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


lowercase						:      Union[str, Any]							=							["""CoLA""", """SST""", """MRPC""", """QQP""", """STS""", """MNLI""", """SNLI""", """QNLI""", """RTE""", """WNLI""", """diagnostic"""]
lowercase						:      Optional[int]							=							{
    """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						:      Optional[Any]							=							"""https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"""
lowercase						:      Any							=							"""https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"""







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					List[Any]:
       print(f"Downloading and extracting {task}..."       )
       lowercase							:  Union[str, 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 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Dict:
       print("""Processing MRPC..."""       )
       lowercase							:  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:
              lowercase							:  Optional[int] 			=   os.path.join(SCREAMING_SNAKE_CASE__      ,			"""msr_paraphrase_train.txt"""       )
              lowercase							:  Optional[int] 			=   os.path.join(SCREAMING_SNAKE_CASE__      ,			"""msr_paraphrase_test.txt"""       )
       else:
              print("""Local MRPC data not specified, downloading data from %s""" % MRPC_TRAIN       )
              lowercase							:  Any 			=   os.path.join(SCREAMING_SNAKE_CASE__      ,			"""msr_paraphrase_train.txt"""       )
              lowercase							:  str 			=   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"""       )       )

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







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Union[str, Any]:
       lowercase							:  Optional[Any] 			=   task_names.split(""","""       )
       if "all" in task_names:
              lowercase							:  Union[str, Any] 			=   TASKS
       else:
              lowercase							:  List[Any] 			=   []
              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 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  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=""""""      ,			)
       lowercase							:  Tuple 			=   parser.parse_args(SCREAMING_SNAKE_CASE__       )

       if not os.path.isdir(args.data_dir       ):
              os.mkdir(args.data_dir       )
       lowercase							:  Optional[Any] 			=   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 collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


lowercase						:      Any							=							logging.get_logger(__name__)

class __snake_case	:
    def __init__(						self	,snake_case	,snake_case=21	,snake_case=[]	,snake_case=None   ):



           '''simple docstring'''







           lowercase							:  Tuple 			=   model
           lowercase							:  List[Any] 			=   trace_batch_nums
           lowercase							:  Union[str, 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							:  List[Any] 			=   collections.deque([]	,snake_case   )
           lowercase							:  List[Any] 			=   []
           lowercase							:  Tuple 			=   0
           lowercase							:  Optional[Any] 			=   0
           lowercase							:  int 			=   False
           lowercase							:  List[str] 			=   """                 """

           self.analyse_model()

           self.register_forward_hook()
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case=None   ):



           '''simple docstring'''







           if frame is not None:
                  self.expand_frame(snake_case   )
           self.frames.append("""\n""".join(self.frame   )   )
           lowercase							:  List[str] 			=   []  # start a new frame
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           self.frame.append(snake_case   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           print("""\n""".join(self.frames   )   )
           lowercase							:  Any 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''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							:  Dict 			=   []
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''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    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case   ):



           '''simple docstring'''







           if torch.is_tensor(snake_case   ):
                  self.expand_frame(get_abs_min_max(snake_case	,snake_case   )   )
                  if detect_overflow(snake_case	,snake_case   ):
                         lowercase							:  str 			=   True
           elif var is None:
                  self.expand_frame(f"{'None':>17} {ctx}"   )
           else:
                  self.expand_frame(f"{'not a tensor':>17} {ctx}"   )
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''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    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







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

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

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

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

           self.save_frame()
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           self.model.apply(self._register_forward_hook   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



           '''simple docstring'''







           module.register_forward_hook(self.forward_hook   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   False

           lowercase							:  str 			=   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							:  Tuple 			=   True

           self.create_frame(snake_case	,snake_case	,snake_case   )

           # 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 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					List[str]:
       lowercase							:  str 			=   var.abs()
       return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       lowercase							:  Any 			=   False
       if torch.isnan(SCREAMING_SNAKE_CASE__       ).any().item():
              lowercase							:  Tuple 			=   True
              print(f"{ctx} has nans"       )
       if torch.isinf(SCREAMING_SNAKE_CASE__       ).any().item():
              lowercase							:  Union[str, Any] 			=   True
              print(f"{ctx} has infs"       )

       # if needed to monitor large elements can enable the following
       if 0:  # and detected:
              lowercase							:  Optional[int] 			=   var[torch.ge(var.abs()      ,			100       )]
              if naaa.numel() > 0:
                     print(f"{ctx}:  n100={naaa.numel()}"       )
              lowercase							:  Union[str, Any] 			=   var[torch.ge(var.abs()      ,			1_000       )]
              if naaaa.numel() > 0:
                     print(f"{ctx}: n1000={naaaa.numel()}"       )
              lowercase							:  List[str] 			=   var[torch.ge(var.abs()      ,			10_000       )]
              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 __snake_case	(   lowerCAmelCase  ):
    _a	:		Dict=       "underflow_overflow"
    _a	:		Union[str, Any]=       "tpu_metrics_debug"
