

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


_UpperCamelCase     =    ['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
_UpperCamelCase     =    {
    '''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     =    '''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
_UpperCamelCase     =    '''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''
def 		UpperCamelCase_(    snake_case__:						str						,					snake_case__:						int     ) ->				List[Any]:
 print(f"Downloading and extracting {task}..."     )
 UpperCAmelCase__           =       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 		UpperCamelCase_(    snake_case__:						str						,					snake_case__:						int     ) ->				int:
 print('Processing MRPC...'     )
 UpperCAmelCase__           =       os.path.join(snake_case__						,					'MRPC'     )
 if not os.path.isdir(snake_case__     ):
  os.mkdir(snake_case__     )
 if path_to_data:
  UpperCAmelCase__           =       os.path.join(snake_case__						,					'msr_paraphrase_train.txt'     )
  UpperCAmelCase__           =       os.path.join(snake_case__						,					'msr_paraphrase_test.txt'     )
 else:
  print('Local MRPC data not specified, downloading data from %s' % MRPC_TRAIN     )
  UpperCAmelCase__           =       os.path.join(snake_case__						,					'msr_paraphrase_train.txt'     )
  UpperCAmelCase__           =       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'     )     )

 UpperCAmelCase__           =       []
 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:
  UpperCAmelCase__           =       data_fh.readline()
  train_fh.write(snake_case__     )
  dev_fh.write(snake_case__     )
  for row in data_fh:
   UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       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:
  UpperCAmelCase__           =       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__     ):
   UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       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_(    snake_case__:						str     ) ->				Dict:
 print('Downloading and extracting diagnostic...'     )
 if not os.path.isdir(os.path.join(snake_case__						,					'diagnostic'     )     ):
  os.mkdir(os.path.join(snake_case__						,					'diagnostic'     )     )
 UpperCAmelCase__           =       os.path.join(snake_case__						,					'diagnostic'						,					'diagnostic.tsv'     )
 urllib.request.urlretrieve(TASKaPATH['diagnostic']						,					snake_case__     )
 print('\tCompleted!'     )
 return
def 		UpperCamelCase_(    snake_case__:						Optional[Any]     ) ->				Tuple:
 UpperCAmelCase__           =       task_names.split(','     )
 if "all" in task_names:
  UpperCAmelCase__           =       TASKS
 else:
  UpperCAmelCase__           =       []
  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 		UpperCamelCase_(    snake_case__:						Union[str, Any]     ) ->				int:
 UpperCAmelCase__           =       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=''						,					)
 UpperCAmelCase__           =       parser.parse_args(snake_case__     )

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






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_UpperCamelCase     =    logging.getLogger(__name__)



@dataclass
class 					lowercase		:



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       field(
     metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}      )
 __SCREAMING_SNAKE_CASE      							=       field(
     default=_UpperCamelCase				,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
 __SCREAMING_SNAKE_CASE      							=       field(
     default=_UpperCamelCase				,       metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}      )
 __SCREAMING_SNAKE_CASE      							=       field(default=_UpperCamelCase				,       metadata={"""help""": """Set this flag to use fast tokenization."""}      )
 # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
 # or just modify its tokenizer_config.json.
 __SCREAMING_SNAKE_CASE      							=       field(
     default=_UpperCamelCase				,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}				,       )







def 		UpperCamelCase_(    ) ->				List[str]:
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 UpperCAmelCase__           =       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

 if len(sys.argv     ) == 2 and sys.argv[1].endswith('.json'     ):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
 else:
  UpperCAmelCase__       ,					UpperCAmelCase__       ,					UpperCAmelCase__           =       parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir     )
     and os.listdir(training_args.output_dir     )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
      ' --overwrite_output_dir to overcome.'     )

 # Setup logging
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'						,					datefmt='%m/%d/%Y %H:%M:%S'						,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,					)
 logger.warning(
     'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'						,					training_args.local_rank						,					training_args.device						,					training_args.n_gpu						,					bool(training_args.local_rank != -1     )						,					training_args.fpaa						,					)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank     ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info('Training/evaluation parameters %s'						,					snake_case__     )

 # Prepare Question-Answering task
 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 UpperCAmelCase__           =       AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path						,					cache_dir=model_args.cache_dir						,					)
 UpperCAmelCase__           =       AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,					cache_dir=model_args.cache_dir						,					use_fast=snake_case__						,					)
 UpperCAmelCase__           =       AutoModelForQuestionAnswering.from_pretrained(
     model_args.model_name_or_path						,					from_tf=bool('.ckpt' in model_args.model_name_or_path     )						,					config=snake_case__						,					cache_dir=model_args.cache_dir						,					)

 # Get datasets
 UpperCAmelCase__           =       hasattr(model.config						,					'lang2id'     )
 UpperCAmelCase__           =       (
     SquadDataset(
         snake_case__						,					tokenizer=snake_case__						,					is_language_sensitive=snake_case__						,					cache_dir=model_args.cache_dir     )
     if training_args.do_train
     else None
 )
 UpperCAmelCase__           =       (
     SquadDataset(
         snake_case__						,					tokenizer=snake_case__						,					mode='dev'						,					is_language_sensitive=snake_case__						,					cache_dir=model_args.cache_dir						,					)
     if training_args.do_eval
     else None
 )

 # Data collator
 UpperCAmelCase__           =       DataCollatorWithPadding(snake_case__						,					pad_to_multiple_of=8     ) if training_args.fpaa else None

 # Initialize our Trainer
 UpperCAmelCase__           =       Trainer(
     model=snake_case__						,					args=snake_case__						,					train_dataset=snake_case__						,					eval_dataset=snake_case__						,					data_collator=snake_case__						,					)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path     ) else None     )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_master():
   tokenizer.save_pretrained(training_args.output_dir     )
def 		UpperCamelCase_(    snake_case__:						Optional[int]     ) ->				Union[str, Any]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
 main()



