
'''simple docstring'''

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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	List[str]							)       ->	str:
 print(f"""Downloading and extracting {task}..."""							)
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		f"""{task}.zip"""
 urllib.request.urlretrieve(TASKaPATH[task]   ,     __UpperCamelCase							)
 with zipfile.ZipFile(__UpperCamelCase							) as zip_ref:
  zip_ref.extractall(__UpperCamelCase							)
 os.remove(__UpperCamelCase							)
 print('\tCompleted!'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]   ,     snake_case_				:	Dict							)       ->	Union[str, Any]:
 print('Processing MRPC...'							)
 SCREAMING_SNAKE_CASE  :  Tuple								=		os.path.join(__UpperCamelCase   ,     'MRPC'							)
 if not os.path.isdir(__UpperCamelCase							):
  os.mkdir(__UpperCamelCase							)
 if path_to_data:
  SCREAMING_SNAKE_CASE  :  Any								=		os.path.join(__UpperCamelCase   ,     'msr_paraphrase_train.txt'							)
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		os.path.join(__UpperCamelCase   ,     'msr_paraphrase_test.txt'							)
 else:
  print('Local MRPC data not specified, downloading data from %s' % MRPC_TRAIN							)
  SCREAMING_SNAKE_CASE  :  List[Any]								=		os.path.join(__UpperCamelCase   ,     'msr_paraphrase_train.txt'							)
  SCREAMING_SNAKE_CASE  :  Dict								=		os.path.join(__UpperCamelCase   ,     'msr_paraphrase_test.txt'							)
  urllib.request.urlretrieve(__UpperCamelCase   ,     __UpperCamelCase							)
  urllib.request.urlretrieve(__UpperCamelCase   ,     __UpperCamelCase							)
 if not os.path.isfile(__UpperCamelCase							):
  raise ValueError(f"""Train data not found at {mrpc_train_file}"""							)
 if not os.path.isfile(__UpperCamelCase							):
  raise ValueError(f"""Test data not found at {mrpc_test_file}"""							)
 urllib.request.urlretrieve(TASKaPATH['MRPC']   ,     os.path.join(__UpperCamelCase   ,     'dev_ids.tsv'							)							)

 SCREAMING_SNAKE_CASE  :  List[str]								=		[]
 with open(os.path.join(__UpperCamelCase   ,     'dev_ids.tsv'							)   ,     encoding='utf8'							) as ids_fh:
  for row in ids_fh:
   dev_ids.append(row.strip().split('\t'							)							)

 with open(__UpperCamelCase   ,     encoding='utf8'							) as data_fh, open(
     os.path.join(__UpperCamelCase   ,     'train.tsv'							)   ,     'w'   ,     encoding='utf8'							) as train_fh, open(os.path.join(__UpperCamelCase   ,     'dev.tsv'							)   ,     'w'   ,     encoding='utf8'							) as dev_fh:
  SCREAMING_SNAKE_CASE  :  str								=		data_fh.readline()
  train_fh.write(__UpperCamelCase							)
  dev_fh.write(__UpperCamelCase							)
  for row in data_fh:
   SCREAMING_SNAKE_CASE  :  List[Any]								=		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(__UpperCamelCase   ,     encoding='utf8'							) as data_fh, open(
     os.path.join(__UpperCamelCase   ,     'test.tsv'							)   ,     'w'   ,     encoding='utf8'							) as test_fh:
  SCREAMING_SNAKE_CASE  :  Dict								=		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(__UpperCamelCase							):
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		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_				:	Optional[int]							)       ->	int:
 print('Downloading and extracting diagnostic...'							)
 if not os.path.isdir(os.path.join(__UpperCamelCase   ,     'diagnostic'							)							):
  os.mkdir(os.path.join(__UpperCamelCase   ,     'diagnostic'							)							)
 SCREAMING_SNAKE_CASE  :  str								=		os.path.join(__UpperCamelCase   ,     'diagnostic'   ,     'diagnostic.tsv'							)
 urllib.request.urlretrieve(TASKaPATH['diagnostic']   ,     __UpperCamelCase							)
 print('\tCompleted!'							)
 return



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]							)       ->	Dict:
 SCREAMING_SNAKE_CASE  :  int								=		task_names.split(','							)
 if "all" in task_names:
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		TASKS
 else:
  SCREAMING_SNAKE_CASE  :  List[str]								=		[]
  for task_name in task_names:
   if task_name not in TASKS:
    raise ValueError(f"""Task {task_name} not found!"""							)
   tasks.append(__UpperCamelCase							)
 return tasks



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[int]							)       ->	Tuple:
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		argparse.ArgumentParser()
 parser.add_argument('--data_dir'   ,     help='directory to save data to'   ,     type=__UpperCamelCase   ,     default='glue_data'							)
 parser.add_argument(
     '--tasks'   ,     help='tasks to download data for as a comma separated string'   ,     type=__UpperCamelCase   ,     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=__UpperCamelCase   ,     default=''   ,     )
 SCREAMING_SNAKE_CASE  :  int								=		parser.parse_args(__UpperCamelCase							)

 if not os.path.isdir(args.data_dir							):
  os.mkdir(args.data_dir							)
 SCREAMING_SNAKE_CASE  :  Tuple								=		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(__UpperCamelCase   ,     args.data_dir							)


if __name__ == "__main__":
   sys.exit(main(sys.argv[1:]))






import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
           SpeechaTextaDecoder,
           SpeechaTextaForCausalLM,
       )


@require_torch
class 	a__						:

       def __init__( self   :  Dict   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Dict=9_9   ,				lowerCamelCase_   :  List[Any]=1_3   ,				lowerCamelCase_   :  Dict=1_6   ,				lowerCamelCase_   :  Any=7   ,				lowerCamelCase_   :  Dict=True   ,				lowerCamelCase_   :  List[str]=True   ,				lowerCamelCase_   :  Tuple=True   ,				lowerCamelCase_   :  Any=False   ,				lowerCamelCase_   :  Union[str, Any]=True   ,				lowerCamelCase_   :  int=2   ,				lowerCamelCase_   :  Tuple=3_2   ,				lowerCamelCase_   :  int=4   ,				lowerCamelCase_   :  Dict=4   ,				lowerCamelCase_   :  str=3_0   ,				lowerCamelCase_   :  Optional[int]=0   ,				lowerCamelCase_   :  Optional[Any]=1   ,				lowerCamelCase_   :  Any=2   ,				lowerCamelCase_   :  Optional[int]=None   ,				):
            a_     :  Optional[Any]				      =     parent
            a_     :  Union[str, Any]				      =     batch_size
            a_     :  Tuple				      =     decoder_seq_length
            # For common tests
            a_     :  Union[str, Any]				      =     self.decoder_seq_length
            a_     :  List[str]				      =     is_training
            a_     :  int				      =     use_attention_mask
            a_     :  Dict				      =     use_labels

            a_     :  List[str]				      =     vocab_size
            a_     :  Optional[int]				      =     d_model
            a_     :  str				      =     d_model
            a_     :  Any				      =     decoder_layers
            a_     :  Tuple				      =     decoder_layers
            a_     :  Dict				      =     decoder_ffn_dim
            a_     :  List[str]				      =     decoder_attention_heads
            a_     :  List[Any]				      =     decoder_attention_heads
            a_     :  List[str]				      =     eos_token_id
            a_     :  Any				      =     bos_token_id
            a_     :  List[Any]				      =     pad_token_id
            a_     :  Dict				      =     decoder_start_token_id
            a_     :  Tuple				      =     use_cache
            a_     :  List[Any]				      =     max_position_embeddings

            a_     :  List[str]				      =     None
            a_     :  Any				      =     decoder_seq_length
            a_     :  List[Any]				      =     2
            a_     :  Optional[Any]				      =     1

       def     UpperCAmelCase( self   :  str     ):
            a_     :  Any				      =     ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size     )

            a_     :  Any				      =     None
            if self.use_attention_mask:
                 a_     :  Optional[Any]				      =     ids_tensor([self.batch_size, self.decoder_seq_length]   ,				vocab_size=2     )

            a_     :  Tuple				      =     None
            if self.use_labels:
                 a_     :  int				      =     ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size     )

            a_     :  int				      =     SpeechaTextaConfig(
                vocab_size=self.vocab_size   ,				d_model=self.d_model   ,				decoder_layers=self.decoder_layers   ,				decoder_ffn_dim=self.decoder_ffn_dim   ,				decoder_attention_heads=self.decoder_attention_heads   ,				eos_token_id=self.eos_token_id   ,				bos_token_id=self.bos_token_id   ,				use_cache=self.use_cache   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.decoder_start_token_id   ,				max_position_embeddings=self.max_position_embeddings   ,				)

            return (
                config,
                input_ids,
                attention_mask,
                lm_labels,
            )

       def     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  List[str]   ,				):
            a_     :  List[Any]				      =     True
            a_     :  Optional[int]				      =     SpeechaTextaDecoder(config=lowerCamelCase_     ).to(lowerCamelCase_     ).eval()
            a_     :  Optional[int]				      =     input_ids[:2]

            input_ids[input_ids == 0] += 1
            # first forward pass
            a_     :  Union[str, Any]				      =     model(lowerCamelCase_   ,				use_cache=lowerCamelCase_     )
            a_     :  Tuple				      =     model(lowerCamelCase_     )
            a_     :  str				      =     model(lowerCamelCase_   ,				use_cache=lowerCamelCase_     )

            self.parent.assertTrue(len(lowerCamelCase_     ) == len(lowerCamelCase_     )     )
            self.parent.assertTrue(len(lowerCamelCase_     ) == len(lowerCamelCase_     ) + 1     )

            a_     :  Tuple				      =     outputs["""past_key_values"""]

            # create hypothetical next token and extent to next_input_ids
            a_     :  Any				      =     ids_tensor((2, 1)   ,				config.vocab_size - 1     ) + 1

            # append to next input_ids and
            a_     :  str				      =     torch.cat([input_ids, next_tokens]   ,				dim=-1     )

            print(lowerCamelCase_     )

            a_     :  Any				      =     model(lowerCamelCase_     )["""last_hidden_state"""]
            a_     :  int				      =     model(lowerCamelCase_   ,				past_key_values=lowerCamelCase_     )["""last_hidden_state"""]

            # select random slice
            a_     :  Tuple				      =     ids_tensor((1,)   ,				output_from_past.shape[-1]     ).item()
            a_     :  List[str]				      =     output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
            a_     :  List[Any]				      =     output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            assert torch.allclose(lowerCamelCase_   ,				lowerCamelCase_   ,				atol=1E-3     )

       def     UpperCAmelCase( self   :  Any     ):
            a_     :  List[Any]				      =     self.prepare_config_and_inputs()
            (
                (
                a_
            )      ,      (
                a_
            )      ,      (
                a_
            )      ,      (
                a_
            )      ,      
            )     :  Optional[int]				      =     config_and_inputs

            a_     :  Tuple				      =     {
                """input_ids""": input_ids,
                """attention_mask""": attention_mask,
            }
            return config, inputs_dict


@require_torch
class 	a__						(    lowerCAmelCase_    ,							lowerCAmelCase_    ,							lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  int				     =  (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
       lowerCamelCase__:  int				     =  (SpeechaTextaForCausalLM,) if is_torch_available() else ()
       lowerCamelCase__:  int				     =  {"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
       lowerCamelCase__:  Dict				     =  True
       lowerCamelCase__:  List[Any]				     =  False

       def     UpperCAmelCase( self   :  List[str]   ,				):
            a_     :  Optional[int]				      =     SpeechaTextaStandaloneDecoderModelTester(self   ,				is_training=lowerCamelCase_     )
            a_     :  str				      =     ConfigTester(self   ,				config_class=lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            pass

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            pass

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            pass

       def     UpperCAmelCase( self   :  str     ):
            self.config_tester.run_common_tests()

       def     UpperCAmelCase( self   :  Dict     ):
            a_     :  int				      =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase_     )

       def     UpperCAmelCase( self   :  int     ):
            return



