



'''simple docstring'''






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


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

lowerCamelCase__	  =		'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'
lowerCamelCase__	  =		'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Union[str, Any] ,       snake_case_    :     Dict  )		->int:






							'''simple docstring'''
							print(F"Downloading and extracting {task}..."  )
							_lowercase							:		int				=   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_    :     Optional[Any]  )		->Dict:






							'''simple docstring'''
							print('''Processing MRPC...'''  )
							_lowercase							:		Dict				=   os.path.join(snake_case_ ,       '''MRPC'''  )
							if not os.path.isdir(snake_case_  ):
														os.mkdir(snake_case_  )
							if path_to_data:
														_lowercase							:		int				=   os.path.join(snake_case_ ,       '''msr_paraphrase_train.txt'''  )
														_lowercase							:		Optional[int]				=   os.path.join(snake_case_ ,       '''msr_paraphrase_test.txt'''  )
							else:
														print('''Local MRPC data not specified, downloading data from %s''' % MRPC_TRAIN  )
														_lowercase							:		Optional[Any]				=   os.path.join(snake_case_ ,       '''msr_paraphrase_train.txt'''  )
														_lowercase							:		str				=   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'''  )  )

							_lowercase							:		Any				=   []
							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:
														_lowercase							:		str				=   data_fh.readline()
														train_fh.write(snake_case_  )
														dev_fh.write(snake_case_  )
														for row in data_fh:
																					_lowercase			, _lowercase			, _lowercase			, _lowercase			, _lowercase							:		Tuple				=   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:
														_lowercase							:		Optional[Any]				=   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_  ):
																					_lowercase			, _lowercase			, _lowercase			, _lowercase			, _lowercase							:		Optional[int]				=   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_    :     Union[str, Any]  )		->Optional[Any]:






							'''simple docstring'''
							print('''Downloading and extracting diagnostic...'''  )
							if not os.path.isdir(os.path.join(snake_case_ ,       '''diagnostic'''  )  ):
														os.mkdir(os.path.join(snake_case_ ,       '''diagnostic'''  )  )
							_lowercase							:		int				=   os.path.join(snake_case_ ,       '''diagnostic''' ,       '''diagnostic.tsv'''  )
							urllib.request.urlretrieve(TASKaPATH['''diagnostic'''] ,       snake_case_  )
							print('''\tCompleted!'''  )
							return





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     int  )		->Tuple:






							'''simple docstring'''
							_lowercase							:		Any				=   task_names.split(''','''  )
							if "all" in task_names:
														_lowercase							:		Union[str, Any]				=   TASKS
							else:
														_lowercase							:		Optional[int]				=   []
														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_    :     Dict  )		->Dict:






							'''simple docstring'''
							_lowercase							:		Tuple				=   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='''''' ,       )
							_lowercase							:		Union[str, Any]				=   parser.parse_args(snake_case_  )

							if not os.path.isdir(args.data_dir  ):
														os.mkdir(args.data_dir  )
							_lowercase							:		int				=   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:]))








'''simple docstring'''






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


lowerCamelCase__	  =		logging.get_logger(__name__)

class 	_lowerCAmelCase     :






		'''simple docstring'''
		def __init__(						self							:       Optional[int]      ,		UpperCamelCase_							:       Optional[Any]      ,		UpperCamelCase_							:       Union[str, Any]=21      ,		UpperCamelCase_							:       Dict=[]      ,		UpperCamelCase_							:       Any=None		)      ->			Any:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   model
									_lowercase							:		Tuple				=   trace_batch_nums
									_lowercase							:		Optional[Any]				=   abort_after_batch_num

									# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
									_lowercase							:		int				=   collections.deque([]      ,		UpperCamelCase_		)
									_lowercase							:		List[Any]				=   []
									_lowercase							:		List[str]				=   0
									_lowercase							:		Optional[int]				=   0
									_lowercase							:		List[Any]				=   False
									_lowercase							:		List[Any]				=   '''                 '''

									self.analyse_model()

									self.register_forward_hook()
		def 						__lowercase      (						self							:       Union[str, Any]      ,		UpperCamelCase_							:       Optional[Any]=None		)      ->			Optional[Any]:







									'''simple docstring'''





									if frame is not None:
																self.expand_frame(UpperCamelCase_		)
									self.frames.append('''\n'''.join(self.frame		)		)
									_lowercase							:		Dict				=   []  # start a new frame
		def 						__lowercase      (						self							:       Union[str, Any]      ,		UpperCamelCase_							:       Optional[Any]		)      ->			Union[str, Any]:







									'''simple docstring'''





									self.frame.append(UpperCamelCase_		)
		def 						__lowercase      (						self							:       str		)      ->			List[str]:







									'''simple docstring'''





									print('''\n'''.join(self.frames		)		)
									_lowercase							:		Union[str, Any]				=   []
		def 						__lowercase      (						self							:       Optional[int]		)      ->			List[str]:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   []
		def 						__lowercase      (						self							:       List[Any]		)      ->			Optional[int]:







									'''simple docstring'''





									print(F"\nDetected inf/nan during batch_number={self.batch_number}"		)
									print(F"Last {len(self.frames		)} forward frames:"		)
									print(F"{'abs min':8} {'abs max':8} metadata"		)
									print('''\n'''.join(self.frames		)		)
									print('''\n\n'''		)
									_lowercase							:		List[Any]				=   []
		def 						__lowercase      (						self							:       List[str]		)      ->			Tuple:







									'''simple docstring'''





									_lowercase							:		int				=   {m: name for name, m in self.model.named_modules()}
									# self.longest_module_name = max(len(v) for v in self.module_names.values())
		def 						__lowercase      (						self							:       Dict      ,		UpperCamelCase_							:       str      ,		UpperCamelCase_							:       Optional[int]		)      ->			int:







									'''simple docstring'''





									if torch.is_tensor(UpperCamelCase_		):
																self.expand_frame(get_abs_min_max(UpperCamelCase_      ,		UpperCamelCase_		)		)
																if detect_overflow(UpperCamelCase_      ,		UpperCamelCase_		):
																							_lowercase							:		Any				=   True
									elif var is None:
																self.expand_frame(F"{'None':>17} {ctx}"		)
									else:
																self.expand_frame(F"{'not a tensor':>17} {ctx}"		)
		def 						__lowercase      (						self							:       Union[str, Any]		)      ->			int:







									'''simple docstring'''





									self.expand_frame(F"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"		)
									self.expand_frame(F"{'abs min':8} {'abs max':8} metadata"		)
		def 						__lowercase      (						self							:       str		)      ->			Any:







									'''simple docstring'''





									self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"		)
		def 						__lowercase      (						self							:       Tuple      ,		UpperCamelCase_							:       Any      ,		UpperCamelCase_							:       Union[str, Any]      ,		UpperCamelCase_							:       Optional[int]		)      ->			Dict:







									'''simple docstring'''





									self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"		)

									# params
									for name, p in module.named_parameters(recurse=UpperCamelCase_		):
																self.analyse_variable(UpperCamelCase_      ,		UpperCamelCase_		)

									# inputs
									if isinstance(UpperCamelCase_      ,		UpperCamelCase_		):
																for i, x in enumerate(UpperCamelCase_		):
																							self.analyse_variable(UpperCamelCase_      ,		F"input[{i}]"		)
									else:
																self.analyse_variable(UpperCamelCase_      ,		'''input'''		)

									# outputs
									if isinstance(UpperCamelCase_      ,		UpperCamelCase_		):
																for i, x in enumerate(UpperCamelCase_		):
																							# possibly a tuple of tuples
																							if isinstance(UpperCamelCase_      ,		UpperCamelCase_		):
																														for j, y in enumerate(UpperCamelCase_		):
																																					self.analyse_variable(UpperCamelCase_      ,		F"output[{i}][{j}]"		)
																							else:
																														self.analyse_variable(UpperCamelCase_      ,		F"output[{i}]"		)
									else:
																self.analyse_variable(UpperCamelCase_      ,		'''output'''		)

									self.save_frame()
		def 						__lowercase      (						self							:       Optional[int]		)      ->			Dict:







									'''simple docstring'''





									self.model.apply(self._register_forward_hook		)
		def 						__lowercase      (						self							:       Optional[int]      ,		UpperCamelCase_							:       int		)      ->			List[str]:







									'''simple docstring'''





									module.register_forward_hook(self.forward_hook		)





		def 						__lowercase      (						self							:       Optional[int]      ,		UpperCamelCase_							:       Optional[int]      ,		UpperCamelCase_							:       Dict      ,		UpperCamelCase_							:       List[Any]		)      ->			List[Any]:







									'''simple docstring'''





									_lowercase							:		str				=   False

									_lowercase							:		Dict				=   True if self.batch_number in self.trace_batch_nums else False
									if trace_mode:
																self.reset_saved_frames()

									if self.total_calls == 0:
																self.batch_start_frame()
									self.total_calls += 1

									# count batch numbers - the very first forward hook of the batch will be called when the
									# batch completes - i.e. it gets called very last - we know this batch has finished
									if module == self.model:
																self.batch_number += 1
																_lowercase							:		Union[str, Any]				=   True

									self.create_frame(UpperCamelCase_      ,		UpperCamelCase_      ,		UpperCamelCase_		)

									# if last_frame_of_batch:
									#     self.batch_end_frame()

									if trace_mode:
																self.trace_frames()

									if last_frame_of_batch:
																self.batch_start_frame()

									if self.detected_overflow and not trace_mode:
																self.dump_saved_frames()

																# now we can abort, as it's pointless to continue running
																raise ValueError(
																    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
																    '''Please scroll up above this traceback to see the activation values prior to this event.'''		)

									# abort after certain batch if requested to do so
									if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
																raise ValueError(
																    F"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
																    F" `abort_after_batch_num={self.abort_after_batch_num}` arg"		)





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Any ,       snake_case_    :     Dict  )		->Any:






							'''simple docstring'''
							_lowercase							:		Union[str, Any]				=   var.abs()
							return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     int ,       snake_case_    :     Any  )		->int:






							'''simple docstring'''
							_lowercase							:		List[Any]				=   False
							if torch.isnan(snake_case_  ).any().item():
														_lowercase							:		Any				=   True
														print(F"{ctx} has nans"  )
							if torch.isinf(snake_case_  ).any().item():
														_lowercase							:		Optional[Any]				=   True
														print(F"{ctx} has infs"  )

							# if needed to monitor large elements can enable the following
							if 0:  # and detected:
														_lowercase							:		List[Any]				=   var[torch.ge(var.abs() ,       1_00  )]
														if naaa.numel() > 0:
																					print(F"{ctx}:  n100={naaa.numel()}"  )
														_lowercase							:		List[str]				=   var[torch.ge(var.abs() ,       10_00  )]
														if naaaa.numel() > 0:
																					print(F"{ctx}: n1000={naaaa.numel()}"  )
														_lowercase							:		Tuple				=   var[torch.ge(var.abs() ,       1_00_00  )]
														if naaaaa.numel() > 0:
																					print(F"{ctx}: n10000={naaaaa.numel()}"  )

							if 0:
														print(F"min={var.min():9.2e} max={var.max():9.2e}"  )

							if 0:
														print(F"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"  )

							return detected




class 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		snake_case_							= 'underflow_overflow'
		snake_case_							= 'tpu_metrics_debug'



