





"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
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_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFXLMForMultipleChoice,
           TFXLMForQuestionAnsweringSimple,
           TFXLMForSequenceClassification,
           TFXLMForTokenClassification,
           TFXLMModel,
           TFXLMWithLMHeadModel,
           XLMConfig,
       )

class 		lowercase :





      def __init__(	self				,						lowercase				,						)		->					Tuple:
            lowerCAmelCase  						=	parent
            lowerCAmelCase  						=	13
            lowerCAmelCase  						=	7
            lowerCAmelCase  						=	True
            lowerCAmelCase  						=	True
            lowerCAmelCase  						=	True
            lowerCAmelCase  						=	True
            lowerCAmelCase  						=	True
            lowerCAmelCase  						=	False
            lowerCAmelCase  						=	False
            lowerCAmelCase  						=	False
            lowerCAmelCase  						=	2
            lowerCAmelCase  						=	99
            lowerCAmelCase  						=	0
            lowerCAmelCase  						=	32
            lowerCAmelCase  						=	2
            lowerCAmelCase  						=	4
            lowerCAmelCase  						=	0.1
            lowerCAmelCase  						=	0.1
            lowerCAmelCase  						=	512
            lowerCAmelCase  						=	16
            lowerCAmelCase  						=	2
            lowerCAmelCase  						=	0.02
            lowerCAmelCase  						=	3
            lowerCAmelCase  						=	4
            lowerCAmelCase  						=	"""last"""
            lowerCAmelCase  						=	True
            lowerCAmelCase  						=	None
            lowerCAmelCase  						=	0





      def    _snake_case      (	self			)		->					str:
            lowerCAmelCase  						=	ids_tensor([self.batch_size, self.seq_length]				,						self.vocab_size			)
            lowerCAmelCase  						=	random_attention_mask([self.batch_size, self.seq_length]				,						dtype=tf.floataa			)

            lowerCAmelCase  						=	None
            if self.use_input_lengths:
                  lowerCAmelCase  						=	(
                      ids_tensor([self.batch_size]				,						vocab_size=2			) + self.seq_length - 2
                  )  # small variation of seq_length

            lowerCAmelCase  						=	None
            if self.use_token_type_ids:
                  lowerCAmelCase  						=	ids_tensor([self.batch_size, self.seq_length]				,						self.n_langs			)

            lowerCAmelCase  						=	None
            lowerCAmelCase  						=	None
            lowerCAmelCase  						=	None
            if self.use_labels:
                  lowerCAmelCase  						=	ids_tensor([self.batch_size]				,						self.type_sequence_label_size			)
                  lowerCAmelCase  						=	ids_tensor([self.batch_size, self.seq_length]				,						self.num_labels			)
                  lowerCAmelCase  						=	ids_tensor([self.batch_size]				,						2				,						dtype=tf.floataa			)
                  lowerCAmelCase  						=	ids_tensor([self.batch_size]				,						self.num_choices			)

            lowerCAmelCase  						=	XLMConfig(
                vocab_size=self.vocab_size				,						n_special=self.n_special				,						emb_dim=self.hidden_size				,						n_layers=self.num_hidden_layers				,						n_heads=self.num_attention_heads				,						dropout=self.hidden_dropout_prob				,						attention_dropout=self.attention_probs_dropout_prob				,						gelu_activation=self.gelu_activation				,						sinusoidal_embeddings=self.sinusoidal_embeddings				,						asm=self.asm				,						causal=self.causal				,						n_langs=self.n_langs				,						max_position_embeddings=self.max_position_embeddings				,						initializer_range=self.initializer_range				,						summary_type=self.summary_type				,						use_proj=self.use_proj				,						bos_token_id=self.bos_token_id				,						)

            return (
                config,
                input_ids,
                token_type_ids,
                input_lengths,
                sequence_labels,
                token_labels,
                is_impossible_labels,
                choice_labels,
                input_mask,
            )





      def    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						)		->					List[Any]:
            lowerCAmelCase  						=	TFXLMModel(config=lowercase			)
            lowerCAmelCase  						=	{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
            lowerCAmelCase  						=	model(lowercase			)

            lowerCAmelCase  						=	[input_ids, input_mask]
            lowerCAmelCase  						=	model(lowercase			)
            self.parent.assertEqual(result.last_hidden_state.shape				,						(self.batch_size, self.seq_length, self.hidden_size)			)





      def    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						)		->					List[str]:
            lowerCAmelCase  						=	TFXLMWithLMHeadModel(lowercase			)

            lowerCAmelCase  						=	{"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
            lowerCAmelCase  						=	model(lowercase			)

            lowerCAmelCase  						=	outputs

            self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.vocab_size)			)





      def    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						)		->					int:
            lowerCAmelCase  						=	TFXLMForQuestionAnsweringSimple(lowercase			)

            lowerCAmelCase  						=	{"""input_ids""": input_ids, """lengths""": input_lengths}

            lowerCAmelCase  						=	model(lowercase			)

            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    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						)		->					int:
            lowerCAmelCase  						=	TFXLMForSequenceClassification(lowercase			)

            lowerCAmelCase  						=	{"""input_ids""": input_ids, """lengths""": input_lengths}

            lowerCAmelCase  						=	model(lowercase			)

            self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.type_sequence_label_size)			)





      def    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						)		->					int:
            lowerCAmelCase  						=	self.num_labels
            lowerCAmelCase  						=	TFXLMForTokenClassification(config=lowercase			)
            lowerCAmelCase  						=	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
            lowerCAmelCase  						=	model(lowercase			)
            self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.seq_length, self.num_labels)			)





      def    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						)		->					Dict:
            lowerCAmelCase  						=	self.num_choices
            lowerCAmelCase  						=	TFXLMForMultipleChoice(config=lowercase			)
            lowerCAmelCase  						=	tf.tile(tf.expand_dims(lowercase				,						1			)				,						(1, self.num_choices, 1)			)
            lowerCAmelCase  						=	tf.tile(tf.expand_dims(lowercase				,						1			)				,						(1, self.num_choices, 1)			)
            lowerCAmelCase  						=	tf.tile(tf.expand_dims(lowercase				,						1			)				,						(1, self.num_choices, 1)			)
            lowerCAmelCase  						=	{
                """input_ids""": multiple_choice_inputs_ids,
                """attention_mask""": multiple_choice_input_mask,
                """token_type_ids""": multiple_choice_token_type_ids,
            }
            lowerCAmelCase  						=	model(lowercase			)
            self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_choices)			)





      def    _snake_case      (	self			)		->					List[str]:
            lowerCAmelCase  						=	self.prepare_config_and_inputs()
            (
                (
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		
            )  						=	config_and_inputs
            lowerCAmelCase  						=	{
                """input_ids""": input_ids,
                """token_type_ids""": token_type_ids,
                """langs""": token_type_ids,
                """lengths""": input_lengths,
            }
            return config, inputs_dict







@require_tf
class 		lowercase ( _UpperCAmelCase					,  _UpperCAmelCase					,  unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				(
          (
              TFXLMModel,
              TFXLMWithLMHeadModel,
              TFXLMForSequenceClassification,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForTokenClassification,
              TFXLMForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE         =				(
          (TFXLMWithLMHeadModel,) if is_tf_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      _SCREAMING_SNAKE_CASE         =				(
          {
              'feature-extraction': TFXLMModel,
              'fill-mask': TFXLMWithLMHeadModel,
              'question-answering': TFXLMForQuestionAnsweringSimple,
              'text-classification': TFXLMForSequenceClassification,
              'text-generation': TFXLMWithLMHeadModel,
              'token-classification': TFXLMForTokenClassification,
              'zero-shot': TFXLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE         =				False
      _SCREAMING_SNAKE_CASE         =				False





      def    _snake_case      (	self				,						lowercase				,						lowercase				,						lowercase				,						lowercase				,						lowercase			)		->					Any:
            if (
                pipeline_test_casse_name == "QAPipelineTests"
                and tokenizer_name is not None
                and not tokenizer_name.endswith("""Fast"""			)
            ):
                  # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                  # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                  # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                  return True

            return False





      def    _snake_case      (	self			)		->					Optional[int]:
            lowerCAmelCase  						=	TFXLMModelTester(self			)
            lowerCAmelCase  						=	ConfigTester(self				,						config_class=lowercase				,						emb_dim=37			)





      def    _snake_case      (	self			)		->					Any:
            self.config_tester.run_common_tests()





      def    _snake_case      (	self			)		->					Optional[int]:
            lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_model(*lowercase			)





      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_lm_head(*lowercase			)





      def    _snake_case      (	self			)		->					Any:
            lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_qa(*lowercase			)





      def    _snake_case      (	self			)		->					Tuple:
            lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_sequence_classif(*lowercase			)





      def    _snake_case      (	self			)		->					Dict:
            lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_for_token_classification(*lowercase			)





      def    _snake_case      (	self			)		->					Tuple:
            lowerCAmelCase  						=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_for_multiple_choice(*lowercase			)





      @slow
      def    _snake_case      (	self			)		->					Optional[Any]:
            for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase  						=	TFXLMModel.from_pretrained(lowercase			)
                  self.assertIsNotNone(lowercase			)







@require_tf
class 		lowercase ( unittest.TestCase ):





      @slow
      def    _snake_case      (	self			)		->					Optional[Any]:
            lowerCAmelCase  						=	TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""			)
            lowerCAmelCase  						=	tf.convert_to_tensor([[14, 447]]				,						dtype=tf.intaa			)  # the president
            lowerCAmelCase  						=	[
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
            ]  # the president the president the president the president the president the president the president the president the president the president
            # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
            lowerCAmelCase  						=	model.generate(lowercase				,						do_sample=lowercase			)
            self.assertListEqual(output_ids[0].numpy().tolist()				,						lowercase			)













"""simple docstring"""





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


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

SCREAMING_SNAKE_CASE__ =       "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
SCREAMING_SNAKE_CASE__ =       "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Any							,						SCREAMING_SNAKE_CASE					:						List[Any]      ):







      '''simple docstring'''




      print(F'Downloading and extracting {task}...'      )
      lowerCAmelCase  						=	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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Optional[Any]							,						SCREAMING_SNAKE_CASE					:						Union[str, Any]      ):







      '''simple docstring'''




      print("""Processing MRPC..."""      )
      lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""MRPC"""      )
      if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
            os.mkdir(SCREAMING_SNAKE_CASE      )
      if path_to_data:
            lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""msr_paraphrase_train.txt"""      )
            lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""msr_paraphrase_test.txt"""      )
      else:
            print("""Local MRPC data not specified, downloading data from %s""" % MRPC_TRAIN      )
            lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""msr_paraphrase_train.txt"""      )
            lowerCAmelCase  						=	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"""      )      )

      lowerCAmelCase  						=	[]
      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:
            lowerCAmelCase  						=	data_fh.readline()
            train_fh.write(SCREAMING_SNAKE_CASE      )
            dev_fh.write(SCREAMING_SNAKE_CASE      )
            for row in data_fh:
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=	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:
            lowerCAmelCase  						=	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      ):
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=	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__							(  SCREAMING_SNAKE_CASE					:						List[str]      ):







      '''simple docstring'''




      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"""      )      )
      lowerCAmelCase  						=	os.path.join(SCREAMING_SNAKE_CASE							,						"""diagnostic"""							,						"""diagnostic.tsv"""      )
      urllib.request.urlretrieve(TASKaPATH["""diagnostic"""]							,						SCREAMING_SNAKE_CASE      )
      print("""\tCompleted!"""      )
      return


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						str      ):







      '''simple docstring'''




      lowerCAmelCase  						=	task_names.split(""","""      )
      if "all" in task_names:
            lowerCAmelCase  						=	TASKS
      else:
            lowerCAmelCase  						=	[]
            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       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      lowerCAmelCase  						=	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=""""""							,						)
      lowerCAmelCase  						=	parser.parse_args(SCREAMING_SNAKE_CASE      )

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






