






"""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						:    List[str]		=						{
    '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						:    Tuple		=						'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'
_UpperCamelCase						:    str		=						'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  print(f'Downloading and extracting {task}...'       )
  lowercase				       =			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     :    Union[str, Any]						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





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

  lowercase				       =			[]
  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:
    lowercase				       =			data_fh.readline()
    train_fh.write(__snake_case       )
    dev_fh.write(__snake_case       )
    for row in data_fh:
      lowercase       ,	lowercase       ,	lowercase       ,	lowercase       ,	lowercase				       =			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:
    lowercase				       =			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       ):
      lowercase       ,	lowercase       ,	lowercase       ,	lowercase       ,	lowercase				       =			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[Any]       ):





  '''simple docstring'''





  print('Downloading and extracting diagnostic...'       )
  if not os.path.isdir(os.path.join(__snake_case						,   'diagnostic'       )       ):
    os.mkdir(os.path.join(__snake_case						,   'diagnostic'       )       )
  lowercase				       =			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]       ):





  '''simple docstring'''





  lowercase				       =			task_names.split(','       )
  if "all" in task_names:
    lowercase				       =			TASKS
  else:
    lowercase				       =			[]
    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     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			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=''						,   )
  lowercase				       =			parser.parse_args(__snake_case       )

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





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_a									=  logging.get_logger(__name__)

_a									=  {
    '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json''',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}






class 				A_					(						snake_case__   ):
     _lowercase     :	List[Any]        =      'm2m_100'
     _lowercase     :	str        =      ['past_key_values']
     _lowercase     :	List[Any]        =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


     def __init__(				self     :						int     ,    UpperCAmelCase     :						Any=1_2_8_1_1_2     ,    UpperCAmelCase     :						Optional[int]=1_0_2_4     ,    UpperCAmelCase     :						Tuple=1_2     ,    UpperCAmelCase     :						List[str]=4_0_9_6     ,    UpperCAmelCase     :						Dict=1_6     ,    UpperCAmelCase     :						int=1_2     ,    UpperCAmelCase     :						int=4_0_9_6     ,    UpperCAmelCase     :						List[str]=1_6     ,    UpperCAmelCase     :						Optional[Any]=0.05     ,    UpperCAmelCase     :						Dict=0.05     ,    UpperCAmelCase     :						Union[str, Any]=True     ,    UpperCAmelCase     :						Dict=True     ,    UpperCAmelCase     :						List[str]="relu"     ,    UpperCAmelCase     :						Dict=1_0_2_4     ,    UpperCAmelCase     :						str=0.1     ,    UpperCAmelCase     :						Dict=0.1     ,    UpperCAmelCase     :						List[str]=0.0     ,    UpperCAmelCase     :						Union[str, Any]=0.02     ,    UpperCAmelCase     :						Optional[Any]=2     ,    UpperCAmelCase     :						str=True     ,    UpperCAmelCase     :						Optional[int]=1     ,    UpperCAmelCase     :						List[Any]=0     ,    UpperCAmelCase     :						Union[str, Any]=2     ,    **UpperCAmelCase     :						Union[str, Any]     ,    )				->      Tuple:
      __lowerCAmelCase:  Optional[Any]        = vocab_size
      __lowerCAmelCase:  Dict        = max_position_embeddings
      __lowerCAmelCase:  Tuple        = d_model
      __lowerCAmelCase:  List[str]        = encoder_ffn_dim
      __lowerCAmelCase:  Any        = encoder_layers
      __lowerCAmelCase:  int        = encoder_attention_heads
      __lowerCAmelCase:  str        = decoder_ffn_dim
      __lowerCAmelCase:  Tuple        = decoder_layers
      __lowerCAmelCase:  Tuple        = decoder_attention_heads
      __lowerCAmelCase:  Any        = dropout
      __lowerCAmelCase:  Optional[int]        = attention_dropout
      __lowerCAmelCase:  Tuple        = activation_dropout
      __lowerCAmelCase:  Optional[Any]        = activation_function
      __lowerCAmelCase:  Union[str, Any]        = init_std
      __lowerCAmelCase:  List[str]        = encoder_layerdrop
      __lowerCAmelCase:  Optional[Any]        = decoder_layerdrop
      __lowerCAmelCase:  Dict        = use_cache
      __lowerCAmelCase:  Any        = encoder_layers
      __lowerCAmelCase:  Tuple        = scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=UpperCAmelCase     ,    bos_token_id=UpperCAmelCase     ,    eos_token_id=UpperCAmelCase     ,    is_encoder_decoder=UpperCAmelCase     ,    decoder_start_token_id=UpperCAmelCase     ,    **UpperCAmelCase     ,    )







class 				A_					(						snake_case__   ):


     @property
     def     UpperCAmelCase    (				self     :						List[Any]   )				->      Mapping[str, Mapping[int, str]]:
      __lowerCAmelCase:  Union[str, Any]        = OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
              ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
          ]   )

      if self.use_past:
       __lowerCAmelCase:  str        = {0: 'batch'}
       __lowerCAmelCase:  Union[str, Any]        = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
      else:
       __lowerCAmelCase:  Any        = {0: 'batch', 1: 'decoder_sequence'}
       __lowerCAmelCase:  Dict        = {0: 'batch', 1: 'decoder_sequence'}

      if self.use_past:
       self.fill_with_past_key_values_(UpperCAmelCase     ,    direction='inputs'   )
      return common_inputs


     def     UpperCAmelCase    (				self     :						Tuple     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Mapping[str, Any]:
      # Copied from OnnxConfig.generate_dummy_inputs
      # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      __lowerCAmelCase:  List[str]        = compute_effective_axis_dimension(
          UpperCAmelCase     ,    fixed_dimension=OnnxConfig.default_fixed_batch     ,    num_token_to_add=0   )

      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      __lowerCAmelCase:  Tuple        = tokenizer.num_special_tokens_to_add(UpperCAmelCase   )
      __lowerCAmelCase:  List[str]        = compute_effective_axis_dimension(
          UpperCAmelCase     ,    fixed_dimension=OnnxConfig.default_fixed_sequence     ,    num_token_to_add=UpperCAmelCase   )

      # Generate dummy inputs according to compute batch and sequence
      __lowerCAmelCase:  int        = [' '.join([tokenizer.unk_token]   ) * seq_length] * batch_size
      __lowerCAmelCase:  Optional[int]        = dict(tokenizer(UpperCAmelCase     ,    return_tensors=UpperCAmelCase   )   )
      return common_inputs


     def     UpperCAmelCase    (				self     :						str     ,    UpperCAmelCase     :						PreTrainedTokenizer     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						int = -1     ,    UpperCAmelCase     :						bool = False     ,    UpperCAmelCase     :						Optional[TensorType] = None     ,    )				->      Mapping[str, Any]:
      __lowerCAmelCase:  Optional[int]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )

      # Generate decoder inputs
      __lowerCAmelCase:  Any        = seq_length if not self.use_past else 1
      __lowerCAmelCase:  Union[str, Any]        = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )
      __lowerCAmelCase:  List[Any]        = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      __lowerCAmelCase:  List[str]        = dict(**UpperCAmelCase     ,    **UpperCAmelCase   )

      if self.use_past:
       if not is_torch_available():
        raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
       else:
        import torch
       __lowerCAmelCase      ,				__lowerCAmelCase:  List[Any]        = common_inputs['input_ids'].shape
       __lowerCAmelCase:  int        = common_inputs['decoder_input_ids'].shape[1]
       __lowerCAmelCase      ,				__lowerCAmelCase:  str        = self.num_attention_heads
       __lowerCAmelCase:  str        = (
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       __lowerCAmelCase:  Optional[int]        = decoder_seq_length + 3
       __lowerCAmelCase:  List[str]        = (
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       __lowerCAmelCase:  Dict        = torch.cat(
           [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase     ,    UpperCAmelCase   )]     ,    dim=1   )

       __lowerCAmelCase:  str        = []
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       __lowerCAmelCase      ,				__lowerCAmelCase:  str        = self.num_layers
       __lowerCAmelCase:  Tuple        = min(UpperCAmelCase     ,    UpperCAmelCase   )
       __lowerCAmelCase:  List[Any]        = max(UpperCAmelCase     ,    UpperCAmelCase   ) - min_num_layers
       __lowerCAmelCase:  Any        = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

       for _ in range(UpperCAmelCase   ):
        common_inputs["past_key_values"].append(
            (
                torch.zeros(UpperCAmelCase   ),
                torch.zeros(UpperCAmelCase   ),
                torch.zeros(UpperCAmelCase   ),
                torch.zeros(UpperCAmelCase   ),
            )   )
       # TODO: test this.
       __lowerCAmelCase:  List[Any]        = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
       for _ in range(UpperCAmelCase     ,    UpperCAmelCase   ):
        common_inputs["past_key_values"].append((torch.zeros(UpperCAmelCase   ), torch.zeros(UpperCAmelCase   ))   )
      return common_inputs


     _lowercase     :	int        =      _generate_dummy_inputs_for_default_and_seqaseq_lm





