'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
   from transformers.models.albert.modeling_tf_albert import (
       TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFAlbertForMaskedLM,
       TFAlbertForMultipleChoice,
       TFAlbertForPreTraining,
       TFAlbertForQuestionAnswering,
       TFAlbertForSequenceClassification,
       TFAlbertForTokenClassification,
       TFAlbertModel,
   )







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: int,   UpperCamelCase__			: Dict,   UpperCamelCase__			: Optional[Any]=13,   UpperCamelCase__			: Any=7,   UpperCamelCase__			: Union[str, Any]=True,   UpperCamelCase__			: Optional[Any]=True,   UpperCamelCase__			: Optional[int]=True,   UpperCamelCase__			: List[Any]=True,   UpperCamelCase__			: Optional[Any]=99,   UpperCamelCase__			: Any=16,   UpperCamelCase__			: Dict=32,   UpperCamelCase__			: Union[str, Any]=2,   UpperCamelCase__			: str=4,   UpperCamelCase__			: List[Any]=37,   UpperCamelCase__			: List[str]="gelu",   UpperCamelCase__			: List[Any]=0.1,   UpperCamelCase__			: Optional[int]=0.1,   UpperCamelCase__			: Any=5_12,   UpperCamelCase__			: Optional[Any]=16,   UpperCamelCase__			: Any=2,   UpperCamelCase__			: Dict=0.02,   UpperCamelCase__			: Tuple=3,   UpperCamelCase__			: Dict=4,   UpperCamelCase__			: str=None,   )     -> List[Any]:
        _A								=     parent
        _A								=     13
        _A								=     7
        _A								=     True
        _A								=     True
        _A								=     True
        _A								=     True
        _A								=     99
        _A								=     16
        _A								=     32
        _A								=     2
        _A								=     4
        _A								=     37
        _A								=     'gelu'
        _A								=     0.1
        _A								=     0.1
        _A								=     5_12
        _A								=     16
        _A								=     2
        _A								=     0.02
        _A								=     3
        _A								=     4
        _A								=     None
   def   __UpperCAmelCase      (			self			: Any    )     -> Union[str, Any]:
        _A								=     ids_tensor([self.batch_size, self.seq_length],   self.vocab_size    )

        _A								=     None
        if self.use_input_mask:
             _A								=     random_attention_mask([self.batch_size, self.seq_length]    )

        _A								=     None
        if self.use_token_type_ids:
             _A								=     ids_tensor([self.batch_size, self.seq_length],   self.type_vocab_size    )

        _A								=     None
        _A								=     None
        _A								=     None
        if self.use_labels:
             _A								=     ids_tensor([self.batch_size],   self.type_sequence_label_size    )
             _A								=     ids_tensor([self.batch_size, self.seq_length],   self.num_labels    )
             _A								=     ids_tensor([self.batch_size],   self.num_choices    )

        _A								=     AlbertConfig(
            vocab_size=self.vocab_size,   hidden_size=self.hidden_size,   embedding_size=self.embedding_size,   num_hidden_layers=self.num_hidden_layers,   num_attention_heads=self.num_attention_heads,   intermediate_size=self.intermediate_size,   hidden_act=self.hidden_act,   hidden_dropout_prob=self.hidden_dropout_prob,   attention_probs_dropout_prob=self.attention_probs_dropout_prob,   max_position_embeddings=self.max_position_embeddings,   type_vocab_size=self.type_vocab_size,   initializer_range=self.initializer_range,   )

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def   __UpperCAmelCase      (			self			: List[str],   UpperCamelCase__			: Dict,   UpperCamelCase__			: Dict,   UpperCamelCase__			: str,   UpperCamelCase__			: List[Any],   UpperCamelCase__			: Dict,   UpperCamelCase__			: Dict,   UpperCamelCase__			: int    )     -> Optional[Any]:
        _A								=     TFAlbertModel(config=UpperCamelCase__    )
        # inputs = {'input_ids': input_ids,
        #           'attention_mask': input_mask,
        #           'token_type_ids': token_type_ids}
        # sequence_output, pooled_output = model(**inputs)
        _A								=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        _A								=     model(UpperCamelCase__    )

        _A								=     [input_ids, input_mask]
        _A								=     model(UpperCamelCase__    )

        _A								=     model(UpperCamelCase__    )

        self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)    )
        self.parent.assertEqual(result.pooler_output.shape,   (self.batch_size, self.hidden_size)    )
   def   __UpperCAmelCase      (			self			: List[Any],   UpperCamelCase__			: Optional[int],   UpperCamelCase__			: List[str],   UpperCamelCase__			: int,   UpperCamelCase__			: str,   UpperCamelCase__			: Tuple,   UpperCamelCase__			: Tuple,   UpperCamelCase__			: List[Any]    )     -> List[str]:
        _A								=     self.num_labels
        _A								=     TFAlbertForPreTraining(config=UpperCamelCase__    )
        _A								=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        _A								=     model(UpperCamelCase__    )
        self.parent.assertEqual(result.prediction_logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)    )
        self.parent.assertEqual(result.sop_logits.shape,   (self.batch_size, self.num_labels)    )
   def   __UpperCAmelCase      (			self			: Dict,   UpperCamelCase__			: Any,   UpperCamelCase__			: Optional[int],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Dict,   UpperCamelCase__			: Tuple,   UpperCamelCase__			: str,   UpperCamelCase__			: Tuple    )     -> Optional[Any]:
        _A								=     TFAlbertForMaskedLM(config=UpperCamelCase__    )
        _A								=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        _A								=     model(UpperCamelCase__    )
        self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)    )
   def   __UpperCAmelCase      (			self			: Optional[int],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Any,   UpperCamelCase__			: Tuple,   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: int,   UpperCamelCase__			: Dict,   UpperCamelCase__			: str    )     -> List[str]:
        _A								=     self.num_labels
        _A								=     TFAlbertForSequenceClassification(config=UpperCamelCase__    )
        _A								=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        _A								=     model(UpperCamelCase__    )
        self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)    )
   def   __UpperCAmelCase      (			self			: int,   UpperCamelCase__			: Optional[Any],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Any,   UpperCamelCase__			: Dict,   UpperCamelCase__			: int,   UpperCamelCase__			: List[str]    )     -> List[Any]:
        _A								=     TFAlbertForQuestionAnswering(config=UpperCamelCase__    )
        _A								=     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        _A								=     model(UpperCamelCase__    )
        self.parent.assertEqual(result.start_logits.shape,   (self.batch_size, self.seq_length)    )
        self.parent.assertEqual(result.end_logits.shape,   (self.batch_size, self.seq_length)    )
   def   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: List[str],   UpperCamelCase__			: int,   UpperCamelCase__			: int,   UpperCamelCase__			: Any,   UpperCamelCase__			: Tuple,   UpperCamelCase__			: List[Any],   UpperCamelCase__			: Tuple    )     -> Union[str, Any]:
        _A								=     self.num_choices
        _A								=     TFAlbertForMultipleChoice(config=UpperCamelCase__    )
        _A								=     tf.tile(tf.expand_dims(UpperCamelCase__,   1    ),   (1, self.num_choices, 1)    )
        _A								=     tf.tile(tf.expand_dims(UpperCamelCase__,   1    ),   (1, self.num_choices, 1)    )
        _A								=     tf.tile(tf.expand_dims(UpperCamelCase__,   1    ),   (1, self.num_choices, 1)    )
        _A								=     {
            'input_ids': multiple_choice_inputs_ids,
            'attention_mask': multiple_choice_input_mask,
            'token_type_ids': multiple_choice_token_type_ids,
        }
        _A								=     model(UpperCamelCase__    )
        self.parent.assertListEqual(list(result['logits'].shape    ),   [self.batch_size, self.num_choices]    )
   def   __UpperCAmelCase      (			self			: List[str],   UpperCamelCase__			: int,   UpperCamelCase__			: List[Any],   UpperCamelCase__			: Union[str, Any],   UpperCamelCase__			: Dict,   UpperCamelCase__			: List[Any],   UpperCamelCase__			: Dict,   UpperCamelCase__			: Any    )     -> List[Any]:
        _A								=     self.num_labels
        _A								=     TFAlbertForTokenClassification(config=UpperCamelCase__    )
        _A								=     {
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _A								=     model(UpperCamelCase__    )
        self.parent.assertListEqual(list(result['logits'].shape    ),   [self.batch_size, self.seq_length, self.num_labels]    )

   def   __UpperCAmelCase      (			self			: Dict    )     -> int:
        _A								=     self.prepare_config_and_inputs()
        (
            (
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					(
            _A
        )		,					
        )								=     config_and_inputs
        _A								=     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_tf
class lowercase_					(		_UpperCamelCase						,						_UpperCamelCase						,						unittest.TestCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      (
       (
           TFAlbertModel,
           TFAlbertForPreTraining,
           TFAlbertForMaskedLM,
           TFAlbertForSequenceClassification,
           TFAlbertForQuestionAnswering,
           TFAlbertForTokenClassification,
           TFAlbertForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   __lowerCAmelCase    						=      (
       {
           "feature-extraction": TFAlbertModel,
           "fill-mask": TFAlbertForMaskedLM,
           "question-answering": TFAlbertForQuestionAnswering,
           "text-classification": TFAlbertForSequenceClassification,
           "token-classification": TFAlbertForTokenClassification,
           "zero-shot": TFAlbertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __lowerCAmelCase    						=      False
   __lowerCAmelCase    						=      False
   def   __UpperCAmelCase      (			self			: List[Any],   UpperCamelCase__			: List[Any],   UpperCamelCase__			: Any,   UpperCamelCase__			: List[Any]=False    )     -> Tuple:
        _A								=     super()._prepare_for_class(UpperCamelCase__,   UpperCamelCase__,   return_labels=UpperCamelCase__    )

        if return_labels:
             if model_class in get_values(UpperCamelCase__    ):
                  _A								=     tf.zeros(self.model_tester.batch_size,   dtype=tf.intaa    )

        return inputs_dict
   def   __UpperCAmelCase      (			self			: str    )     -> int:
        _A								=     TFAlbertModelTester(self    )
        _A								=     ConfigTester(self,   config_class=UpperCamelCase__,   hidden_size=37    )
   def   __UpperCAmelCase      (			self			: List[Any]    )     -> Optional[int]:
        self.config_tester.run_common_tests()
   def   __UpperCAmelCase      (			self			: Tuple    )     -> Union[str, Any]:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_model(*UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: int    )     -> Tuple:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_for_pretraining(*UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Optional[Any]    )     -> List[str]:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_for_masked_lm(*UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Dict    )     -> Union[str, Any]:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_for_multiple_choice(*UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Any    )     -> str:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_for_sequence_classification(*UpperCamelCase__    )
   def   __UpperCAmelCase      (			self			: Any    )     -> Tuple:
        _A								=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_albert_for_question_answering(*UpperCamelCase__    )

   @slow
   def   __UpperCAmelCase      (			self			: Dict    )     -> Tuple:
        for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A								=     TFAlbertModel.from_pretrained(UpperCamelCase__    )
             self.assertIsNotNone(UpperCamelCase__    )







@require_tf
class lowercase_					(		unittest.TestCase ):







   """simple docstring"""

   @slow
   def   __UpperCAmelCase      (			self			: List[str]    )     -> Union[str, Any]:
        _A								=     TFAlbertForPreTraining.from_pretrained('albert-base-v2'    )
        _A								=     tf.constant([[0, 1, 2, 3, 4, 5]]    )
        _A								=     model(UpperCamelCase__    )[0]

        _A								=     [1, 6, 3_00_00]
        self.assertEqual(output.shape,   UpperCamelCase__    )

        _A								=     tf.constant(
            [
                [
                    [4.595_668, 0.74_462_754, -1.818_147],
                    [4.5_954_347, 0.7_454_184, -1.8_188_258],
                    [4.5_954_905, 0.7_448_235, -1.8_182_316],
                ]
            ]    )
        tf.debugging.assert_near(output[:, :3, :3],   UpperCamelCase__,   atol=1e-4    )




'''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:]))



