch.cuda.is_available() else torch.device('cpu' )

  UpperCAmelCase__							=				send_to_device(__a       ,       __a )
  self.assertTrue(torch.equal(resulta.cpu()       ,       __a ) )

  UpperCAmelCase__							=				send_to_device((tensor, [tensor, tensor], 1)       ,       __a )
  self.assertIsInstance(__a       ,       __a )
  self.assertTrue(torch.equal(resulta[0].cpu()       ,       __a ) )
  self.assertIsInstance(resulta[1]       ,       __a )
  self.assertTrue(torch.equal(resulta[1][0].cpu()       ,       __a ) )
  self.assertTrue(torch.equal(resulta[1][1].cpu()       ,       __a ) )
  self.assertEqual(resulta[2]       ,       1 )

  UpperCAmelCase__							=				send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}       ,       __a )
  self.assertIsInstance(__a       ,       __a )
  self.assertTrue(torch.equal(resulta['a'].cpu()       ,       __a ) )
  self.assertIsInstance(resulta['b']       ,       __a )
  self.assertTrue(torch.equal(resulta['b'][0].cpu()       ,       __a ) )
  self.assertTrue(torch.equal(resulta['b'][1].cpu()       ,       __a ) )
  self.assertEqual(resulta['c']       ,       1 )

  UpperCAmelCase__							=				send_to_device(ExampleNamedTuple(a=__a       ,       b=[tensor, tensor]       ,       c=1 )       ,       __a )
  self.assertIsInstance(__a       ,       __a )
  self.assertTrue(torch.equal(resulta.a.cpu()       ,       __a ) )
  self.assertIsInstance(resulta.b       ,       __a )
  self.assertTrue(torch.equal(resulta.b[0].cpu()       ,       __a ) )
  self.assertTrue(torch.equal(resulta.b[1].cpu()       ,       __a ) )
  self.assertEqual(resulta.c       ,       1 )

  UpperCAmelCase__							=				send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1} )       ,       __a )
  self.assertIsInstance(__a       ,       __a )
  self.assertTrue(torch.equal(resulta['a'].cpu()       ,       __a ) )
  self.assertIsInstance(resulta['b']       ,       __a )
  self.assertTrue(torch.equal(resulta['b'][0].cpu()       ,       __a ) )
  self.assertTrue(torch.equal(resulta['b'][1].cpu()       ,       __a ) )
  self.assertEqual(resulta['c']       ,       1 )




 def   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  with self.assertRaises(__a ) as cm:
   UpperCAmelCase__							=				recursively_apply(torch.tensor       ,       (torch.tensor(1 ), 1)       ,       error_on_other_type=__a )
  self.assertEqual(
      str(cm.exception )       ,       'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'       ,       )




 def   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				torch.tensor([1, 2, 3, 4, 5] )
  self.assertEqual(listify(__a )       ,       [1, 2, 3, 4, 5] )

  UpperCAmelCase__							=				torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] )
  self.assertEqual(listify(__a )       ,       [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] )

  UpperCAmelCase__							=				torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]] )
  self.assertEqual(
      listify(__a )       ,       [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]] )




 def   UpperCamelCase__							(self ) ->					int:






  """simple docstring"""





  with patch_environment(aa=1       ,       BB=2 ):
   self.assertEqual(os.environ.get('AA' )       ,       '1' )
   self.assertEqual(os.environ.get('BB' )       ,       '2' )

  self.assertNotIn('AA'       ,       os.environ )
  self.assertNotIn('BB'       ,       os.environ )




 def   UpperCamelCase__							(self ) ->					Dict:






  """simple docstring"""





  UpperCAmelCase__							=				RegressionModel()
  UpperCAmelCase__							=				model.forward
  UpperCAmelCase__							=				convert_outputs_to_fpaa(model.forward )
  UpperCAmelCase__							=				extract_model_from_parallel(__a       ,       keep_fpaa_wrapper=__a )
  UpperCAmelCase__							=				pickle.dumps(__a )




 @require_cuda
 def   UpperCamelCase__							(self ) ->					Tuple:






  """simple docstring"""





  UpperCAmelCase__							=				RegressionModel()
  UpperCAmelCase__							=				model.forward
  UpperCAmelCase__							=				torch.cuda.amp.autocast(dtype=torch.floataa )(model.forward )
  UpperCAmelCase__							=				convert_outputs_to_fpaa(model.forward )
  UpperCAmelCase__							=				extract_model_from_parallel(__a       ,       keep_fpaa_wrapper=__a )
  UpperCAmelCase__							=				pickle.dumps(__a )




 @require_cuda
 @require_torch_min_version(version='2.0' )
 def   UpperCamelCase__							(self ) ->					List[str]:






  """simple docstring"""





  UpperCAmelCase__							=				RegressionModel()
  UpperCAmelCase__							=				model.forward
  UpperCAmelCase__							=				torch.cuda.amp.autocast(dtype=torch.floataa )(model.forward )
  UpperCAmelCase__							=				convert_outputs_to_fpaa(model.forward )
  UpperCAmelCase__							=				torch.compile(model.forward       ,       backend='inductor' )
  UpperCAmelCase__							=				torch.randn(4       ,       10 ).cuda()
  UpperCAmelCase__							=				model(__a )




 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				RegressionModel()
  # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  UpperCAmelCase__							=				torch.nn.parallel.DataParallel(__a )
  UpperCAmelCase__							=				extract_model_from_parallel(__a )

  self.assertEqual(__a       ,       __a )




 @require_torch_min_version(version='2.0' )
 def   UpperCamelCase__							(self ) ->					Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__							=				RegressionModel()
  UpperCAmelCase__							=				torch.compile(__a )

  # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
  UpperCAmelCase__							=				torch.nn.parallel.DataParallel(__a )
  UpperCAmelCase__							=				torch.compile(__a )
  UpperCAmelCase__							=				extract_model_from_parallel(__a )

  self.assertEqual(compiled_model._orig_mod       ,       compiled_model_unwrapped._orig_mod )




 def   UpperCamelCase__							(self ) ->					str:






  """simple docstring"""





  self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3] )] )       ,       torch.device('cpu' ) )
  self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3] )} )       ,       torch.device('cpu' ) )
  self.assertIsNone(find_device([1, 'a'] ) )



