'''simple docstring'''






import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple=2      ,		__snake_case       : Tuple=True      ,		__snake_case       : str=False							):
     _A								=     Accelerator(step_scheduler_with_optimizer=__snake_case      ,		split_batches=__snake_case							)
     _A								=     torch.nn.Linear(2      ,		4							)
     _A								=     torch.optim.AdamW(model.parameters()      ,		lr=1.0							)
     _A								=     torch.optim.lr_scheduler.OneCycleLR(__snake_case      ,		max_lr=0.01      ,		steps_per_epoch=2      ,		epochs=1							)
     _A		,					_A		,					_A								=     accelerator.prepare(__snake_case      ,		__snake_case      ,		__snake_case							)

     # Optimizer has stepped
     scheduler.step()
     if step_scheduler_with_optimizer or (num_processes == 1):
          assert (
              scheduler.scheduler.last_epoch == num_processes
          ), F'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
     else:
          assert (
              scheduler.scheduler.last_epoch != num_processes
          ), F'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Union[str, Any]=2      ,		__snake_case       : List[str]=True      ,		__snake_case       : Tuple=False							):
     _A								=     Accelerator(step_scheduler_with_optimizer=__snake_case      ,		split_batches=__snake_case							)
     _A								=     torch.nn.Linear(2      ,		4							)
     _A								=     torch.optim.AdamW(model.parameters()      ,		lr=1.0							)
     _A								=     torch.optim.lr_scheduler.LambdaLR(__snake_case      ,		lr_lambda=lambda __snake_case							: 1 - n / 1_0							)
     _A		,					_A		,					_A								=     accelerator.prepare(__snake_case      ,		__snake_case      ,		__snake_case							)

     # Optimizer has stepped
     _A								=     False
     scheduler.step()
     _A								=     1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
     assert (
         scheduler.get_last_lr()[0] == expected_lr
     ), F'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'

     # Optimizer has not stepped
     _A								=     True
     scheduler.step()
     if not step_scheduler_with_optimizer:
          _A								=     1 - 2 / 1_0
     assert (
         scheduler.get_last_lr()[0] == expected_lr
     ), F'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int = 2							):
     from transformers import get_linear_schedule_with_warmup

     _A								=     [1, 2, 4]
     for num_steps in steps:
          _A								=     GradientAccumulationPlugin(num_steps=__snake_case      ,		adjust_scheduler=num_steps > 1							)
          _A								=     Accelerator(gradient_accumulation_plugin=__snake_case							)
          _A								=     torch.nn.Linear(2      ,		4							)
          _A								=     torch.optim.AdamW(model.parameters()      ,		lr=10.0							)
          _A								=     get_linear_schedule_with_warmup(optimizer=__snake_case      ,		num_warmup_steps=0      ,		num_training_steps=2_0							)

          _A		,					_A		,					_A								=     accelerator.prepare(__snake_case      ,		__snake_case      ,		__snake_case							)

          for i in range(1_0 * num_steps							):
               with accelerator.accumulate(__snake_case							):
                    optimizer.step()
                    scheduler.step()

               if i == (1_0 * num_steps - 2):
                    assert (
                        scheduler.get_last_lr()[0] != 0
                    ), F'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
          assert (
              scheduler.get_last_lr()[0] == 0
          ), F'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
          GradientState._reset_state()







@require_cpu
class lowercase_					(		unittest.TestCase ):







   """simple docstring"""

   def   __UpperCAmelCase      (			self			: List[Any]    )     -> Optional[Any]:
        debug_launcher(partial(UpperCamelCase__,   num_processes=1    ),   num_processes=1    )
        debug_launcher(partial(UpperCamelCase__,   num_processes=1,   split_batches=UpperCamelCase__    ),   num_processes=1    )
   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> Tuple:
        debug_launcher(partial(UpperCamelCase__,   num_processes=1    ),   num_processes=1    )
        debug_launcher(partial(UpperCamelCase__,   num_processes=1,   split_batches=UpperCamelCase__    ),   num_processes=1    )
   def   __UpperCAmelCase      (			self			: List[str]    )     -> Any:
        debug_launcher(partial(UpperCamelCase__,   num_processes=1,   step_scheduler_with_optimizer=UpperCamelCase__    ),   num_processes=1    )
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> Union[str, Any]:
        debug_launcher(partial(UpperCamelCase__,   num_processes=1,   step_scheduler_with_optimizer=UpperCamelCase__    ),   num_processes=1    )
   def   __UpperCAmelCase      (			self			: List[str]    )     -> Optional[Any]:
        AcceleratorState._reset_state(UpperCamelCase__    )
        debug_launcher(UpperCamelCase__    )
        debug_launcher(partial(UpperCamelCase__,   num_processes=1,   split_batches=UpperCamelCase__    ),   num_processes=1    )
   def   __UpperCAmelCase      (			self			: str    )     -> List[str]:
        AcceleratorState._reset_state(UpperCamelCase__    )
        debug_launcher(UpperCamelCase__    )
        debug_launcher(partial(UpperCamelCase__,   num_processes=1,   split_batches=UpperCamelCase__    ),   num_processes=1    )
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> List[str]:
        AcceleratorState._reset_state(UpperCamelCase__    )
        debug_launcher(partial(UpperCamelCase__,   step_scheduler_with_optimizer=UpperCamelCase__    )    )
   def   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> Optional[Any]:
        AcceleratorState._reset_state(UpperCamelCase__    )
        debug_launcher(partial(UpperCamelCase__,   step_scheduler_with_optimizer=UpperCamelCase__    )    )

   @require_huggingface_suite
   def   __UpperCAmelCase      (			self			: Any    )     -> str:
        AcceleratorState._reset_state(UpperCamelCase__    )
        debug_launcher(partial(UpperCamelCase__,   num_processes=1    )    )
        debug_launcher(UpperCamelCase__    )




'''simple docstring'''






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


_UpperCAmelCase						:    int     					=					['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
_UpperCAmelCase						:    Dict     					=					{
    '''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''',
}

_UpperCAmelCase						:    Any     					=					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
_UpperCAmelCase						:    List[Any]     					=					'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Union[str, Any]							):
     print(F'Downloading and extracting {task}...'							)
     _A								=     F'{task}.zip'
     urllib.request.urlretrieve(TASKaPATH[task]      ,		__snake_case							)
     with zipfile.ZipFile(__snake_case							) as zip_ref:
          zip_ref.extractall(__snake_case							)
     os.remove(__snake_case							)
     print('\tCompleted!'							)





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

     _A								=     []
     with open(os.path.join(__snake_case      ,		'dev_ids.tsv'							)      ,		encoding='utf8'							) as ids_fh:
          for row in ids_fh:
               dev_ids.append(row.strip().split('\t'							)							)

     with open(__snake_case      ,		encoding='utf8'							) as data_fh, open(
         os.path.join(__snake_case      ,		'train.tsv'							)      ,		'w'      ,		encoding='utf8'							) as train_fh, open(os.path.join(__snake_case      ,		'dev.tsv'							)      ,		'w'      ,		encoding='utf8'							) as dev_fh:
          _A								=     data_fh.readline()
          train_fh.write(__snake_case							)
          dev_fh.write(__snake_case							)
          for row in data_fh:
               _A		,					_A		,					_A		,					_A		,					_A								=     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(__snake_case      ,		encoding='utf8'							) as data_fh, open(
         os.path.join(__snake_case      ,		'test.tsv'							)      ,		'w'      ,		encoding='utf8'							) as test_fh:
          _A								=     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(__snake_case							):
               _A		,					_A		,					_A		,					_A		,					_A								=     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       : List[Any]							):
     print('Downloading and extracting diagnostic...'							)
     if not os.path.isdir(os.path.join(__snake_case      ,		'diagnostic'							)							):
          os.mkdir(os.path.join(__snake_case      ,		'diagnostic'							)							)
     _A								=     os.path.join(__snake_case      ,		'diagnostic'      ,		'diagnostic.tsv'							)
     urllib.request.urlretrieve(TASKaPATH['diagnostic']      ,		__snake_case							)
     print('\tCompleted!'							)
     return





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     _A								=     task_names.split(','							)
     if "all" in task_names:
          _A								=     TASKS
     else:
          _A								=     []
          for task_name in task_names:
               if task_name not in TASKS:
                    raise ValueError(F'Task {task_name} not found!'							)
               tasks.append(__snake_case							)
     return tasks





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]							):
     _A								=     argparse.ArgumentParser()
     parser.add_argument('--data_dir'      ,		help='directory to save data to'      ,		type=__snake_case      ,		default='glue_data'							)
     parser.add_argument(
         '--tasks'      ,		help='tasks to download data for as a comma separated string'      ,		type=__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=__snake_case      ,		default=''      ,		)
     _A								=     parser.parse_args(__snake_case							)

     if not os.path.isdir(args.data_dir							):
          os.mkdir(args.data_dir							)
     _A								=     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(__snake_case      ,		args.data_dir							)


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



